Merge branch 'main' of github.com:yt-project/yt into mask_phase_plots

.gitignore

@@ -40,7 +40,7 @@ yt/utilities/lib/bounding_volume_hierarchy.c
 yt/utilities/lib/contour_finding.c
 yt/utilities/lib/cykdtree/kdtree.cpp
 yt/utilities/lib/cykdtree/utils.cpp
-yt/utilities/lib/cyoctree.cpp
+yt/utilities/lib/cyoctree.c
 yt/utilities/lib/depth_first_octree.c
 yt/utilities/lib/distance_queue.c
 yt/utilities/lib/element_mappings.c

.pre-commit-config.yaml

@@ -12,22 +12,28 @@ yt/extern\
 |yt/visualization/_colormap_data.py\
 )"
 
-repos:
+ci:
+    autofix_prs: false
 
+repos:
+-   repo: https://github.com/asottile/setup-cfg-fmt
+    rev: v1.17.0
+    hooks:
+    -   id: setup-cfg-fmt
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.4.0
+    rev: v4.0.1
     hooks:
     - id: trailing-whitespace
     - id: end-of-file-fixer
     - id: no-commit-to-branch
       args: [--branch, main]
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.11.0
+    rev: v2.16.0
     hooks:
     - id: pyupgrade
       args: [--py36-plus]
 -   repo: https://github.com/psf/black
-    rev: 20.8b1
+    rev: 21.5b1
     hooks:
     - id: black
       language_version: python3
@@ -37,7 +43,7 @@ repos:
     - id: isort
       additional_dependencies: [toml]
 -   repo: https://github.com/PyCQA/flake8
-    rev: '3.9.0'
+    rev: '3.9.2'
     hooks:
     - id: flake8
       additional_dependencies: [
@@ -49,3 +55,7 @@ repos:
     hooks:
     -   id: blacken-docs
         additional_dependencies: [black==20.8b1]
+-   repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.8.0
+    hooks:
+    -   id: rst-backticks

CONTRIBUTING.rst

@@ -244,9 +244,9 @@ If you're interested in participating in yt development, take a look at the
 <https://github.com/yt-project/yt/issues>`_.
 You can search by labels, indicating estimated level of difficulty or category,
 to find issues that you would like to contribute to.  Good first issues are
-marked with a label of `new contributor friendly`.  While we try to triage the
+marked with a label of *new contributor friendly*.  While we try to triage the
 issue tracker regularly to assign appropriate labels to every issue, it may be
-the case that issues not marked as `new contributor friendly` are actually
+the case that issues not marked as *new contributor friendly* are actually
 suitable for new contributors.
 
 Here are some predefined issue searches that might be useful:
@@ -726,8 +726,9 @@ It is recommended (though not required) that you install ``pre-commit`` on your
 
 So that our hooks will run and update your changes on every commit.
 If you do not want to/are unable to configure ``pre-commit`` on your machine, note that
-after opening a pull request, a bot will run the hooks and validate your contribution by
-appending commits to your branch.
+after opening a pull request, it will still be run as a static checker as part of our CI.
+Some hooks also come with auto-fixing capabilities, which you can trigger manually in a
+PR by commenting ``pre-commit.ci run`` (see ` <https://pre-commit.ci/#features>`_).
 
 Here's a list of the main automated formatters we use along with a short description
 
@@ -738,9 +739,21 @@ Here's a list of the main automated formatters we use along with a short descrip
 
 The complete configuration is located in ``.pre-commit-config.yaml``.
 
-.. note:: It is not recommended to run formatters directly on the command line because
-    versions available in your system may conflict with the ones we run through
-    ``pre-commit`` hooks (which are updated periodically).
+Note that formatters should not be run directly on the command line as, for instance
+
+.. code-block:: bash
+
+    $ black yt
+
+But it can still be done as
+
+.. code-block:: bash
+
+    $ pre-commit run black --all-files
+
+The reason is that you may have a specific version of ``black`` installed which can
+produce different results, while the one that's installed with pre-commit is guaranteed
+to be in sync with the rest of contributors.
 
