diff --git a/.flake8 b/.flake8
index 1fee4a5fa..5f10e3839 100644
--- a/.flake8
+++ b/.flake8
@@ -16,6 +16,8 @@ ignore =
     N815
     # Docstring in imperative mood. This should *not* be the case for @property's, but can't ignore them atm.
     D401
+    # Module shadowing a builtin
+    A005
 max-line-length = 88
 max-complexity = 70
 docstring-convention=numpy
diff --git a/.github/workflows/test_build_macos.yaml b/.github/workflows/test_build_macos.yaml
index 3f7f6f844..d079ec3f7 100644
--- a/.github/workflows/test_build_macos.yaml
+++ b/.github/workflows/test_build_macos.yaml
@@ -20,7 +20,7 @@ jobs:
     env:
       PYTHON: ${{ matrix.python-version }}
       CC: gcc
-    name: Testing
+    name: Test MacOS Build
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -46,7 +46,6 @@ jobs:
         uses: conda-incubator/setup-miniconda@v3
         with:
           # auto-update-conda: true
-          mamba-version: "*"
           channels: conda-forge,defaults
           python-version: ${{ matrix.python-version }}
           environment-file: ci/macos-latest-env.yml
diff --git a/.github/workflows/test_suite.yaml b/.github/workflows/test_suite.yaml
index 91d673e3c..3182ac3bb 100644
--- a/.github/workflows/test_suite.yaml
+++ b/.github/workflows/test_suite.yaml
@@ -46,7 +46,6 @@ jobs:
         uses: conda-incubator/setup-miniconda@v3
         with:
           # auto-update-conda: true
-          mamba-version: "*"
           channels: conda-forge,defaults
           python-version: ${{ matrix.python-version }}
           environment-file: ci/${{ matrix.os }}-env.yml
diff --git a/.github/workflows/test_suite_nointegration.yaml b/.github/workflows/test_suite_nointegration.yaml
index 5dfad87f8..0d18c9016 100644
--- a/.github/workflows/test_suite_nointegration.yaml
+++ b/.github/workflows/test_suite_nointegration.yaml
@@ -20,7 +20,7 @@ jobs:
       PYTHON: ${{ matrix.python-version }}
       OS: ${{ matrix.os }}
       CC: gcc
-    name: Testing
+    name: Sans Integration Tests
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -45,7 +45,6 @@ jobs:
         uses: conda-incubator/setup-miniconda@v3
         with:
           # auto-update-conda: true
-          mamba-version: "*"
           channels: conda-forge,defaults
           python-version: ${{ matrix.python-version }}
           environment-file: ci/${{ matrix.os }}-env.yml
diff --git a/docs/conf.py b/docs/conf.py
index 321ac5530..f39982792 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -35,7 +35,7 @@ def __getattr__(cls, name):
 out = subprocess.run(["python", "setup.py", "--version"], capture_output=True)
 
 try:
-    from py21cmfast import cache_tools
+    from py21cmfast.io import cache_tools
 except ImportError:
     raise 
     
diff --git a/docs/faqs/misc.rst b/docs/faqs/misc.rst
index f8084a774..14bc1aa4a 100644
--- a/docs/faqs/misc.rst
+++ b/docs/faqs/misc.rst
@@ -41,27 +41,6 @@ To make the current configuration permanent, simply use the ``write`` method::
     >>> p21.config['direc'] = 'my_own_cache'
     >>> p21.config.write()
 
-Global Parameters
------------------
-There are a bunch of "global" parameters that are used throughout the C code. These are
-parameters that are deemed to be constant enough to not expose them through the
-regularly-used input structs, but nevertheless may necessitate modification from
-time-to-time. These are accessed through the ``global_params`` object::
-
-    >>> from py21cmfast import global_params
-
-Help on the attributes can be obtained via ``help(global_params)`` or
-`in the docs <../reference/_autosummary/py21cmfast.inputs.html>`_. Setting the
-attributes (which affects them everywhere throughout the code) is as simple as, eg::
-
-    >>> global_params.Z_HEAT_MAX = 30.0
-
-If you wish to use a certain parameter for a fixed portion of your code (eg. for a single
-run), it is encouraged to use the context manager, eg.::
-
-    >>> with global_params.use(Z_HEAT_MAX=10):
-    >>>    run_lightcone(...)
-
 How can I read a Coeval object from disk?
 -----------------------------------------
 
@@ -69,7 +48,7 @@ The simplest way to read a :class:`py21cmfast.outputs.Coeval` object that has be
 written to disk is by doing::
 
     import py21cmfast as p21c
-    coeval = p21c.Coeval.read("my_coeval.h5")
+    coeval = p21c.Coeval.from_file("my_coeval.h5")
 
 However, you may want to read parts of the data, or read the data using a different
 language or environment. You can do this as long as you have the HDF5 library (i.e.
@@ -84,9 +63,6 @@ structure of the file yourself interactively. But here is an example using h5py:
     # the CosmoParams, FlagOptions and AstroParams are accessed the same way.
     print(dict(fl['user_params'].attrs))
 
-    # print a dict of all globals used for the coeval
-    print(dict(fl['_globals'].attrs))
-
     # Get the redshift and random seed of the coeval box
     redshift = fl.attrs['redshift']
     seed = fl.attrs['random_seed']
@@ -122,3 +98,19 @@ while the globally averaged quantities are in the ``global_quantities`` group::
     redshifts = fl['node_redshifts']
 
     plt.plot(redshifts, global_Tb)
+
+Can I instantiate my own OutputStruct objects?
+-------------------------------------------
+Usually, you create instances of an :class:`py21cmfast.wrapper.outputs.OutputStruct`
+object by running either :func:`py21cmfast.run_coeval` or some lower-level function,
+like :func:`py21cmfast.compute_initial_conditions`. However, it's possible you want to
+switch out a simulation step from ``21cmFAST`` and insert your own, but then go on using
+that box in further ``21cmFAST`` simulation components. The way to do this is as follows,
+using the ``InitialConditions`` as an example::
+
+    ics = p21c.InitialConditions.new(inputs=p21c.InputParameters())
+    ics.set('lowres_density', my_computed_value)
+
+You would use this ``.set()`` method on each of the fields you needed to set. Now this
+data should be properly shared with the backend C-code, and the object can be used
+in subsequent steps within ``21cmFAST``.
diff --git a/docs/tutorials/coeval_cubes.ipynb b/docs/tutorials/coeval_cubes.ipynb
index d01e0bcc5..6d02cf8c6 100644
--- a/docs/tutorials/coeval_cubes.ipynb
+++ b/docs/tutorials/coeval_cubes.ipynb
@@ -39,9 +39,7 @@
     "# For plotting the cubes, we use the plotting submodule:\n",
     "from py21cmfast import plotting\n",
     "\n",
-    "# For interacting with the cache\n",
-    "from py21cmfast import cache_tools\n",
-    "\n"
+    "from tempfile import mkdtemp"
    ]
   },
   {
@@ -58,7 +56,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using 21cmFAST version 3.0.2\n"
+      "Using 21cmFAST version 3.4.1.dev400+gf3997fed\n"
      ]
     }
    ],
@@ -76,27 +74,10 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:11.367976Z",
-     "start_time": "2020-02-29T22:10:11.062517Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-10-02 09:51:10,651 | INFO | Removed 0 files from cache.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "if not os.path.exists('_cache'):\n",
-    "    os.mkdir('_cache')\n",
-    "    \n",
-    "p21c.config['direc'] = '_cache'\n",
-    "cache_tools.clear_cache(direc=\"_cache\")"
+    "cache = p21c.OutputCache(mkdtemp())"
    ]
   },
   {
@@ -110,1431 +91,1268 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The simplest (and typically most efficient) way to produce a coeval cube is simply to use the `run_coeval` method. This consistently performs all steps of the calculation, re-using any data that it can without re-computation or increased memory overhead."
