Data containers based on geometric selection
yt.data_objects.selection_data_containers.
YTCutRegion
(data_source, conditionals, ds=None, field_parameters=None, base_object=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
This is a data object designed to allow individuals to apply logical operations to fields and filter as a result of those cuts.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> sp = ds.sphere("max", (1.0, 'Mpc'))
>>> cr = ds.cut_region(sp, ["obj['temperature'] < 1e3"])
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTCuttingPlane
(normal, center, north_vector=None, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer2D
This is a data object corresponding to an oblique slice through the simulation domain.
This object is typically accessed through the cutting object that hangs off of index objects. A cutting plane is an oblique plane through the data, defined by a normal vector and a coordinate. It attempts to guess an ‘north’ vector, which can be overridden, and then it pixelizes the appropriate data onto the plane without interpolation.
Parameters: 


Notes
This data object in particular can be somewhat expensive to create. It’s also important to note that unlike the other 2D data objects, this object provides px, py, pz, as some cells may have a height from the plane.
Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> cp = ds.cutting([0.1, 0.2, 0.9], [0.5, 0.42, 0.6])
>>> print cp["Density"]
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
normal
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_frb
(width, resolution, height=None, periodic=False)[source]¶This function returns a FixedResolutionBuffer generated from this object.
An ObliqueFixedResolutionBuffer is an object that accepts a variableresolution 2D object and transforms it into an NxM bitmap that can be plotted, examined or processed. This is a convenience function to return an FRB directly from an existing 2D data object. Unlike the corresponding to_frb function for other YTSelectionContainer2D objects, this does not accept a ‘center’ parameter as it is assumed to be centered at the center of the cutting plane.
Parameters: 


Returns:  frb – A fixed resolution buffer, which can be queried for fields. 
Return type: 
Examples
>>> v, c = ds.find_max("density")
>>> sp = ds.sphere(c, (100.0, 'au'))
>>> L = sp.quantities.angular_momentum_vector()
>>> cutting = ds.cutting(L, c)
>>> frb = cutting.to_frb( (1.0, 'pc'), 1024)
>>> write_image(np.log10(frb["Density"]), 'density_1pc.png')
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
to_pw
(fields=None, center='c', width=None, axes_unit=None)[source]¶Create a PWViewerMPL
from this
object.
This is a barebones mechanism of creating a plot window from this object, which can then be moved around, zoomed, and on and on. All behavior of the plot window is relegated to that routine.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTDataCollection
(obj_list, ds=None, field_parameters=None, data_source=None, center=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
By selecting an arbitrary object_list, we can act on those grids. Child cells are not returned.
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTDataObjectUnion
(data_objects, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
This is a more efficient method of selecting the union of multiple data selection objects.
Creating one of these objects returns the union of all of the subobjects; it is designed to be a faster method than chaining  (or) operations to create a single, large union.
Parameters:  data_objects (Iterable of YTSelectionContainer3D) – The data objects to union 

Examples
>>> import yt
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp1 = ds.sphere((0.4, 0.5, 0.6), 0.1)
>>> sp2 = ds.sphere((0.3, 0.5, 0.15), 0.1)
>>> sp3 = ds.sphere((0.5, 0.5, 0.9), 0.1)
>>> new_obj = ds.union((sp1, sp2, sp3))
>>> print(new_obj.sum("cell_volume"))
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTDisk
(center, normal, radius, height, fields=None, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
By providing a center, a normal, a radius and a height we can define a cylinder of any proportion. Only cells whose centers are within the cylinder will be selected.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> c = [0.5,0.5,0.5]
>>> disk = ds.disk(c, [1,0,0], (1, 'kpc'), (10, 'kpc'))
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTEllipsoid
(center, A, B, C, e0, tilt, fields=None, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
By providing a center,*A*,*B*,*C*,*e0*,*tilt* we can define a ellipsoid of any proportion. Only cells whose centers are within the ellipsoid will be selected.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> c = [0.5,0.5,0.5]
>>> ell = ds.ellipsoid(c, 0.1, 0.1, 0.1, np.array([0.1, 0.1, 0.1]), 0.2)
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
t2
= None¶calculate the original e1 given the tilt about the x axis when e0 was aligned to x after t1, t2 rotations about z, y
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTIntersectionContainer3D
(data_objects, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
This is a more efficient method of selecting the intersection of multiple data selection objects.
Creating one of these objects returns the intersection of all of the subobjects; it is designed to be a faster method than chaining & (“and”) operations to create a single, large intersection.
Parameters:  data_objects (Iterable of YTSelectionContainer3D) – The data objects to intersect 

Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> sp1 = ds.sphere((0.4, 0.5, 0.6), 0.15)
>>> sp2 = ds.sphere((0.38, 0.51, 0.55), 0.1)
>>> sp3 = ds.sphere((0.35, 0.5, 0.6), 0.15)
>>> new_obj = ds.intersection((sp1, sp2, sp3))
>>> print(new_obj.sum("cell_volume"))
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTOrthoRay
(axis, coords, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer1D
This is an orthogonal ray cast through the entire domain, at a specific coordinate.
This object is typically accessed through the ortho_ray object that hangs off of index objects. The resulting arrays have their dimensionality reduced to one, and an ordered list of points at an (x,y) tuple along axis are available.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> oray = ds.ortho_ray(0, (0.2, 0.74))
>>> print oray["Density"]
Note: The lowlevel data representation for rays are not guaranteed to be spatially ordered. In particular, with AMR datasets, higher resolution data is tagged on to the end of the ray. If you want this data represented in a spatially ordered manner, manually sort it by the “t” field, which is the value of the parametric variable that goes from 0 at the start of the ray to 1 at the end:
>>> my_ray = ds.ortho_ray(...)
>>> ray_sort = np.argsort(my_ray["t"])
>>> density = my_ray["density"][ray_sort]
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
coords
¶fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTPoint
(p, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer0D
A 0dimensional object defined by a single point
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> c = [0.5,0.5,0.5]
>>> point = ds.point(c)
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTRay
(start_point, end_point, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer1D
This is an arbitrarilyaligned ray cast through the entire domain, at a specific coordinate.
This object is typically accessed through the ray object that hangs off of index objects. The resulting arrays have their dimensionality reduced to one, and an ordered list of points at an (x,y) tuple along axis are available, as is the t field, which corresponds to a unitless measurement along the ray from start to end.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> ray = ds.ray((0.2, 0.74, 0.11), (0.4, 0.91, 0.31))
>>> print ray["Density"], ray["t"], ray["dts"]
Note: The lowlevel data representation for rays are not guaranteed to be spatially ordered. In particular, with AMR datasets, higher resolution data is tagged on to the end of the ray. If you want this data represented in a spatially ordered manner, manually sort it by the “t” field, which is the value of the parametric variable that goes from 0 at the start of the ray to 1 at the end:
>>> my_ray = ds.ray(...)
>>> ray_sort = np.argsort(my_ray["t"])
>>> density = my_ray["density"][ray_sort]
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTRegion
(center, left_edge, right_edge, fields=None, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
A 3D region of data with an arbitrary center.
Takes an array of three left_edge coordinates, three right_edge coordinates, and a center that can be anywhere in the domain. If the selected region extends past the edges of the domain, no data will be found there, though the object’s left_edge or right_edge are not modified.
Parameters: 


apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTSlice
(axis, coord, center=None, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer2D
This is a data object corresponding to a slice through the simulation domain.
This object is typically accessed through the slice object that hangs off of index objects. Slice is an orthogonal slice through the data, taking all the points at the finest resolution available and then indexing them. It is more appropriately thought of as a slice ‘operator’ than an object, however, as its field and coordinate can both change.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> slice = ds.slice(0, 0.25)
>>> print slice["Density"]
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_frb
(width, resolution, center=None, height=None, periodic=False)¶This function returns a FixedResolutionBuffer generated from this object.
A FixedResolutionBuffer is an object that accepts a variableresolution 2D object and transforms it into an NxM bitmap that can be plotted, examined or processed. This is a convenience function to return an FRB directly from an existing 2D data object.
Parameters: 