 Below are a list of additional guidelines for coding in yt, that are not automatically
 enforced.

conftest.py

@@ -69,7 +69,7 @@ def pytest_configure(config):
     # Register custom marks for answer tests and big data
     config.addinivalue_line("markers", "answer_test: Run the answer tests.")
     config.addinivalue_line(
-        "markers", "big_data: Run answer tests that require" " large data files."
+        "markers", "big_data: Run answer tests that require large data files."
     )
 
 

doc/helper_scripts/show_fields.py

@@ -187,7 +187,7 @@ def print_all_fields(fl):
 
 
 class FieldInfo:
-    """ a simple container to hold the information about fields """
+    """a simple container to hold the information about fields"""
 
     def __init__(self, ftype, field, ptype):
         name = field[0]

doc/source/analyzing/domain_analysis/clump_finding.rst

@@ -58,7 +58,7 @@ and either return True or False.
 .. code:: python
 
    def _minimum_gas_mass(clump, min_mass):
-       return clump["gas", "cell_mass"].sum() >= min_mass
+       return clump["gas", "mass"].sum() >= min_mass
 
 
    add_validator("minimum_gas_mass", _minimum_gas_mass)
@@ -94,7 +94,7 @@ Calculating Clump Quantities
 
 By default, a number of quantities will be calculated for each clump when the
 clump finding process has finished.  The default quantities are: ``total_cells``,
-``cell_mass``, ``mass_weighted_jeans_mass``, ``volume_weighted_jeans_mass``,
+``mass``, ``mass_weighted_jeans_mass``, ``volume_weighted_jeans_mass``,
 ``max_grid_level``, ``min_number_density``, and ``max_number_density``.
 Additional items can be added with the
 :func:`~yt.data_objects.level_sets.clump_handling.Clump.add_info_item`
@@ -115,7 +115,7 @@ of available info items by calling
 
    def _mass_weighted_jeans_mass(clump):
        jeans_mass = clump.data.quantities.weighted_average_quantity(
-           "jeans_mass", ("gas", "cell_mass")
+           "jeans_mass", ("gas", "mass")
        ).in_units("Msun")
        return "Jeans Mass (mass-weighted): %.6e Msolar." % jeans_mass
 
@@ -215,13 +215,13 @@ The ``leaves`` attribute returns a list of all leaf clumps.
 
    print(ds_clumps.leaves)
 
-Info items for each clump can be accessed with the `clump` field type.  Gas
-or grid fields should be accessed using the `grid` field type and particle
+Info items for each clump can be accessed with the ``"clump"`` field type.  Gas
+or grid fields should be accessed using the ``"grid"`` field type and particle
 fields should be access using the specific particle type.
 
 .. code:: python
 
    my_clump = ds_clumps.leaves[0]
-   print(my_clumps["clump", "cell_mass"])
+   print(my_clumps["clump", "mass"])
    print(my_clumps["grid", "density"])
    print(my_clumps["all", "particle_mass"])

doc/source/analyzing/domain_analysis/cosmology_calculator.rst

@@ -4,9 +4,9 @@ Cosmology Calculator
 ====================
 
 The cosmology calculator can be used to calculate cosmological distances and
-times given a set of cosmological parameters.  A cosmological dataset, `ds`,
+times given a set of cosmological parameters.  A cosmological dataset, ``ds``,
 will automatically have a cosmology calculator configured with the correct
-parameters associated with it as `ds.cosmology`.  A standalone
+parameters associated with it as ``ds.cosmology``.  A standalone
 :class:`~yt.utilities.cosmology.Cosmology` calculator object can be created
 in the following way:
 
@@ -77,8 +77,8 @@ redshift and time are available:
    x, x.to("Mpc") and x.to("Mpccm") will be the same.  The user should take
    care to understand which reference frame is correct for the given calculation.
 
-The helper functions, `co.quan`
-and `co.arr` exist to create unitful `YTQuantities` and `YTArray` with the
+The helper functions, ``co.quan``
+and ``co.arr`` exist to create unitful ``YTQuantities`` and ``YTArray`` with the
 unit registry of the cosmology calculator.  For more information on the usage
 and meaning of each calculation, consult the reference documentation at
 :ref:`cosmology-calculator-ref`.

doc/source/analyzing/domain_analysis/index.rst

@@ -72,5 +72,5 @@ Extensions
 There are a number of independent, yt-related packages for things
 like visual effects, interactive widgets, synthetic absorption
 spectra, X-ray observations, and merger-trees. See the
-`yt Extensions <http://yt-project.org/extensions.html>` page for
+`yt Extensions <http://yt-project.org/extensions.html>`_ page for
 a list of available extension packages.

doc/source/analyzing/fields.rst

@@ -51,8 +51,8 @@ for tab-completing as well as easier access to information.
 Additionally, if you have `ipywidgets
 <https://ipywidgets.readthedocs.io/en/stable/>`_ installed and are in a `Jupyter
 environment <https://jupyter.org/>`_, you can view the rich representation of
-the fields (including source code) by either typing `ds.fields` as the last
-item in a cell or by calling `display(ds.fields)`.  The resulting output will
+the fields (including source code) by either typing ``ds.fields`` as the last
+item in a cell or by calling ``display(ds.fields)``.  The resulting output will
 have tabs and source:
 
 .. image:: _images/fields_ipywidget.png
@@ -412,6 +412,8 @@ different magnetic field units in the different :ref:`unit systems <unit_systems
 determine how to set up special magnetic field handling when designing a new frontend, check out
 :ref:`bfields-frontend`.
 