+    "### Setting Up Inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All possible input parameters for a `21cmFAST` simulation are defined in the \n",
+    "`InputParameters` object. This object is sub-divided into four constituent sections:\n",
+    "`cosmo_params`, `astro_params`, `user_params`, and `flag_options`, as well as the\n",
+    "`random_seed` and `node_redshifts`, which define the redshifts required for computing\n",
+    "the evolution of the simulation.\n",
+    "\n",
+    "You can create a full `InputParameters` object quickly and easily in a few ways.\n",
+    "The first is simply to instantiate it directly. Here, the only _required_ parameter\n",
+    "is the random seed:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:27.413255Z",
-     "start_time": "2020-02-29T22:10:11.369635Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "coeval8, coeval9, coeval10 = p21c.run_coeval(\n",
-    "    redshift = [8.0, 9.0, 10.0],\n",
-    "    user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100, \"USE_INTERPOLATION_TABLES\": True},\n",
-    "    cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n",
-    "    astro_params = p21c.AstroParams({\"HII_EFF_FACTOR\":20.0}),\n",
-    "    random_seed=12345\n",
-    ")"
+    "inputs = p21c.InputParameters(random_seed=12345)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are a number of possible inputs for `run_coeval`, which you can check out either in the [API reference](../reference/py21cmfast.html) or by calling `help(p21c.run_coeval)`. Notably, the `redshift` must be given: it can be a single number, or a list of numbers, defining the redshift at which the output coeval cubes will be defined. \n",
-    "\n",
-    "Other params we've given here are `user_params`, `cosmo_params` and `astro_params`. These are all used for defining input parameters into the backend C code (there's also another possible input of this kind; `flag_options`). These can be given either as a dictionary (as `user_params` has been), or directly as a relevant object (like `cosmo_params` and `astro_params`). If creating the object directly, the parameters can be passed individually or via a single dictionary. So there's a lot of flexibility there! Nevertheless we *encourage* you to use the basic dictionary. The other ways of passing the information are there so we can use pre-defined objects later on. For more information about these \"input structs\", see the [API docs](../reference/_autosummary/py21cmfast.inputs.html).\n",
-    "\n",
-    "We've also given a `direc` option: this is the directory in which to search for cached data (and also where cached data should be written). Throughout this notebook we're going to set this directly to the `_cache` folder, which allows us to manage it directly. By default, the cache location is set in the global configuration in `~/.21cmfast/config.yml`. You'll learn more about caching further on in this tutorial. \n",
-    "\n",
-    "Finally, we've given a random seed. This sets all the random phases for the simulation, and ensures that we can exactly reproduce the same results on every run. \n",
-    "\n",
-    "The output of `run_coeval` is a list of `Coeval` instances, one for each input redshift (it's just a single object if a single redshift was passed, not a list). They store *everything* related to that simulation, so that it can be completely compared to other simulations. \n",
-    "\n",
-    "For example, the input parameters:"
+    "You can check out the full set of default parameters:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:27.418017Z",
-     "start_time": "2020-02-29T22:10:27.414590Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Random Seed:  12345\n",
-      "Redshift:  8.0\n",
-      "UserParams(BOX_LEN:100, DIM:300, HII_DIM:100, HMF:1, POWER_SPECTRUM:0, USE_FFTW_WISDOM:False, USE_RELATIVE_VELOCITIES:False)\n"
+      "cosmo_params: CosmoParams(SIGMA_8=0.8102, hlittle=0.6766, OMm=0.30964144154550644, OMb=0.04897468161869667, POWER_INDEX=0.9665)\n",
+      "user_params: UserParams(BOX_LEN=300.0, HII_DIM=200, DIM=600, NON_CUBIC_FACTOR=1.0, USE_FFTW_WISDOM=False, HMF='ST', USE_RELATIVE_VELOCITIES=False, POWER_SPECTRUM='EH', N_THREADS=1, PERTURB_ON_HIGH_RES=False, NO_RNG=False, USE_INTERPOLATION_TABLES=True, INTEGRATION_METHOD_ATOMIC='GAUSS-LEGENDRE', INTEGRATION_METHOD_MINI='GAUSS-LEGENDRE', USE_2LPT=True, MINIMIZE_MEMORY=False, KEEP_3D_VELOCITIES=False, SAMPLER_MIN_MASS=100000000.0, SAMPLER_BUFFER_FACTOR=2.0, MAXHALO_FACTOR=2.0, N_COND_INTERP=200, N_PROB_INTERP=400, MIN_LOGPROB=-12.0, SAMPLE_METHOD='MASS-LIMITED', AVG_BELOW_SAMPLER=True, HALOMASS_CORRECTION=0.9, PARKINSON_G0=1.0, PARKINSON_y1=0.0, PARKINSON_y2=0.0, Z_HEAT_MAX=35.0, ZPRIME_STEP_FACTOR=1.02)\n",
+      "astro_params: AstroParams(HII_EFF_FACTOR=30.0, F_STAR10=-1.3, ALPHA_STAR=0.5, F_STAR7_MINI=-2.8, ALPHA_STAR_MINI=0.5, F_ESC10=-1.0, ALPHA_ESC=-0.5, F_ESC7_MINI=-2.0, M_TURN=8.7, R_BUBBLE_MAX=15.0, ION_Tvir_MIN=4.69897, L_X=40.5, L_X_MINI=40.5, NU_X_THRESH=500.0, X_RAY_SPEC_INDEX=1.0, X_RAY_Tvir_MIN=4.69897, F_H2_SHIELD=0.0, t_STAR=0.5, N_RSD_STEPS=20, A_LW=2.0, BETA_LW=0.6, A_VCB=1.0, BETA_VCB=1.8, UPPER_STELLAR_TURNOVER_MASS=11.447, UPPER_STELLAR_TURNOVER_INDEX=-0.6, SIGMA_STAR=0.25, SIGMA_LX=0.5, SIGMA_SFR_LIM=0.19, SIGMA_SFR_INDEX=-0.12, CORR_STAR=0.5, CORR_SFR=0.2, CORR_LX=0.2)\n",
+      "flag_options: FlagOptions(USE_MINI_HALOS=False, USE_CMB_HEATING=True, USE_LYA_HEATING=True, USE_MASS_DEPENDENT_ZETA=True, USE_HALO_FIELD=True, APPLY_RSDS=True, SUBCELL_RSD=False, INHOMO_RECO=False, USE_TS_FLUCT=False, FIX_VCB_AVG=False, HALO_STOCHASTICITY=True, USE_EXP_FILTER=True, FIXED_HALO_GRIDS=False, CELL_RECOMB=True, PHOTON_CONS_TYPE='no-photoncons', USE_UPPER_STELLAR_TURNOVER=True, M_MIN_in_Mass=True, HALO_SCALING_RELATIONS_MEDIAN=False)\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "print(\"Random Seed: \", coeval8.random_seed)\n",
-    "print(\"Redshift: \", coeval8.redshift)\n",
-    "print(coeval8.user_params)"
+    "print(inputs)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is where the utility of being able to pass a *class instance* for the parameters arises: we could run another iteration of coeval cubes, with the same user parameters, simply by doing `p21c.run_coeval(user_params=coeval8.user_params, ...)`.\n",
-    "\n",
-    "Also in the `Coeval` instance are the various outputs from the different steps of the computation. You'll see more about what these steps are further on in the tutorial. But for now, we show that various boxes are available:"
+    "To set any desired parameters, the easiest way is to pass them at instantiation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:27.431340Z",
-     "start_time": "2020-02-29T22:10:27.419347Z"
-    }
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = p21c.InputParameters(\n",
+    "    user_params=p21c.UserParams.new(HII_DIM=150),\n",
+    "    random_seed=12345\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(300, 300, 300)\n",
-      "(100, 100, 100)\n"
+      "150\n"
      ]
     }
    ],
    "source": [
-    "print(coeval8.hires_density.shape)\n",
-    "print(coeval8.brightness_temp.shape)"
+    "print(inputs.user_params.HII_DIM)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Along with these, full instances of the output from each step are available as attributes that end with \"struct\". These instances themselves contain the `numpy` arrays of the data cubes, and some other attributes that make them easier to work with:"
+    "However, you can also get a new set of inputs by evolving an existing set:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:27.440514Z",
-     "start_time": "2020-02-29T22:10:27.433056Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17.622644"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "coeval8.brightness_temp_struct.global_Tb"
+    "inputs_large = inputs.evolve_input_structs(BOX_LEN=1000.0, HII_DIM=500, DIM=1000)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, each of the components of the cube are cached to disk (in our `_cache/` folder) as we run it. However, the `Coeval` cube itself is _not_ written to disk by default. Writing it to disk incurs some redundancy, since that data probably already exists in the cache directory in seperate files.  \n",
-    "\n",
-    "Let's save to disk. The save method by default writes in the current directory (not the cache!):"
+    "In this case, you don't need to specify which sub-category of parameter each updated\n",
+    "parameter belongs to. Nevertheless, you can also update those sub-categories directly:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:27.644169Z",
-     "start_time": "2020-02-29T22:10:27.442997Z"
-    }
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "filename = coeval8.save(direc='_cache')"
+    "inputs_small = inputs_large.clone(user_params=p21c.UserParams())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The filename of the saved file is returned:"
+    "However, probably the easiest way to start a new simulation is to base your parameters\n",
+    "on a particular template. A number of templates are built-in, spanning the range of \n",
+    "simulations from small and fast to large and sophisticated:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:27.652399Z",
-     "start_time": "2020-02-29T22:10:27.647474Z"
-    }
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Coeval_z8.0_a3c7dea665420ae9c872ba2fab1b3d7d_r12345.h5\n"
+      "simple:\n",
+      "     A simple 21cmFAST run with no minihalos, discrete halos, recombinations or spin temperature fluctuations\n",
+      "const-zeta:\n",
+      "     A 21cmFAST run with constant ionising efficiency for halos of all mass\n",
+      "latest:\n",
+      "     Our latest fiducial run without discrete halos, includes recominations and spin temperature fluctuations\n",
+      "mini:\n",
+      "     A run including minihalos\n",
+      "latest-dhalos:\n",
+      "     Our latest fiducial run with discrete halos, recominations and spin temperature fluctuations\n",
+      "mini-dhalos:\n",
+      "     Run with discrete halos, including the molecularly cooled galaxy component\n",
+      "Park19:\n",
+      "     Exact fiducial parameters from Park et al 2019. Disables modules implemented afterwards\n",
+      "Qin20:\n",
+      "     Exact fiducial parameters from Qin et al 2020. Disables modules implemented afterwards\n",
+      "Munoz21:\n",
+      "     Exact fiducial parameters from Munoz et al 2021. Disables modules implemented afterwards\n",
+      "latest-small:\n",
+      "     Small version of latest.toml\n"
      ]
     }
    ],
    "source": [
-    "print(os.path.basename(filename))"
+    "for template in p21c.run_templates.list_templates():\n",
+    "    print(f\"{template['name']}:\\n     {template['description']}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Such files can be read in:"
+    "In this tutorial, we want something small and fast:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:28.655553Z",
-     "start_time": "2020-02-29T22:10:27.654557Z"
-    }
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "new_coeval8 = p21c.Coeval.read(filename, direc='.')"