Returns:  frb – A fixed resolution buffer, which can be queried for fields. 
Return type: 
Examples
>>> proj = ds.proj("Density", 0)
>>> frb = proj.to_frb( (100.0, 'kpc'), 1024)
>>> write_image(np.log10(frb["Density"]), 'density_100kpc.png')
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
to_pw
(fields=None, center='c', width=None, origin='centerwindow')[source]¶Create a PWViewerMPL
from this
object.
This is a barebones mechanism of creating a plot window from this object, which can then be moved around, zoomed, and on and on. All behavior of the plot window is relegated to that routine.
write_out
(filename, fields=None, format='%0.16e')¶yt.data_objects.selection_data_containers.
YTSphere
(center, radius, ds=None, field_parameters=None, data_source=None)[source]¶Bases: yt.data_objects.data_containers.YTSelectionContainer3D
A sphere of points defined by a center and a radius.
Parameters: 


Examples
>>> import yt
>>> ds = yt.load("RedshiftOutput0005")
>>> c = [0.5,0.5,0.5]
>>> sphere = ds.sphere(c, (1., "kpc"))
apply_units
(arr, units)¶argmax
(field, axis=None)¶Return the values at which the field is maximized.
This will, in a parallelaware fashion, find the maximum value and then return to you the values at that maximum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_max_rho = reg.argmax("density", axis="temperature")
>>> max_rho_xyz = reg.argmax("density")
>>> t_mrho, v_mrho = reg.argmax("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmax("density")
argmin
(field, axis=None)¶Return the values at which the field is minimized.
This will, in a parallelaware fashion, find the minimum value and then return to you the values at that minimum location that are requested for “axis”. By default it will return the spatial positions (in the natural coordinate system), but it can be any field
Parameters: 


Returns:  
Return type:  A list of YTQuantities as specified by the axis argument. 
Examples
>>> temp_at_min_rho = reg.argmin("density", axis="temperature")
>>> min_rho_xyz = reg.argmin("density")
>>> t_mrho, v_mrho = reg.argmin("density", axis=["temperature",
... "velocity_magnitude"])
>>> x, y, z = reg.argmin("density")
blocks
¶calculate_isocontour_flux
(field, value, field_x, field_y, field_z, fluxing_field=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and calculates the flux over those contours.
This function will conduct marching cubes on all the cells in a given data container (gridbygrid), and then for each identified triangular segment of an isocontour in a given cell, calculate the gradient (i.e., normal) in the isocontoured field, interpolate the local value of the “fluxing” field, the area of the triangle, and then return:
area * local_flux_value * (n dot v)
Where area, local_value, and the vector v are interpolated at the barycenter (weighted by the vertex values) of the triangle. Note that this specifically allows for the field fluxing across the surface to be different from the field being contoured. If the fluxing_field is not specified, it is assumed to be 1.0 everywhere, and the raw flux with no localweighting is returned.
Additionally, the returned flux is defined as flux into the surface, not flux out of the surface.
Parameters: 


Returns:  flux – The summed flux. Note that it is not currently scaled; this is simply the codeunit area times the fields. 
Return type: 
Examples
This will create a data object, find a nice value in the center, and calculate the metal flux over it.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> flux = dd.calculate_isocontour_flux("Density", rho,
... "velocity_x", "velocity_y", "velocity_z", "Metal_Density")
chunks
(fields, chunking_style, **kwargs)¶clear_data
()¶Clears out all data from the YTDataContainer instance, freeing memory.
clone
()¶Clone a data object.
This will make a duplicate of a data object; note that the field_parameters may not necessarily be deeplycopied. If you modify the field parameters inplace, it may or may not be shared between the objects, depending on the type of object that that particular field parameter is.
Notes
One use case for this is to have multiple identical data objects that are being chunked over in different orders.
Examples
>>> ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
>>> sp = ds.sphere("c", 0.1)
>>> sp_clone = sp.clone()
>>> sp["density"]
>>> print sp.field_data.keys()
[("gas", "density")]
>>> print sp_clone.field_data.keys()
[]
comm
= None¶convert
(datatype)¶This will attempt to convert a given unit to cgs from code units. It either returns the multiplicative factor or throws a KeyError.
cut_region
(field_cuts, field_parameters=None)¶Return a YTCutRegion, where the a cell is identified as being inside the cut region based on the value of one or more fields. Note that in previous versions of yt the name ‘grid’ was used to represent the data object used to construct the field cut, as of yt 3.0, this has been changed to ‘obj’.
Parameters: 