+.. _species-fields:
+
 Species Fields
 --------------
 
@@ -454,7 +456,7 @@ defined:
 To refer to the number density of the entirety of a single atom or molecule (regardless
 of its ionization state), please use the ``MM_nuclei_density`` fields.
 
-Finally, if the abundances of hydrogen and helium are not defined, it is assumed that
+Finally, if the abundances of hydrogen and helium are not defined, it is
 assumed that these elements are fully ionized with primordial abundances In this case,
 the following fields are defined:
 
@@ -607,7 +609,7 @@ this will be handled automatically:
     )
 
 Which, because the axis is ``"z"``, will give you the same result if you had
-projected the `"velocity_z"`` field. This also works for off-axis projections:
+projected the ``"velocity_z"`` field. This also works for off-axis projections:
 
 .. code-block:: python
 
@@ -806,10 +808,6 @@ default this is 64, but it can be supplied as the final argument to
 ``add_nearest_neighbor_field``.  For the example above, it would be
 ``nearest_neighbor_64``.
 
-This can then be used as input to the function
-``add_volume_weighted_smoothed_field``, which can enable smoothing particle
-types that would normally not be smoothed.
-
 Commonly, not just the identity of the nearest particle is interesting, but the
 value of a given field associated with that particle.  yt provides a function
 that can do this, as well.  This deposits into the indexing octree the value

doc/source/analyzing/filtering.rst

@@ -12,7 +12,7 @@ Filters can be generated based on spatial position, say in a sphere
 in the center of your dataset space, or more generally they can be
 defined by the properties of any field in the simulation.
 
-Because `mesh fields` are internally different from `particle fields`,
+Because *mesh fields* are internally different from *particle fields*,
 there are different ways of filtering each type as indicated below;
 however, filtering fields by spatial location (i.e. geometric
 objects) will apply to both types equally.
@@ -54,11 +54,11 @@ set a simple mask based on the contents of one of our fields.
     import yt
     ds = yt.load('Enzo_64/DD0042/data0042')
     ad = ds.all_data()
-    hot = ad["temperature"].in_units('K') > 1e6
-    print('Temperature of all data: ad["temperature"] = \n%s' % ad["temperature"])
+    hot = ad[("gas", "temperature")].in_units('K') > 1e6
+    print('Temperature of all data: ad[("gas", "temperature")] = \n%s' % ad[("gas", "temperature")])
     print("Boolean Mask: hot = \n%s" % hot)
-    print('Temperature of "hot" data: ad["temperature"][hot] = \n%s' %
-          ad['temperature'][hot])
+    print('Temperature of "hot" data: ad[("gas", "temperature")][hot] = \n%s' %
+          ad[("gas", "temperature")][hot])
 
 This was a simple example, but one can make the conditionals that define
 a boolean mask have multiple parts, and one can stack masks together to
@@ -71,9 +71,9 @@ used if you simply need to access the NumPy arrays:
     ds = yt.load('Enzo_64/DD0042/data0042')
     ad = ds.all_data()
     overpressure_and_fast = (ad["pressure"] > 1e-14) & (ad["velocity_magnitude"].in_units('km/s') > 1e2)
-    print('Density of all data: ad["density"] = \n%s' % ad['density'])
-    print('Density of "overpressure and fast" data: overpressure_and_fast['density'] = \n%s' %
-          overpressure_and_fast['density'])
+    print('Density of all data: ad[("gas", "density")] = \n%s' % ad[("gas", "density")])
+    print('Density of "overpressure and fast" data: overpressure_and_fast[("gas", "density")] = \n%s' %
+          overpressure_and_fast[("gas", "density")])
 
 .. _cut-regions:
 
@@ -96,13 +96,13 @@ filtering out unwanted regions. Such wrapper functions are methods of
    import yt
    ds = yt.load('Enzo_64/DD0042/data0042')
    ad = ds.all_data()
-   overpressure_and_fast = ad.include_above('pressure', 1e-14)
+   overpressure_and_fast = ad.include_above(("gas", "pressure"), 1e-14)
    # You can chain include_xx and exclude_xx to produce the intersection of cut regions
-   overpressure_and_fast = overpressure_and_fast.include_above('velocity_magnitude', 1e2, 'km/s')
+   overpressure_and_fast = overpressure_and_fast.include_above(("gas", "velocity_magnitude"), 1e2, 'km/s')
 