+    "inputs = p21c.InputParameters.from_template('simple', random_seed=1234).evolve_input_structs(\n",
+    "    BOX_LEN=100.0, DIM=200, HII_DIM=100\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some convenient plotting functions exist in the `plotting` module. These can work directly on `Coeval` objects, or any of the output structs (as we'll see further on in the tutorial). By default the `coeval_sliceplot` function will plot the `brightness_temp`, using the standard traditional colormap:"
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "Note\n",
+    "\n",
+    "Why do we require you to specify the random seed explicitly? Because doing so minimizes\n",
+    "surprises. As we will see, `21cmFAST` attempts to cache its results, and can therefore\n",
+    "return the same simulation when the same parameters are given. This is sometimes \n",
+    "surprising, if you were trying to generate multiple realizations of simulations with the \n",
+    "same parameters. Always explicitly specifying the seed requires *you* to take control\n",
+    "of this behavior.\n",
+    "\n",
+    "</div>"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:29.863171Z",
-     "start_time": "2020-02-29T22:10:28.657079Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x288 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "fig, ax = plt.subplots(1,3, figsize=(14,4))\n",
-    "for i, (coeval, redshift) in enumerate(zip([coeval8, coeval9, coeval10], [8,9,10])):\n",
-    "    plotting.coeval_sliceplot(coeval, ax=ax[i], fig=fig);\n",
-    "    plt.title(\"z = %s\"%redshift)\n",
-    "plt.tight_layout()"
+    "#### Specifying the evolution"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Any 3D field can be plotted, by setting the `kind` argument. For example, we could alternatively have plotted the dark matter density cubes perturbed to each redshift:"
+    "While the simulations we will perform in this tutorial will not require any evolution\n",
+    "over cosmic time, there are several situations in which this is required:\n",
+    "\n",
+    "1. The desired output is a lightcone that evolves over redshift.\n",
+    "2. The input options (generally toggled via parameters in `InputParameters.flag_options`)\n",
+    "   specify physical processes that can only be computed by integrating over cosmic \n",
+    "   history. For example, computing spin temperature fluctuations or using a population\n",
+    "   of mini-halos will require this.\n",
+    "\n",
+    "To check if your simulation parameters *require* evolution, you can do:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:30.842329Z",
-     "start_time": "2020-02-29T22:10:29.864621Z"
-    }
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x288 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1,3, figsize=(14,4))\n",
-    "for i, (coeval, redshift) in enumerate(zip([coeval8, coeval9, coeval10], [8,9,10])):\n",
-    "    plotting.coeval_sliceplot(coeval, kind='density', ax=ax[i], fig=fig);\n",
-    "    plt.title(\"z = %s\"%redshift)\n",
-    "plt.tight_layout()"
+    "print(inputs.evolution_required)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To see more options for the plotting routines, see the [API Documentation](../reference/_autosummary/py21cmfast.plotting.html)."
+    "Models that _do_ require evolution also require a specification for which redshifts to\n",
+    "use when scrolling through cosmic history. These will be set by default when using such\n",
+    "a model, for example:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "`Coeval` instances are not cached themselves -- they are containers for data that is itself cached (i.e. each of the `_struct` attributes of `Coeval`). See the [api docs](../reference/_autosummary/py21cmfast.outputs.html) for more detailed information on these. \n",
-    "\n",
-    "You can see the filename of each of these structs (or the filename it would have if it were cached -- you can opt to *not* write out any given dataset):"
+    "inputs_with_evolution = p21c.InputParameters.from_template(\"Munoz21\", random_seed=123)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:30.849484Z",
-     "start_time": "2020-02-29T22:10:30.844024Z"
-    }
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'InitialConditions_6f0eb48c62c36acef23416d5d0fbcf3b_r12345.h5'"
+       "True"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "coeval8.init_struct.filename"
+    "inputs_with_evolution.evolution_required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of redshift nodes:  88\n",
+      "Highest redshift:  35.40225603367622\n",
+      "Lowest redshift:  5.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of redshift nodes: \", len(inputs_with_evolution.node_redshifts))\n",
+    "print(\"Highest redshift: \", max(inputs_with_evolution.node_redshifts))\n",
+    "print(\"Lowest redshift: \" , min(inputs_with_evolution.node_redshifts))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also write the struct anywhere you'd like on the filesystem. This will not be able to be automatically used as a cache, but it could be useful for sharing files with colleagues."
+    "You can specify these \"node\" redshifts yourself:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:31.199972Z",
-     "start_time": "2020-02-29T22:10:30.851552Z"
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35.0, 25.0, 15.0, 10.0, 7.0, 6.0, 5.0)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "coeval8.init_struct.save(fname='my_init_struct.h5')"
+    "p21c.InputParameters.from_template(\n",
+    "    'Munoz21', random_seed=123, node_redshifts=(5,6,7,10, 15, 25, 35)\n",
+    ").node_redshifts"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This brief example covers most of the basic usage of `21cmFAST` (at least with `Coeval` objects -- there are also `Lightcone` objects for which there is a separate tutorial). \n",
-    "\n",
-    "For the rest of the tutorial, we'll cover a more advanced usage, in which each step of the calculation is done independently."
+    "When doing so, remember that accuracy is decreased when fewer nodes are used, especially\n",
+    "during times when the universe is changing more rapidly (e.g. throughout reionization).\n",
+    "Also keep in mind that the redshifts must _at least_ extend to `Z_HEAT_MAX`: a parameter\n",
+    "that defines the redshift beyond which background fluctuations are considered negligible.\n",
+    "An error will be raised if this is not respected."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Advanced Step-by-Step Usage"
+    "Unless you have a strong reason to set your own node redshifts, the best way to manipulate\n",
+    "them is to modify the `Z_HEAT_MAX` and `ZPRIME_STEP_FACTOR` parameters, which modify the\n",
+    "default behaviour:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of redshift nodes:  141\n",
+      "Highest redshift:  25.176144908768336\n",
+      "Lowest redshift:  5.5\n"
+     ]
+    }
+   ],
    "source": [
-    "Most users most of the time will want to use the high-level `run_coeval` function from the previous section. However, there are several independent steps when computing the brightness temperature field, and these can be performed one-by-one, adding any other effects between them if desired. This means that the new `21cmFAST` is much more flexible. In this section, we'll go through in more detail how to use the lower-level methods.\n",
-    "\n",
-    "Each step in the chain will receive a number of input-parameter classes which define how the calculation should run. These are the `user_params`, `cosmo_params`, `astro_params` and `flag_options` that we saw in the previous section.\n",
-    "\n",
-    "Conversely, each step is performed by running a function which will return a single object. Every major function returns an object of the same fundamental class (an ``OutputStruct``) which has various methods for reading/writing the data, and ensuring that it's in the right state to receive/pass to and from C.\n",
-    "These are the objects stored as `init_box_struct` etc. in the `Coeval` class.\n",
+    "finer_zgrid = p21c.InputParameters(\n",
+    "    random_seed=123, \n",
+    "    flag_options={\"USE_TS_FLUCT\": True}, \n",
+    "    user_params={\"Z_HEAT_MAX\": 25.0, \"ZPRIME_STEP_FACTOR\": 1.01}\n",
+    ")\n",
     "\n",
-    "As we move through each step, we'll outline some extra details, hints and tips about using these inputs and outputs."
