Merge pull request #4844 from cphyc/remove-parenthesis-in-getitem-calls

Remove parenthesis in getitem calls

doc/source/analyzing/fields.rst

@@ -148,7 +148,7 @@ following:
     density = ad["gas", "density"]
 
     # full field tuple
-    density = ad[("gas", "density")]
+    density = ad["gas", "density"]
 
     # through the ds.fields object
     density = ad[ds.fields.gas.density]

doc/source/analyzing/filtering.rst

@@ -168,7 +168,7 @@ only the particles with ``particle_type`` (i.e.  field = ``('all',
 
     @yt.particle_filter(requires=["particle_type"], filtered_type="all")
     def stars(pfilter, data):
-        filter = data[(pfilter.filtered_type, "particle_type")] == 2
+        filter = data[pfilter.filtered_type, "particle_type"] == 2
         return filter
 
 The :func:`~yt.data_objects.particle_filters.particle_filter` decorator takes a
@@ -188,7 +188,7 @@ As an alternative syntax, you can also define a new particle filter via the
 .. code-block:: python
 
     def stars(pfilter, data):
-        filter = data[(pfilter.filtered_type, "particle_type")] == 2
+        filter = data[pfilter.filtered_type, "particle_type"] == 2
         return filter
 
 

doc/source/analyzing/ionization_cube.py

@@ -11,8 +11,8 @@
     name="IonizedHydrogen", units="", display_name=r"\frac{\rho_{HII}}{\rho_H}"
 )
 def IonizedHydrogen(field, data):
-    return data[("gas", "HII_Density")] / (
-        data[("gas", "HI_Density")] + data[("gas", "HII_Density")]
+    return data["gas", "HII_Density"] / (
+        data["gas", "HI_Density"] + data["gas", "HII_Density"]
     )
 
 

doc/source/cookbook/custom_colorbar_tickmarks.ipynb

@@ -46,7 +46,7 @@
    },
    "outputs": [],
    "source": [
-    "plot = slc.plots[(\"gas\", \"density\")]"
+    "plot = slc.plots[\"gas\", \"density\"]"
    ]
   },
   {

doc/source/cookbook/extract_fixed_resolution_data.py

@@ -25,7 +25,7 @@
 f = h5py.File("my_data.h5", mode="w")
 
 # We create a dataset at the root, calling it "density"
-f.create_dataset("/density", data=cube[("gas", "density")])
+f.create_dataset("/density", data=cube["gas", "density"])
 
 # We close our file
 f.close()

doc/source/cookbook/hse_field.py

@@ -17,12 +17,12 @@
 
 def _hse(field, data):
     # Remember that g is the negative of the potential gradient
-    gx = -data[("gas", "density")] * data[("gas", "gravitational_potential_gradient_x")]
-    gy = -data[("gas", "density")] * data[("gas", "gravitational_potential_gradient_y")]
-    gz = -data[("gas", "density")] * data[("gas", "gravitational_potential_gradient_z")]
-    hx = data[("gas", "pressure_gradient_x")] - gx
-    hy = data[("gas", "pressure_gradient_y")] - gy
-    hz = data[("gas", "pressure_gradient_z")] - gz
+    gx = -data["gas", "density"] * data["gas", "gravitational_potential_gradient_x"]
+    gy = -data["gas", "density"] * data["gas", "gravitational_potential_gradient_y"]
+    gz = -data["gas", "density"] * data["gas", "gravitational_potential_gradient_z"]
+    hx = data["gas", "pressure_gradient_x"] - gx
+    hy = data["gas", "pressure_gradient_y"] - gy
+    hz = data["gas", "pressure_gradient_z"] - gz
     h = np.sqrt((hx * hx + hy * hy + hz * hz) / (gx * gx + gy * gy + gz * gz))
     return h
 

doc/source/cookbook/matplotlib-animation.py

@@ -8,7 +8,7 @@
 plot = yt.SlicePlot(ts[0], "z", ("gas", "density"))
 plot.set_zlim(("gas", "density"), 8e-29, 3e-26)
 