Examples
To find the total mass of hot gas with temperature greater than 10^6 K in your volume:
>>> ds = yt.load("RedshiftOutput0005")
>>> ad = ds.all_data()
>>> cr = ad.cut_region(["obj['temperature'] > 1e6"])
>>> print cr.quantities.total_quantity("cell_mass").in_units('Msun')
extract_connected_sets
(field, num_levels, min_val, max_val, log_space=True, cumulative=True)¶This function will create a set of contour objects, defined by having connected cell structures, which can then be studied and used to ‘paint’ their source grids, thus enabling them to be plotted.
Note that this function can return a connected set object that has no member values.
extract_isocontours
(field, value, filename=None, rescale=False, sample_values=None)¶This identifies isocontours on a cellbycell basis, with no consideration of global connectedness, and returns the vertices of the Triangles in that isocontour.
This function simply returns the vertices of all the triangles calculated by the marching cubes algorithm; for more complex operations, such as identifying connected sets of cells above a given threshold, see the extract_connected_sets function. This is more useful for calculating, for instance, total isocontour area, or visualizing in an external program (such as MeshLab.)
Parameters: 


Returns: 

Examples
This will create a data object, find a nice value in the center, and output the vertices to “triangles.obj” after rescaling them.
>>> dd = ds.all_data()
>>> rho = dd.quantities["WeightedAverageQuantity"](
... "Density", weight="CellMassMsun")
>>> verts = dd.extract_isocontours("Density", rho,
... "triangles.obj", True)
fcoords
¶fcoords_vertex
¶fwidth
¶get_data
(fields=None)¶get_dependencies
(fields)¶get_field_parameter
(name, default=None)¶This is typically only used by derived field functions, but it returns parameters used to generate fields.
has_field_parameter
(name)¶Checks if a field parameter is set.
has_key
(key)¶Checks if a data field already exists.
icoords
¶index
¶integrate
(field, weight=None, axis=None)¶Compute the integral (projection) of a field along an axis.
This projects a field along an axis.
Parameters:  

Returns:  
Return type:  YTProjection 
Examples
>>> column_density = reg.integrate("density", axis="z")
ires
¶keys
()¶max
(field, axis=None)¶Compute the maximum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the maximum of the given field. Supplying an axis will result in a return value of a YTProjection, with method ‘mip’ for maximum intensity. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> max_temp = reg.max("temperature")
>>> max_temp_proj = reg.max("temperature", axis="x")
mean
(field, axis=None, weight=None)¶Compute the mean of a field, optionally along an axis, with a weight.
This will, in a parallelaware fashion, compute the mean of the given field. If an axis is supplied, it will return a projection, where the weight is also supplied. By default the weight field will be “ones” or “particle_ones”, depending on the field being averaged, resulting in an unweighted average.
Parameters:  

Returns:  
Return type:  Scalar or YTProjection. 
Examples
>>> avg_rho = reg.mean("density", weight="cell_volume")
>>> rho_weighted_T = reg.mean("temperature", axis="y", weight="density")
min
(field, axis=None)¶Compute the minimum of a field.
This will, in a parallelaware fashion, compute the minimum of the given field. Supplying an axis is not currently supported. If the max has already been requested, it will use the cached extrema value.
Parameters:  

Returns:  
Return type:  Scalar. 
Examples
>>> min_temp = reg.min("temperature")
paint_grids
(field, value, default_value=None)¶This function paints every cell in our dataset with a given value. If default_value is given, the other values for the given in every grid are discarded and replaced with default_value. Otherwise, the field is mandated to ‘know how to exist’ in the grid.
Note that this only paints the cells in the dataset, so cells in grids with child cells are left untouched.
particles
¶partition_index_2d
(axis)¶partition_index_3d
(ds, padding=0.0, rank_ratio=1)¶partition_index_3d_bisection_list
()¶Returns an array that is used to drive _partition_index_3d_bisection, below.
partition_region_3d
(left_edge, right_edge, padding=0.0, rank_ratio=1)¶Given a region, it subdivides it into smaller regions for parallel analysis.
pf
¶profile
(bin_fields, fields, n_bins=64, extrema=None, logs=None, units=None, weight_field='cell_mass', accumulation=False, fractional=False, deposition='ngp')¶Create a 1, 2, or 3D profile object from this data_source.
The dimensionality of the profile object is chosen by the number of
fields given in the bin_fields argument. This simply calls
yt.data_objects.profiles.create_profile()
.
Parameters: 