+    "print(\"Number of redshift nodes: \", len(finer_zgrid.node_redshifts))\n",
+    "print(\"Highest redshift: \", max(finer_zgrid.node_redshifts))\n",
+    "print(\"Lowest redshift: \" , min(finer_zgrid.node_redshifts))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initial Conditions"
+    "## Run a simulation using `run_coeval`"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first step is to get the initial conditions, which defines the *cosmological* density field before any redshift evolution is applied."
+    "The simplest (and typically most efficient) way to produce a coeval cube is simply to \n",
+    "use the `run_coeval` method. This consistently performs all steps of the calculation, \n",
+    "re-using any data that it can without re-computation or increased memory overhead.\n",
+    "\n",
+    "The `run_coeval` method is a generator, yielding a `Coeval` object at each redshift\n",
+    "that is requested, or required for calculating the evolutionary history."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 37,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:41.882249Z",
-     "start_time": "2020-02-29T22:10:31.202726Z"
+     "end_time": "2020-02-29T22:10:27.413255Z",
+     "start_time": "2020-02-29T22:10:11.369635Z"
     }
    },
    "outputs": [],
    "source": [
-    "initial_conditions = p21c.initial_conditions(\n",
-    "    user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100},\n",
-    "    cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n",
-    "    random_seed=54321\n",
-    ")"
+    "coevals = []\n",
+    "for coeval in p21c.run_coeval(\n",
+    "    inputs=inputs, \n",
+    "    out_redshifts=[8, 9, 10],\n",
+    "    cache=cache,\n",
+    "):\n",
+    "    coevals.append(coeval)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We've already come across all these parameters as inputs to the `run_coeval` function. Indeed, most of the steps have very similar interfaces, and are able to take a random seed and parameters for where to look for the cache. We use a different seed than in the previous section so that all our boxes are \"fresh\" (we'll show how the caching works in a later section).\n",
+    "There are a number of possible inputs for `run_coeval`, which you can check out either in the [API reference](../reference/py21cmfast.html) or by calling `help(p21c.run_coeval)`. \n",
+    "\n",
+    "Notably, in our case here, the `out_redshifts` must be given: these are the redshifts\n",
+    "at which we compute the coeval boxes. When the simulation requires evolution over \n",
+    "cosmic history, coeval boxes will be computed at every node _as well as_ any \n",
+    "`out_redshifts` specified. \n",
     "\n",
-    "These initial conditions have 100 cells per side, and a box length of 100 Mpc. Note again that they can either be passed as a dictionary containing the input parameters, or an actual instance of the class. While the former is the suggested way, one benefit of the latter is that it can be queried for the relevant parameters (by using ``help`` or a post-fixed ``?``), or even queried for defaults:"
+    "Since the function is a generator, it yields a `Coeval` box on each iteration. These\n",
+    "boxes can be saved or post-processed before completing the next iteration, allowing\n",
+    "arbitrarily flexible computations to be performed.\n",
+    "\n",
+    "We've also given a `cache` parameter: this specifies a directory in which the various\n",
+    "component calculations of this simulation will be cached. By default, this will occur\n",
+    "in the current directory, but here we are using a temporary directory for convenience.\n",
+    "While by default all sub-components are written into the cache, this can be turned off\n",
+    "altogether, or toggled per subcomponent. The main reason to use the cache is if you \n",
+    "might want to re-use simulated component boxes in other simulations in the future.\n",
+    "A prime example of this is the `InitialConditions`, for which a single simulation can\n",
+    "be used for a multitude of astrophysical simulations with varying `AstroParams`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output on each iteration is, as we have mentioned, a `Coeval` object. This is a simple\n",
+    "object that contains a number of 3D arrays of data, one for each of the component fields\n",
+    "simulated. They can be accessed as attributes of the object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:41.887141Z",
-     "start_time": "2020-02-29T22:10:41.883585Z"
-    }
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'SIGMA_8': 0.8102,\n",
-       " 'hlittle': 0.6766,\n",
-       " 'OMm': 0.30964144154550644,\n",
-       " 'OMb': 0.04897468161869667,\n",
-       " 'POWER_INDEX': 0.9665}"
+       "10.0"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "p21c.CosmoParams._defaults_"
+    "coevals[0].redshift"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 43,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of Brightness Temperature Box:  (100, 100, 100)\n",
+      "Mean brightness temperature (mK):  24.368357\n"
+     ]
+    }
+   ],
    "source": [
-    "(these defaults correspond to the Planck15 cosmology contained in Astropy)."
+    "print(\"Shape of Brightness Temperature Box: \", coevals[0].brightness_temp.shape)\n",
+    "print(\"Mean brightness temperature (mK): \", coevals[0].brightness_temp.mean())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So what is in the ``initial_conditions`` object? It is what we call an ``OutputStruct``, and we have seen it before, as the `init_box_struct` attribute of `Coeval`. It contains a number of arrays specifying the density and velocity fields of our initial conditions, as well as the defining parameters. For example, we can easily show the cosmology parameters that are used (note the non-default $\\sigma_8$ that we passed):"
+    "A list of all simulated fields can be accessed:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:42.368956Z",
-     "start_time": "2020-02-29T22:10:41.888436Z"
-    }
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "CosmoParams(OMb:0.04897468161869667, OMm:0.30964144154550644, POWER_INDEX:0.9665, SIGMA_8:0.8, hlittle:0.6766)"
+       "['lowres_density',\n",
+       " 'lowres_vx',\n",
+       " 'lowres_vy',\n",
+       " 'lowres_vz',\n",
+       " 'hires_density',\n",
+       " 'hires_vx',\n",
+       " 'hires_vy',\n",
+       " 'hires_vz',\n",
+       " 'lowres_vx_2LPT',\n",
+       " 'lowres_vy_2LPT',\n",
+       " 'lowres_vz_2LPT',\n",
+       " 'hires_vx_2LPT',\n",
+       " 'hires_vy_2LPT',\n",
+       " 'hires_vz_2LPT',\n",
+       " 'lowres_vcb',\n",
+       " 'density',\n",
+       " 'velocity_z',\n",
+       " 'velocity_x',\n",
+       " 'velocity_y',\n",
+       " 'xH_box',\n",
+       " 'Gamma12_box',\n",
+       " 'MFP_box',\n",
+       " 'z_re_box',\n",
+       " 'dNrec_box',\n",
+       " 'temp_kinetic_all_gas',\n",
+       " 'Fcoll',\n",
+       " 'Fcoll_MINI',\n",
+       " 'brightness_temp',\n",
+       " 'Ts_box',\n",
+       " 'x_e_box',\n",
+       " 'Tk_box',\n",
+       " 'J_21_LW_box',\n",
+       " 'halo_mass',\n",
+       " 'halo_stars',\n",
+       " 'halo_stars_mini',\n",
+       " 'count',\n",
+       " 'halo_sfr',\n",
+       " 'halo_sfr_mini',\n",
+       " 'halo_xray',\n",
+       " 'n_ion',\n",
+       " 'whalo_sfr']"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "initial_conditions.cosmo_params"
+    "coevals[0].get_fields()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A handy tip is that the ``CosmoParams`` class also has a reference to a corresponding Astropy cosmology, which can be used more broadly:"
+    "The `Coeval` object also maintains a reference to the input parameters:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:42.528736Z",
-     "start_time": "2020-02-29T22:10:42.374015Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "FlatLambdaCDM(name=\"Planck15\", H0=67.7 km / (Mpc s), Om0=0.31, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.049)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initial_conditions.cosmo_params.cosmo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 45,