-fig = plot.plots[("gas", "density")].figure
+fig = plot.plots["gas", "density"].figure
 
 
 # animate must accept an integer frame number. We use the frame number

doc/source/cookbook/multi_plot_3x2_FRB.py

@@ -43,8 +43,8 @@
 
     # converting our fixed resolution buffers to NDarray so matplotlib can
     # render them
-    dens = np.array(frb[("gas", "density")])
-    temp = np.array(frb[("gas", "temperature")])
+    dens = np.array(frb["gas", "density"])
+    temp = np.array(frb["gas", "temperature"])
 
     plots.append(den_axis.imshow(dens, norm=LogNorm()))
     plots[-1].set_clim((5e-32, 1e-29))

doc/source/cookbook/multi_plot_slice_and_proj.py

@@ -43,12 +43,12 @@
 # Converting our Fixed Resolution Buffers to numpy arrays so that matplotlib
 # can render them
 
-slc_dens = np.array(slc_frb[("gas", "density")])
-proj_dens = np.array(proj_frb[("gas", "density")])
-slc_temp = np.array(slc_frb[("gas", "temperature")])
-proj_temp = np.array(proj_frb[("gas", "temperature")])
-slc_vel = np.array(slc_frb[("gas", "velocity_magnitude")])
-proj_vel = np.array(proj_frb[("gas", "velocity_magnitude")])
+slc_dens = np.array(slc_frb["gas", "density"])
+proj_dens = np.array(proj_frb["gas", "density"])
+slc_temp = np.array(slc_frb["gas", "temperature"])
+proj_temp = np.array(proj_frb["gas", "temperature"])
+slc_vel = np.array(slc_frb["gas", "velocity_magnitude"])
+proj_vel = np.array(proj_frb["gas", "velocity_magnitude"])
 
 plots = [
     dens_axes[0].imshow(slc_dens, origin="lower", norm=LogNorm()),

doc/source/cookbook/multiplot_2x2_time_series.py

@@ -39,7 +39,7 @@
     p.set_zlim(("gas", "density"), 1e-4, 1e-2)
 
     # This forces the ProjectionPlot to redraw itself on the AxesGrid axes.
-    plot = p.plots[("gas", "density")]
+    plot = p.plots["gas", "density"]
     plot.figure = fig
     plot.axes = grid[i].axes
     plot.cax = grid.cbar_axes[i]

doc/source/cookbook/multiplot_phaseplot.py

@@ -40,7 +40,7 @@
     p.set_zlim(("gas", "mass"), 1e42, 1e46)
 
     # This forces the ProjectionPlot to redraw itself on the AxesGrid axes.
-    plot = p.plots[("gas", "mass")]
+    plot = p.plots["gas", "mass"]
     plot.figure = fig
     plot.axes = grid[i].axes
     if i == 0:

doc/source/cookbook/rad_velocity.py

@@ -44,9 +44,9 @@
 
 ax.plot(
     rp0.x.value,
-    rp0[("gas", "radial_velocity")].in_units("km/s").value,
+    rp0["gas", "radial_velocity"].in_units("km/s").value,
     rp1.x.value,
-    rp1[("gas", "radial_velocity")].in_units("km/s").value,
+    rp1["gas", "radial_velocity"].in_units("km/s").value,
 )
 
 ax.set_xlabel(r"$\mathrm{r\ (kpc)}$")

doc/source/cookbook/radial_profile_styles.py

@@ -24,7 +24,7 @@
 ax = fig.add_subplot(111)
 
 # Plot the density as a log-log plot using the default settings
-dens_plot = ax.loglog(rp.x.value, rp[("gas", "density")].value)
+dens_plot = ax.loglog(rp.x.value, rp["gas", "density"].value)
 
 # Here we set the labels of the plot axes
 

doc/source/cookbook/simple_contour_in_slice.py

@@ -25,7 +25,7 @@
 )
 