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 = ad.profile(ad, [("gas", "density")],
... [("gas", "temperature"),
... ("gas", "velocity_x")])
>>> print (profile.x)
>>> print (profile["gas", "temperature"])
>>> plot = profile.plot()
ptp
(field)¶Compute the range of values (maximum  minimum) of a field.
This will, in a parallelaware fashion, compute the “peaktopeak” of the given field.
Parameters:  field (string or tuple field name) – The field to average. 

Returns:  
Return type:  Scalar 
Examples
>>> rho_range = reg.ptp("density")
save_as_dataset
(filename=None, fields=None)¶Export a data object to a reloadable yt dataset.
This function will take a data object and output a dataset
containing either the fields presently existing or fields
given in the fields
list. The resulting dataset can be
reloaded as a yt dataset.
Parameters: 


Returns:  filename – The name of the file that has been created. 
Return type: 
Examples
>>> import yt
>>> ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046")
>>> sp = ds.sphere(ds.domain_center, (10, "Mpc"))
>>> fn = sp.save_as_dataset(fields=["density", "temperature"])
>>> sphere_ds = yt.load(fn)
>>> # the original data container is available as the data attribute
>>> print (sds.data["density"])
[ 4.46237613e32 4.86830178e32 4.46335118e32 ..., 6.43956165e30
3.57339907e30 2.83150720e30] g/cm**3
>>> ad = sphere_ds.all_data()
>>> print (ad["temperature"])
[ 1.00000000e+00 1.00000000e+00 1.00000000e+00 ..., 4.40108359e+04
4.54380547e+04 4.72560117e+04] K
save_object
(name, filename=None)¶Save an object. If filename is supplied, it will be stored in
a shelve
file of that name. Otherwise, it will be stored via
yt.data_objects.api.GridIndex.save_object()
.
selector
¶set_field_parameter
(name, val)¶Here we set up dictionaries that get passed up and down and ultimately to derived fields.
std
(field, weight=None)¶Compute the variance of a field.
This will, in a parallelware fashion, compute the variance of the given field.
Parameters:  

Returns:  
Return type:  Scalar 
sum
(field, axis=None)¶Compute the sum of a field, optionally along an axis.
This will, in a parallelaware fashion, compute the sum of the given field. If an axis is specified, it will return a projection (using method type “sum”, which does not take into account path length) along that axis.
Parameters:  

Returns:  
Return type:  Either a scalar or a YTProjection. 
Examples
>>> total_vol = reg.sum("cell_volume")
>>> cell_count = reg.sum("ones", axis="x")
tiles
¶to_dataframe
(fields=None)¶Export a data object to a pandas DataFrame.
This function will take a data object and construct from it and optionally a list of fields a pandas DataFrame object. If pandas is not importable, this will raise ImportError.
Parameters:  fields (list of strings or tuple field names, default None) – If this is supplied, it is the list of fields to be exported into the data frame. If not supplied, whatever fields presently exist will be used. 

Returns:  df – The data contained in the object. 
Return type:  DataFrame 
Examples
>>> dd = ds.all_data()
>>> df1 = dd.to_dataframe(["density", "temperature"])
>>> dd["velocity_magnitude"]
>>> df2 = dd.to_dataframe()
to_glue
(fields, label='yt', data_collection=None)¶Takes specific fields in the container and exports them to Glue (http://www.glueviz.org) for interactive analysis. Optionally add a label. If you are already within the Glue environment, you can pass a data_collection object, otherwise Glue will be started.
volume
()¶Return the volume of the data container. This is found by adding up the volume of the cells with centers in the container, rather than using the geometric shape of the container, so this may vary very slightly from what might be expected from the geometric volume.
write_out
(filename, fields=None, format='%0.16e')¶