    "metadata": {},
-   "source": [
-    "Merely printing the initial conditions object gives a useful representation of its dependent parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:42.821922Z",
-     "start_time": "2020-02-29T22:10:42.535714Z"
-    }
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "InitialConditions(UserParams(BOX_LEN:100, DIM:300, HII_DIM:100, HMF:1, POWER_SPECTRUM:0, USE_FFTW_WISDOM:False, USE_RELATIVE_VELOCITIES:False);\n",
-      "\tCosmoParams(OMb:0.04897468161869667, OMm:0.30964144154550644, POWER_INDEX:0.9665, SIGMA_8:0.8, hlittle:0.6766);\n",
-      "\trandom_seed:54321)\n"
+      "cosmo_params: CosmoParams(SIGMA_8=0.8102, hlittle=0.6766, OMm=0.30964144154550644, OMb=0.04897468161869667, POWER_INDEX=0.9665)\n",
+      "user_params: UserParams(BOX_LEN=100.0, HII_DIM=100, DIM=200, NON_CUBIC_FACTOR=1.0, USE_FFTW_WISDOM=False, HMF='ST', USE_RELATIVE_VELOCITIES=False, POWER_SPECTRUM='EH', N_THREADS=1, PERTURB_ON_HIGH_RES=False, NO_RNG=False, USE_INTERPOLATION_TABLES=True, INTEGRATION_METHOD_ATOMIC='GAUSS-LEGENDRE', INTEGRATION_METHOD_MINI='GAUSS-LEGENDRE', USE_2LPT=True, MINIMIZE_MEMORY=False, KEEP_3D_VELOCITIES=False, SAMPLER_MIN_MASS=100000000.0, SAMPLER_BUFFER_FACTOR=2.0, MAXHALO_FACTOR=2.0, N_COND_INTERP=200, N_PROB_INTERP=400, MIN_LOGPROB=-12.0, SAMPLE_METHOD='MASS-LIMITED', AVG_BELOW_SAMPLER=True, HALOMASS_CORRECTION=0.9, PARKINSON_G0=1.0, PARKINSON_y1=0.0, PARKINSON_y2=0.0, Z_HEAT_MAX=35.0, ZPRIME_STEP_FACTOR=1.02)\n",
+      "astro_params: AstroParams(HII_EFF_FACTOR=30.0, F_STAR10=-1.3, ALPHA_STAR=0.5, F_STAR7_MINI=-2.8, ALPHA_STAR_MINI=0.5, F_ESC10=-1.0, ALPHA_ESC=-0.5, F_ESC7_MINI=-2.0, M_TURN=8.7, R_BUBBLE_MAX=15.0, ION_Tvir_MIN=4.69897, L_X=40.5, L_X_MINI=40.5, NU_X_THRESH=500.0, X_RAY_SPEC_INDEX=1.0, X_RAY_Tvir_MIN=4.69897, F_H2_SHIELD=0.0, t_STAR=0.5, N_RSD_STEPS=20, A_LW=2.0, BETA_LW=0.6, A_VCB=1.0, BETA_VCB=1.8, UPPER_STELLAR_TURNOVER_MASS=11.447, UPPER_STELLAR_TURNOVER_INDEX=-0.6, SIGMA_STAR=0.25, SIGMA_LX=0.5, SIGMA_SFR_LIM=0.19, SIGMA_SFR_INDEX=-0.12, CORR_STAR=0.5, CORR_SFR=0.2, CORR_LX=0.2)\n",
+      "flag_options: FlagOptions(USE_MINI_HALOS=False, USE_CMB_HEATING=True, USE_LYA_HEATING=True, USE_MASS_DEPENDENT_ZETA=True, USE_HALO_FIELD=False, APPLY_RSDS=True, SUBCELL_RSD=False, INHOMO_RECO=False, USE_TS_FLUCT=False, FIX_VCB_AVG=False, HALO_STOCHASTICITY=False, USE_EXP_FILTER=False, FIXED_HALO_GRIDS=False, CELL_RECOMB=False, PHOTON_CONS_TYPE='no-photoncons', USE_UPPER_STELLAR_TURNOVER=True, M_MIN_in_Mass=True, HALO_SCALING_RELATIONS_MEDIAN=False)\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "print(initial_conditions)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(side-note: the string representation of the object is used to uniquely define it in order to save it to the cache... which we'll explore soon!).\n",
-    "\n",
-    "To see which arrays are defined in the object, access the ``fieldnames`` (this is true for *all* `OutputStruct` objects):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:42.840338Z",
-     "start_time": "2020-02-29T22:10:42.828276Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lowres_density',\n",
-       " 'lowres_vx',\n",
-       " 'lowres_vy',\n",
-       " 'lowres_vz',\n",
-       " 'lowres_vx_2LPT',\n",
-       " 'lowres_vy_2LPT',\n",
-       " 'lowres_vz_2LPT',\n",
-       " 'hires_density',\n",
-       " 'lowres_vcb',\n",
-       " 'hires_vcb']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initial_conditions.fieldnames"
+    "print(coevals[0].inputs)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `coeval_sliceplot` function also works on `OutputStruct` objects (as well as the `Coeval` object as we've already seen). It takes the object, and a specific field name. By default, the field it plots is the _first_ field in `fieldnames` (for any `OutputStruct`)."
+    "We can also save/load a Coeval object to disk:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 46,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:43.078040Z",
-     "start_time": "2020-02-29T22:10:42.845666Z"
+     "end_time": "2020-02-29T22:10:27.644169Z",
+     "start_time": "2020-02-29T22:10:27.442997Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotting.coeval_sliceplot(initial_conditions, \"hires_density\");"
+    "coevals[0].save(cache.direc / \"coeval.h5\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Perturbed Field"
+    "Note that, if you are also using the cache (as we are), then saving the `Coeval` object\n",
+    "creates redundant data on disk. The files created by `Coeval` are more convenient for \n",
+    "saving, sharing and using in further post-processing, but are not easily used for \n",
+    "caching purposes."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "After obtaining the initial conditions, we need to *perturb* the field to a given redshift (i.e. the redshift we care about). This step clearly requires the results of the previous step, which we can easily just pass in. Let's do that:"
+    "The file that we wrote can be easily read:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:43.461429Z",
-     "start_time": "2020-02-29T22:10:43.079489Z"
-    }
-   },
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "perturbed_field = p21c.perturb_field(\n",
-    "    redshift = 8.0,\n",
-    "    init_boxes = initial_conditions\n",
-    ")"
+    "coeval10 = p21c.Coeval.from_file(cache.direc / 'coeval.h5')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that we didn't need to pass in any input parameters, because they are all contained in the `initial_conditions` object itself. The random seed is also taken from this object.\n",
-    "\n",
-    "Again, the output is an `OutputStruct`, so we can view its fields:"
+    "Some convenient plotting functions exist in the `plotting` module. These can work directly on `Coeval` objects, or any of the output structs (as we'll see further on in the tutorial). By default the `coeval_sliceplot` function will plot the `brightness_temp`, using the standard traditional colormap:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 48,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:43.467710Z",
-     "start_time": "2020-02-29T22:10:43.464305Z"
+     "end_time": "2020-02-29T22:10:29.863171Z",
+     "start_time": "2020-02-29T22:10:28.657079Z"
     }
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "['density', 'velocity']"
+       "<Figure size 1400x400 with 6 Axes>"
       ]
      },
-     "execution_count": 23,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "perturbed_field.fieldnames"
+    "fig, ax = plt.subplots(1,3, figsize=(14,4))\n",
+    "for i, coeval in enumerate(coevals):\n",
+    "    plotting.coeval_sliceplot(coeval, ax=ax[i], fig=fig);\n",
+    "    plt.title(f\"z = {coeval.redshift}\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This time, it has only density and velocity (the velocity direction is chosen without loss of generality). Let's view the perturbed density field:"
+    "Any 3D field can be plotted, by setting the `kind` argument. For example, we could alternatively have plotted the dark matter density cubes perturbed to each redshift:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 49,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:43.658615Z",
-     "start_time": "2020-02-29T22:10:43.469373Z"
+     "end_time": "2020-02-29T22:10:30.842329Z",
+     "start_time": "2020-02-29T22:10:29.864621Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 1400x400 with 6 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plotting.coeval_sliceplot(perturbed_field, \"density\");"
+    "fig, ax = plt.subplots(1,3, figsize=(14,4))\n",
+    "for i, coeval in enumerate(coevals):\n",
+    "    plotting.coeval_sliceplot(coeval, kind='density', ax=ax[i], fig=fig);\n",
+    "    plt.title(f\"z = {coeval.redshift}\")\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is clear here that the density used is the *low*-res density, but the overall structure of the field looks very similar."