 # This is the plot object.
-po = sp.plots[("gas", "density")]
+po = sp.plots["gas", "density"]
 
 # Turn off the colormap image, leaving just the contours.
 po.axes.images[0].set_visible(False)

doc/source/cookbook/simple_slice_matplotlib_example.py

@@ -13,15 +13,15 @@
 slc.render()
 
 # Get a reference to the matplotlib axes object for the plot
-ax = slc.plots[("gas", "density")].axes
+ax = slc.plots["gas", "density"].axes
 
 # Let's adjust the x axis tick labels
 for label in ax.xaxis.get_ticklabels():
     label.set_color("red")
     label.set_fontsize(16)
 
 # Get a reference to the matplotlib figure object for the plot
-fig = slc.plots[("gas", "density")].figure
+fig = slc.plots["gas", "density"].figure
 
 # And create a mini-panel of a gaussian histogram inside the plot
 rect = (0.2, 0.2, 0.2, 0.2)

doc/source/cookbook/streamlines_isocontour.py

@@ -59,7 +59,7 @@
 surface = ds.surface(sphere, ("gas", "density"), 1e-24)
 
 # Color this isodensity surface according to the log of the temperature field
-colors = yt.apply_colormap(np.log10(surface[("gas", "temperature")]), cmap_name="hot")
+colors = yt.apply_colormap(np.log10(surface["gas", "temperature"]), cmap_name="hot")
 
 # Render this surface
 p3dc = Poly3DCollection(surface.triangles, linewidth=0.0)

doc/source/cookbook/surface_plot.py

@@ -16,7 +16,7 @@
 surface = ds.surface(sphere, ("gas", "density"), 1e-24)
 
 # Color this isodensity surface according to the log of the temperature field
-colors = yt.apply_colormap(np.log10(surface[("gas", "temperature")]), cmap_name="hot")
+colors = yt.apply_colormap(np.log10(surface["gas", "temperature"]), cmap_name="hot")
 
 # Create a 3D matplotlib figure for visualizing the surface
 fig = plt.figure()

doc/source/cookbook/time_series.py

@@ -30,7 +30,7 @@
     # Create a sphere of radius 100 kpc at the center of the dataset volume
     sphere = ds.sphere("c", (100.0, "kpc"))
     # Calculate the entropy within that sphere
-    entr = sphere[("gas", "entropy")].sum()
+    entr = sphere["gas", "entropy"].sum()
     # Store the current time and sphere entropy for this dataset in our
     # storage dictionary as a tuple
     store.result = (ds.current_time.in_units("Gyr"), entr)

doc/source/cookbook/yt_gadget_owls_analysis.ipynb

@@ -160,7 +160,7 @@
    },
    "outputs": [],
    "source": [
-    "ad[(\"PartType0\", \"Coordinates\")]"
+    "ad[\"PartType0\", \"Coordinates\"]"
    ]
   },
   {
@@ -171,7 +171,7 @@
    },
    "outputs": [],
    "source": [
-    "ad[(\"PartType4\", \"IronFromSNIa\")]"
+    "ad[\"PartType4\", \"IronFromSNIa\"]"
    ]
   },
   {
@@ -182,7 +182,7 @@
    },
    "outputs": [],
    "source": [
-    "ad[(\"PartType1\", \"ParticleIDs\")]"
+    "ad[\"PartType1\", \"ParticleIDs\"]"
    ]
   },
   {
@@ -193,7 +193,7 @@
    },
    "outputs": [],
    "source": [
-    "ad[(\"PartType0\", \"Hydrogen\")]"
+    "ad[\"PartType0\", \"Hydrogen\"]"
    ]
   },
   {

doc/source/examining/loading_data.rst

@@ -648,9 +648,9 @@ to the z direction.
 