-   print('Density of all data: ad["density"] = \n%s' % ad['density'])
-   print('Density of "overpressure and fast" data: overpressure_and_fast["density"] = \n%s' %
-          overpressure_and_fast['density'])
+   print('Density of all data: ad[("gas", "density")] = \n%s' % ad[("gas", density")])
+   print('Density of "overpressure and fast" data: overpressure_and_fast[("gas", "density")] = \n%s' %
+          overpressure_and_fast[("gas", "density")])
 
 The following exclude and include functions are supported:
    - :func:`~yt.data_objects.data_containers.YTSelectionContainer3D.include_equal` - Only include values equal to given value

doc/source/analyzing/generating_processed_data.rst

@@ -146,7 +146,7 @@ density within a sphere can be created in the following way:
            ("gas", "temperature"),  # profile field
            ("gas", "radial_velocity"),
        ],  # profile field
-       weight_field=("gas", "cell_mass"),
+       weight_field=("gas", "mass"),
    )
 
 The binning, weight, and profile data can now be access as:
@@ -180,8 +180,11 @@ temperature can be created as follows:
 .. code-block:: python
 
    profile2d = source.profile(
-       [("gas", "density"), ("gas", "temperature")],  # the x bin field  # the y bin field
-       [("gas", "cell_mass")],  # the profile field
+       [
+           ("gas", "density"),
+           ("gas", "temperature"),
+       ],  # the x bin field  # the y bin field
+       [("gas", "mass")],  # the profile field
        weight_field=None,
    )
 
@@ -191,7 +194,7 @@ Accessing the x, y, and profile fields work just as with one-dimensional profile
 
    print(profile2d.x)
    print(profile2d.y)
-   print(profile2d["gas", "cell_mass"])
+   print(profile2d["gas", "mass"])
 
 One of the more interesting things that is enabled with this approach is
 the generation of 1D profiles that correspond to 2D profiles.  For instance, a
@@ -219,7 +222,7 @@ for each bin field or ``None`` to use the default settings.
     custom_bins = np.array([1e-27, 1e-25, 2e-25, 5e-25, 1e-23])
     profile2d = source.profile(
         [("gas", "density"), ("gas", "temperature")],
-        [("gas", "cell_mass")],
+        [("gas", "mass")],
         override_bins={("gas", "density"): custom_bins, ("gas", "temperature"): None},
     )
 
@@ -230,8 +233,8 @@ Exporting Profiles to DataFrame
 
 One-dimensional profile data can be exported to a :class:`~pandas.DataFrame` object
 using the :meth:`yt.data_objects.profiles.Profile1D.to_dataframe` method. Bins which
-do not have data will have their fields filled with `NaN`s, except for the bin field
-itself. If you only want to export the bins which are used, set `only_used=True`.
+do not have data will have their fields filled with ``NaN``, except for the bin field
+itself. If you only want to export the bins which are used, set ``only_used=True``.
 
 .. code-block:: python
 
@@ -253,8 +256,8 @@ Exporting Profiles to QTable
 One-dimensional profile data also can be exported to an AstroPy :class:`~astropy.table.QTable`
 object. This table can then be written to disk in a number of formats, such as ASCII text
 or FITS files, and manipulated in a number of ways. Bins which do not have data
-will have their mask values set to `False`. If you only want to export the bins
-which are used, set `only_used=True`. Units are preserved in the table by converting
+will have their mask values set to ``False``. If you only want to export the bins
+which are used, set ``only_used=True``. Units are preserved in the table by converting
 each :class:`~yt.units.yt_array.YTArray` to an :class:`~astropy.units.Quantity`.
 
 To export the 1D profile to a Table object, simply call
@@ -330,4 +333,4 @@ interoperability with anything that can take xarray data.  The classes that can
    grid = ds.r[::256j, ::256j, ::256j]
    obj = grid.to_xarray(fields=["density", "temperature"])
 
-The returned object, `obj`, will now have the correct labeled axes and so forth.
+The returned object, ``obj``, will now have the correct labeled axes and so forth.

doc/source/analyzing/ionization_cube.py

@@ -11,7 +11,9 @@
     name="IonizedHydrogen", units="", display_name=r"\frac{\rho_{HII}}{\rho_H}"
 )
 def IonizedHydrogen(field, data):
-    return data["HII_Density"] / (data["HI_Density"] + data["HII_Density"])
+    return data[("gas", "HII_Density")] / (
+        data[("gas", "HI_Density")] + data[("gas", "HII_Density")]
+    )
 
 
 ts = yt.DatasetSeries("SED800/DD*/*.index", parallel=8)