+    "To see more options for the plotting routines, see the [API Documentation](../reference/_autosummary/py21cmfast.plotting.html)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Ionization Field"
+    "This brief example covers most of the basic usage of `21cmFAST` (at least with `Coeval` objects -- there are also `Lightcone` objects for which there is a separate tutorial). \n",
+    "\n",
+    "For the rest of the tutorial, we'll cover a more advanced usage, in which each step of the calculation is done independently."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we need to ionize the box. This is where things get a little more tricky. In the simplest case (which, let's be clear, is what we're going to do here) the ionization occurs at the *saturated limit*, which means we can safely ignore the contribution of the spin temperature. This means we can directly calculate the ionization on the density/velocity fields that we already have. A few more parameters are needed here, and so two more \"input parameter dictionaries\" are available, ``astro_params`` and ``flag_options``. Again, a reminder that their parameters can be viewed by using eg. `help(p21c.AstroParams)`, or by looking at the [API docs](../reference/_autosummary/py21cmfast.inputs.html)."
+    "## Advanced Step-by-Step Usage"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For now, let's leave everything as default. In that case, we can just do:"
+    "Most users most of the time will want to use the high-level `run_coeval` function from the previous section. However, there are several independent steps when computing the brightness temperature field, and these can be performed one-by-one, adding any other effects between them if desired. In this section, we'll go through in more detail how to use the lower-level methods.\n",
+    "\n",
+    "Each step is performed by running a function which will return a single object. \n",
+    "Every major function returns an object of the same fundamental class (an ``OutputStruct``) \n",
+    "which has methods to share its underlying data with the backend C code, and maintain \n",
+    "knowledge of the state of its memory.\n",
+    "\n",
+    "As we move through each step, we'll outline some extra details, hints and tips about using these inputs and outputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first thing to do is to set up the input parameters. Here, we'll use the same\n",
+    "basic set of parameters as we used in `run_coeval`, but with a different random seed, \n",
+    "which ensures that we compute all new boxes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:44.909517Z",
-     "start_time": "2020-02-29T22:10:43.659834Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-02-29 15:10:43,902 | INFO | Existing init_boxes found and read in (seed=54321).\n"
-     ]
-    }
-   ],
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "ionized_field = p21c.ionize_box(\n",
-    "    perturbed_field = perturbed_field\n",
-    ")"
+    "new_inputs = inputs.clone(random_seed=42)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That was easy! All the information required by ``ionize_box`` was given directly by the ``perturbed_field`` object. If we had _also_ passed a redshift explicitly, this redshift would be checked against that from the ``perturbed_field`` and an error raised if they were incompatible:"
+    "### Initial Conditions"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see the fieldnames:"
+    "The first step is to get the initial conditions, which defines the *cosmological* density field before any redshift evolution is applied."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 63,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:44.914928Z",
-     "start_time": "2020-02-29T22:10:44.911001Z"
+     "end_time": "2020-02-29T22:10:41.882249Z",
+     "start_time": "2020-02-29T22:10:31.202726Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['first_box', 'xH_box', 'Gamma12_box', 'z_re_box', 'dNrec_box']"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ionized_field.fieldnames"
+    "initial_conditions = p21c.compute_initial_conditions(inputs=new_inputs, cache=cache, write=True)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here the ``first_box`` field is actually just a flag to tell the C code whether this has been *evolved* or not. Here, it hasn't been, it's the \"first box\" of an evolutionary chain. Let's plot the neutral fraction:"
+    "As we said, all of these \"single field\" functions return objects of type `OutputStruct`.\n",
+    "These objects have a few things in common. For example, they hold a reference to their\n",
+    "input parameters:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:45.120787Z",
-     "start_time": "2020-02-29T22:10:44.916354Z"
-    }
-   },
+   "execution_count": 64,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "100"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plotting.coeval_sliceplot(ionized_field, \"xH_box\");"
+    "initial_conditions.inputs.user_params.HII_DIM"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Brightness Temperature"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use what we have to get the brightness temperature:"
+    "More importantly, once computed, they hold the simulated data. This data is held in \n",
+    "special `Array` objects, and all the available simulated fields can be accessed through\n",
+    "the `.arrays` attribute:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:45.153348Z",
-     "start_time": "2020-02-29T22:10:45.122062Z"
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['lowres_density', 'lowres_vx', 'lowres_vy', 'lowres_vz', 'hires_density', 'hires_vx', 'hires_vy', 'hires_vz', 'lowres_vx_2LPT', 'lowres_vy_2LPT', 'lowres_vz_2LPT', 'hires_vx_2LPT', 'hires_vy_2LPT', 'hires_vz_2LPT'])"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "brightness_temp = p21c.brightness_temperature(ionized_box=ionized_field, perturbed_field=perturbed_field)"
+    "initial_conditions.arrays.keys()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This has only a single field, ``brightness_temp``:"
+    "As we mentioned, the values of these attributes are _not_ numpy arrays, but custom\n",
+    "`Array` objects. These have the ability to track their state: whether they initialized,\n",
+    "computed, stored on disk, etc., and they also know how to share themselves with the \n",
+    "C backend. While this is useful for the simulation, it is not that helpful to _you_ as \n",
+    "a user. That's OK: these objects aren't really meant to be the primary way you interact\n",
+    "with `21cmFAST` as a user. \n",
+    "\n",
+    "However, to get at the underlying numpy array, you can use the `.get()` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:45.355868Z",
-     "start_time": "2020-02-29T22:10:45.155043Z"
-    }
-   },
+   "execution_count": 66,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "0.0001017182"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plotting.coeval_sliceplot(brightness_temp);"
+    "initial_conditions.get(\"lowres_density\").mean()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The Problem"
+    "This has the handy feature that even if the array has been purged from memory but stored\n",
+    "on disk, the `.get()` method will still return the array (it will read it from disk,\n",
+    "and store it back on the object until you tell the object to purge it again).\n",
+    "\n",
+    "For example:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "And there you have it -- you've computed each of the four steps (there's actually another, `spin_temperature`, that you require if you don't assume the saturated limit) individually. \n",
-    "\n",
-    "However, some problems quickly arise. What if you want the `perturb_field`, but don't care about the initial conditions? We know how to get the full `Coeval` object in one go, but it would seem that the sub-boxes have to _each_ be computed as the input to the next. \n",
-    "\n",
-    "A perhaps more interesting problem is that some quantities require *evolution*: i.e. a whole bunch of simulations at a string of redshifts must be performed in order to obtain the current redshift. This is true when not in the saturated limit, for example. That means you'd have to manually compute each redshift in turn, and pass it to the computation at the next redshift. While this is definitely possible, it becomes difficult to set up manually when all you care about is the box at the final redshift.\n",
-    "\n",
-    "`py21cmfast` solves this by making each of the functions recursive: if `perturb_field` is not passed the `init_boxes` that it needs, it will go and compute them, based on the parameters that you've passed it. If the previous `spin_temp` box required for the current redshift is not passed -- it will be computed (and if it doesn't have a previous `spin_temp` *it* will be computed, and so on).\n",
-    "\n",
-    "That's all good, but what if you now want to compute another `perturb_field`, with the same fundamental parameters (but at a different redshift)? Since you didn't ever see the `init_boxes`, they'll have to be computed all over again. That's where the automatic caching comes in, which is where we turn now..."