     ds.index
     ad = ds.all_data()
-    print(ds.field_info[("raw", "Ex")].nodal_flag)
+    print(ds.field_info["raw", "Ex"].nodal_flag)
     print(ad["raw", "Ex"].shape)
-    print(ds.field_info[("raw", "Bx")].nodal_flag)
+    print(ds.field_info["raw", "Bx"].nodal_flag)
     print(ad["raw", "Bx"].shape)
     print(ds.field_info["raw", "Bx"].nodal_flag)
     print(ad["raw", "Bx"].shape)
@@ -753,11 +753,11 @@ direction.
 
     ds.index
     ad = ds.all_data()
-    print(ds.field_info[("enzo", "Ex")].nodal_flag)
+    print(ds.field_info["enzo", "Ex"].nodal_flag)
     print(ad["enzo", "Ex"].shape)
-    print(ds.field_info[("enzo", "BxF")].nodal_flag)
+    print(ds.field_info["enzo", "BxF"].nodal_flag)
     print(ad["enzo", "Bx"].shape)
-    print(ds.field_info[("enzo", "Bx")].nodal_flag)
+    print(ds.field_info["enzo", "Bx"].nodal_flag)
     print(ad["enzo", "Bx"].shape)
 
 Here, the field ``('enzo', 'Ex')`` is nodal in two directions, so four values

doc/source/examining/low_level_inspection.rst

@@ -243,13 +243,13 @@ shape is the same, but the number of cells with data is less
        level=0, left_edge=[0, 0.0, 0.0], dims=ds.domain_dimensions, data_source=sp
    )
 
-   print(cg_sp[("gas", "density")].shape)
+   print(cg_sp["gas", "density"].shape)
    (64, 64, 64)
 
-   print(cg_sp[("gas", "density")].size)
+   print(cg_sp["gas", "density"].size)
    262144
 
-   print(cg_sp[("gas", "density")][cg_sp[("gas", "density")] != 0].size)
+   print(cg_sp["gas", "density"][cg_sp["gas", "density"] != 0].size)
    17256
 
 The ``data_source`` can be any :ref:`3D Data Container <region-reference>`. Also

doc/source/visualizing/callbacks.rst

@@ -229,8 +229,8 @@ Clump Finder Callback
    ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
    data_source = ds.disk([0.5, 0.5, 0.5], [0.0, 0.0, 1.0], (8.0, "kpc"), (1.0, "kpc"))
 
-   c_min = 10 ** np.floor(np.log10(data_source[("gas", "density")]).min())
-   c_max = 10 ** np.floor(np.log10(data_source[("gas", "density")]).max() + 1)
+   c_min = 10 ** np.floor(np.log10(data_source["gas", "density"]).min())
+   c_max = 10 ** np.floor(np.log10(data_source["gas", "density"]).max() + 1)
 
    master_clump = Clump(data_source, ("gas", "density"))
    master_clump.add_validator("min_cells", 20)

doc/source/yt4differences.rst

@@ -218,10 +218,10 @@ method:
     plot.save()
 
     arbitrary_grid = ds.arbitrary_grid([0.0, 0.0, 0.0], [25, 25, 25], dims=[16, 16, 16])
-    ag_density = arbitrary_grid[("gas", "density")]
+    ag_density = arbitrary_grid["gas", "density"]
 
     covering_grid = ds.covering_grid(4, 0, 16)
-    cg_density = covering_grid[("gas", "density")]
+    cg_density = covering_grid["gas", "density"]
 
 In the above example the ``covering_grid`` and the ``arbitrary_grid`` will return
 the same data. In fact, these containers are very similar but provide a
@@ -296,10 +296,10 @@ gather machinery also added.
     print(octree.sph_smoothing_style)
 
     # the density will be calculated using SPH scatter
-    density = octree[("PartType0", "density")]
+    density = octree["PartType0", "density"]
 
     # this will return the x positions of the octs
-    x = octree[("index", "x")]
+    x = octree["index", "x"]
 
 The above code can be modified to use the gather approach by using
 ``ds.sph_smoothing_style = 'gather'`` before any field access. The octree just