+    "initial_conditions.purge()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Using the Automatic Cache"
+    "Once purged, the array's state shows that it is not initialized or in memory, but it\n",
+    "is stored on disk (because we passed `write=True` when computing it):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ArrayState(initialized=False, c_memory=False, computed_in_mem=False, on_disk=True)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initial_conditions.lowres_density.state"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To solve all this, ``21cmFAST`` uses an on-disk caching mechanism, where all boxes are saved in HDF5 format in a default location. The cache allows for reading in previously-calculated boxes automatically if they match the parameters that are input. The functions used at every step (in the previous section) will try to use a cached box instead of calculating a new one, unless its explicitly asked *not* to. \n",
-    "\n",
-    "Thus, we could do this:"
+    "However, when we use `.get()`, the array is re-loaded:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:45.559944Z",
-     "start_time": "2020-02-29T22:10:45.357189Z"
-    }
-   },
+   "execution_count": 70,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-02-29 15:10:45,367 | INFO | Existing z=8.0 perturb_field boxes found and read in (seed=12345).\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "0.0001017182"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "perturbed_field = p21c.perturb_field(\n",
-    "    redshift = 8.0,\n",
-    "    user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100},\n",
-    "    cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n",
-    ")\n",
-    "plotting.coeval_sliceplot(perturbed_field, \"density\");"
+    "initial_conditions.get(\"lowres_density\").mean()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that here we pass exactly the same parameters as were used in the previous section. It gives a message that the full box was found in the cache and immediately returns. However, if we change the redshift:"
+    "The `coeval_sliceplot` function also works on `OutputStruct` objects (as well as the `Coeval` object as we've already seen). It takes the object, and a specific field name. By default, the field it plots is the _first_ field in `arrays` (for any `OutputStruct`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 72,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:46.331135Z",
-     "start_time": "2020-02-29T22:10:45.561345Z"
+     "end_time": "2020-02-29T22:10:43.078040Z",
+     "start_time": "2020-02-29T22:10:42.845666Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-02-29 15:10:45,748 | INFO | Existing init_boxes found and read in (seed=12345).\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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2J4baE8dwpmVAoFfQ0allFUPr05+IkHz1jebsHZayLJeFgQqQhtRFp+CSTkGIjNtky3SG91NUpcnp65jw3CNdpD0Znb2DWpFQvVVJ3A3RMFIdeynX0sGt5U0IXEijP6ER6LOwnOVSvfmqObobCbVk5fW2pNucsKXJyMs2lhsHbZtNko2nU8oAlzPdQ6RKUPI6O8egQZKOtYUsqya47MwaxwptAkBXtA1RWDgeQ+eZ1J1ISMuUrN89ZQn4WjZ3Jxik2IjXefwFLo+VSM4gQhh3uS8Sddre8QOFbURMbl5ioGcj0XDTvSv9NaaeBRoJ3/de+2jYZ3PSxnv4BRtDqSf7Yd5PlxIpeFJTctJQdGyJAvR4zWafjdfh1O9wG7pqsFXl+7as7bdeg7thVRHgyiqr7K5ajqzv363MOTfhnPuIc+5F59wLzrlvudn2DzYyq9WQjY8FYmoKZpUWEja9SCTAohxS+BTpEgAGZm0KkkO7cY2O4RaTvaejM1mExfq1UlK1JGhU/CRJ+naTpEYQZflQqcguQ+2SpQMNXk1ScETOJSILcjv8PiWuKnChbV2XjudrFqoPydcJxSSQJ8vJwEI0CfIbOmeze3fCxnb9lDmNNyatNzfeYG2MXI5jWqcGvac8UzZH4i0d0b3DkaCaMbFcKGjjKAMKrEbUKhGSAaBxkTQEJYSrWIwoJuOxL/kkU9kWRf2gw5z0kHC9RyMyVsWmzUMksw5acZXaPIMpCuIk5GK3IYIzqTK8J1JV3XDOSgnbhjYjyodSlQAgWyPyJp1G46VzDk5+JCsRyR0NF/dVQKm+HK/z/DP23aE50jW2Zpzdlt1BAOCfAfio9/6HnXNNAEM32/jBfphVVlll+956/vYXgM65MQDfDuBvAID3vg2gfbN9HuyHWd6DX1qOlYSS1BWlsYgUGMT6SJ7N6OPI0pmQ6KT7jCUce5U2Y8pQ7Xoik6OUIfqagi696igG2aBEaHGtqKsfJVw4g9OPl5JzFVIPofVScnea7K12e0R+GX1yNVWdCvUVEz+VSr4J3dLnt/mozcoq2QcAdaIRzeKtc9bfxpIh2QEmQw9cjGhVBGChlNWnjP7QWLbjtF6JKTNK+lc1rCZJp9km05uIopV2BiT+pwbRj5L3hU4SxCnCMV61Eq2hSql8i2wjW0jQ9gHSTogOhciC307XOR1T0ls2T5vvTD8yUUNcnzSzgN6y0g8/1DKIqE7+Xn+UAgWqPEaE2Z1OZK1K1zWk6CkhnygyrcvZaxb9gXdq+TbILK2BS3uOpSQB4DEYJvwl59ybADwP4H3e+1VsY5XPrLLKKrur1vb1vn/e++e8988mf88lu9UBvBXAz3vv3wJgFcD7b3acBxuZNZvwjx6Dmyc66ldVJysWlK2RSKoqNUHcDwipQb0WJVUCOZFIaSNBuSoMLGQkBKCIoiJB61v9JGEfpbCozqKQ02BS6acmUmYxWhcEBtO6n4pMboliqtp6t3juQKwILmFFHqf16vXC8YCI3uTb8xPmz2lcJRF5hX6Y5cSPJwTLvoxcM3QbEqxTn6LGl/2V7JGbUXoZiZ3J+AQisCPyqLcKfU0lG2pKG5OEkCeK1vEmiiKTQPRzNZXFxCLPEoEMtThToirvheYXiaCOE0GJLJ2mfelaqdbomNLt+HmQUo/3aZByEsoiIusNEVl24pgKpTUoWSQhUKFEJfMPXYkI9to32/4HP1+SON+l7cTZ38cuArjovf8M338Et3iYVcisssoqu6vW867v383Me38FwAXn3NP86LsAfPVm+zzYyMx7uM1u4Cr5taTepGYvCtnVGMVUdKt22aJgPkmkFudGUsZKxQkCjCvJcl1y1qdNrK+2SHkXppZ4SQ0laS4hzUjJyUJmQ0ITinpFP55m8YCm5HeRTFDifwmRT0lHi6skueypSR4vQX7y+UmEUX0i4mtT+gYAuqyxOcwUnlAUo2TiPQExahwKsUgsU37OVKJaIobcP/g9Jaa4JO5YctsK2SmKrLQsosMs7aMr/oACumUKUW9SxWhStMhXXdesVjxuvc9PSCltjCQq3U7oqiCLrvGWP1VjN8v7k8he1y5t1/G6usPmmxMiUzEfIMplBxkiIXCJKRABDl6P53zwM7bP2pEiI2C3dgcZAD8O4JcZyTyDW9TTfbAfZpVVVtm+t3wX0UwA8N5/HsCzO92+ephVVllld9Wq3MydWi2LYe1O4hgeLqoPuJIKgVQt3KWoT5UfoUP7GpeTC6rfSOpEWuNRr6IuqH2F1rXUa/bRhOKyOKczWSlQoia45a3LWS03QqBBx0sdw1y+5OukJzBtKecSMtMSKV1mapkkh7OOQ2JqfSEuebNBbqMULemlaUzpbHY+tu+oxe9Vd0DLPip5iPpgx2Z7VOdQzcuQfqWxrW1Nj9H4OBGStaydT5Q2HjXCa1BFkcadlmlc3vu0boCoDUtS2S3xNnUdDkU9MDn6M7o2dF6hIlJKWpZrQEomCoLwvhEB2ifk6KDoq2Pf4HKW6WC9I3FM25PWzspR++7Qp0igZo1NVaQanIn3XGue/dvGjXC71rlH6UwP/sOsssoq29e2G9LsbuzBfpjlHm5tA546Vz5LHLxCH3L6lmYZkThDMi2AlWOsIcnK5VmpVmIh1aRG7XRVAQph8mIdzZQ6oZk5OPGpux/C+nL6pkTbUnqLUIq2yZIAhtCoU5oLAwpqTWku+aUrYR/RHUJ6S7s4i2bno/ZW6JUc2kRVPSqftid4rpPxnIdUo2B8pHjuom8kNTyFfEPqVkh+V1I96TAJchLyDUEPpu3kIksnif6OKWa905awntVP8pyJ4uZK9SiBiFj1qushSobGfD25TkKJQpC8B6RzVqjqXqL0hPuItBfRVQqhC9VIYDAl0I6eeNSOkwQAGss2lkMkwq4+bmlTgzPcZ4bIMK2FQRJu7freVDTfjjS71/ZgP8wqq6yyfW8VMtuppVXGE4JqjTOakFI+xdA0Zy2RIVP/yOg5qn2u0nczWFQ8LZByhZ5EC6EvJaTT9NNPV2heM7QqmZe0810a7tfMHZLEiUQU0k99KaJXcP9QgYoKqr0R1tNsR/Jv0MUnautSwTakuawmfZPPShWcBpjuRRqKPGWhZmiyj9KaQgUp+QL70DjKpnNWClpKtA3XvFGUPxIi9sm5hkpd6h+v/eZx821lm/SjJii+fpbpVhpvXmf5aTU+gTYCYPPNVhuh12QF+/P0vaofabqaVgii42hsKSlVWypJDiEhCtNHWtNYklzsn3k0bNsZsXMcumT97I7Ytqp0PrBhvrPuaKRhKOG+QOG5A6sCAJVVVtlDYZU44w6sN1TH8hsPY/gcE5QvJknLg0UiqmzpGfMZjH59GWVT1aEQrVNyeh+BxewAJVvkG9MMWkpRSo+vWpuKYgqBlasopURMFwjBxbQW3ydNyrPylGphlv2Fbr0oJwQAvkfZm1lWI2ftRdU98Km0Dbsl8UGJSNaJuhqXbxSPC8RosmoxaLZvqO5nsfoUkCAoJci3txdLCGg5pKcxUsxoacYIZrpNIAirXiYrH9VJfE6rYwXTeFNiKBOyZ9TXJb7X5hzbkYAn74180rZJKy01L9PfqxXECBPAl639fGnrfapoZrkGZkix++qZsO2wUsBUoWvRzm3hrUYm74wSlXYTNLqk6vB7U52p4+/NY+aBfphVVlll+9+qgiY7sO6gw/U31DFwjWKEjx0L39XPm//gxtvNfyEBwcaqzUCD9BHUl2MUaulpm6UG5ihP8+fk5Jw0UT93I0oABY5YOWlc0cxFzv6N7YdYfp2a2hAS3Ez8PJvy3zHaKN6R+EnjMd0oDyjRjt1rEXlQrkaILZXNdsMUnDxE7hLrOEqgMEskdELVcM7YNfLBGhIO3NweQXlVY1KKknyKKedK53HU+lKbIYIlqgj9TvcR94++pt5lQ+eq0N2bSKozLRZRoHh99RvkkBHNFZArKzkJWQq1C2HK55UPxX2E8GuXGJ1W2tQG/WAp8lNkkulGOYUWa5sUosRW070nC35U+Qmno+Ble5LRVqXSUba8O2DXcnOclZgW45Fqx4jmyoKdu7TdZgDcrj3QD7PKKqts/9tDjcycc38bwH8Fm3i+BEsgPQrgVwFMAfgsgL9OdcltrdYGRi54rDwyuOW7oSETWGwt2qw4esFmy1Umz3aHSvI+AOrrFDccsJmk/bYnbBvKK7uJeJzGGXK1hMgkmEc/hhBTN9mn5iQdbUimJ3RH3lEUYoynHbhKeSmqqQ0Sf16mgiVEbc0LxWR6yX9niQDg2mOUCF9h9JLJyinvSCZhy8YcE9hPWEGQ7mFDtLU59i3xsylyt37ajjN4hqiCKEiS3gBQP2nIWlLOEkDsHTN2vaSHUp9iqMhNv6MknsTlStFYLIxSLFCjRH/XVuJ/EsVT8nuD/i8djxXUr7/NrvPw1XjOI5eZhTDFDIAZ3hNEpS6piq7x6Y6YTJAyLnTNMo1lKihAtIh5rhQkCCrknfgsl05T7nvFtmkuWTsD89Zua8m2bazE/rfH6JfN9uYhdK+Q2T2XAHLOHQfwtwA8671/PYAagB8B8A8B/BPv/ZMA5gH82L3uW2WVVbb31vG1vn97bfdLz6wOYNA5V4cVKZgB8E6YABsAfAjAu+9T3yqrrLI9tJ7P+v7ttd3zZab3/pJz7h8BOA9gHcB/gOl7L3jvhaUvAjjeb/9UN7zVGsf42Q1k67bb3BtjeHzxW2ypMDhry7OhC9SrHys6nNPl1PBLtuSR87s9TVKinKdJPcWwhFC4v1SzUgqeWaKx5kkyzQ8yvD9cdM4qGb5Q11LpPyLwhtoCVHBNne5aFojQKzqElpckZqYaZQPXqJempd2Zi+wbyaGJE9idOl7sgz7nvr3JYR4v3lb1JaMYhOWl9nF9ljAi2KpGJZdgQVdOzvhE8Vf0lvopo2BIt0t9SmkQy6dsOTkwZ981F6wdBTCC5tdSkcRsG9lxVp6yZezmBNWIN2x8NiYj0hhkOzVVkzpmS8hQxyGtqMWUttY5Gx8t7QJxt48TXpW/nGoAKO2LdQLctTjW05/h/UcKidKZVg/b+xrjX4NXEsL5OnXREsXaO7F7xTO7H8vMSQA/COA0gGMAhgF8X59N+4ZSUt3wZmO43yaVVVbZPrIesr5/e233IwDw3QBe9d5fAwDn3G8A+AsAJpxzdaKzEwAu36ohX3PoDNex9Bqb3bpDcQZYO8kqQwc0gxoamfpckdjp0nQdOehJWGz1mPQrVddeglKUWBzSaUiZUMiejvrazPWwj8imIqQKieUTclaTtkDHus4RiKgwpGGRbOpG4wNdqUFBa559ESKTA7y+vDWuEmou0oGOssItEKRsRBQVwVNISpQEBUyAOHauBHbyw6RfdLYSkgOyI4G3doM0F0nepNWNFAwQ0VbEYPa1ezQREjhh/Ry5ZOcfkAevYe0ixzZNBFcVJh576KL1pTXPdCbVKUgRFNF+IFSLpqM0rOlENZYVp3RPBOvY+/ZpQ195KyK/piSqVJWpJFxQqCEhKgzvk/oaVxA5r6EWKkn3VQ3L96upsQt7aJEZbHn5DufckLNfirS9/xDAD3Ob9wD4rfvQt8oqq2yPbbcVzW/X7ofP7DPOuY/A6BddAJ8D8ByA3wHwq865n+VnH7xVW1k7x+DFZQyd5eyZ0BQ2T9nM37xufou1R8yPsXbKfF1DX6ePYjGmiwTBOs3G4ZVk0bEkvaNm7fUGbJYV+bZcIad34mDcRT4U0ThUFUjhd87c2WKSVEy0pdSSzJNGIESYRxSkbXpTNts3v36l0IfOqH3fnEuT09cKfQLTcjqsbYAkPK+6mTqOEFn9GsmmqtidJIIHbf5S4r1qX6a+BJFBG6VULfU/SNIkFc1VUTzUSlCi9hFDmIPnI9HZvcGQ8Y1nKPV0huk/E0nKE4D2ePxZtBYMpawfNAgz/gIryssXWlNF+Lh/qGHAIVUCeJAA0lgjCnMGH6jSmojWGzc45oPR1+u4QmgzQb6lZHfJBqVyRFwhaNvGDRvbYVKT2iM2Br3BeM41rgLqi1tT5nZjnfwhJs167z8A4AOlj88AePt96E5llVV2F+1OeGbOubMAlgH0AHS999vWBHiwMwDyHG69HZKVfab3JvUAACAASURBVOI/0gyUsVbiyLLNXh0SPEPkMEURijap/iQjShus7t2eiMMVvBIqbE0/iaJ3shCZQ5x1FRkLUt603jQTiBsRedTWS0J5IryKrJv4NbI187N0icCCLBFldwauzBf7kVgQipSfTZV+VuMsn8snp5lb35WFKZPkcVV8B2WIwuflKC0QpZHqW8m3ANA5bm30I3M2L1H4UtdOkjpJOtnx3zNUtfS0HaczSnQybGPYlcJQM7Z/4zW2zcQrdn3bB1hVnXLpzYuGJuX7sjfFvgVZ8Ukm8aeCm7w2kqgKCf66b7hvIAwDQcSyNSdJchJ5mQaWPjp0PZuzJOwy6X2YaWuDqjif+OSUUuUbe8MF24MMgO/03l+/1UYP9sOsssoq2/dWSQDtxPIcbnk1+J7EDwMQfRCMlnlyu+ovG48qZ8TQpShlnjLEjLRpZpKPyeURRdTnDen1GNHr0E/VuLJYOL5LgnWKJsp3FiJZki4iP6k9Ff1LDfrPmlc4+7IQS+B6JRFWCQW2XizJHZdqYnYOjcbzWGAEjz6ZHrlwQoRIUp9CZFZSReU6lJSryftIJoUCMOxLHiq0RyQb0Br5U4oyZpSkqSm1KPWZCd1yW0lI4zHzg8k3BwAZjzl5w9B5EEmUw4vvl59JCoLQpzRwndFSInCNj/xfaZGStaeJwCirM3Sex5dwZzKmIW1pjvfetO2bSl8DiFLlSX91rkLVQvQFNM1asEJbdb52poopgM3L8Z7JBsjvW9oqz7Qb226ZmXJGac95758rbeYB/AfnnAfwC32+D/ZgP8wqq6yyfW/b1QDgg2nbhxPtW733l51zhwB8zDn3ovf+k/02vF/pTJVVVtk3iHXyWt+/nZj3/jJfZwH8Jm4SJHywkZn38N0uPHXs3UtxyRK0nZTu84ph/R5ThWp0SAc6AdsDInlV6S3dCZFyU+Iit2Uq1crjFko/80O2RHrs163dVC9NZFDPIET9qi1JpfigIIJL/N6+wdA56Qki9EpRVRWdgEjUDXpcCm5QgbY3bOdRW00qFnF8suvmQM+mtARjXxMncFDwYH97R43qoApVQWut3zJTKTdKSQpViZIgiAIw2lZ0C37dpRpuTfUoEakdjqq0QbONaVleS9aknd5Bqnxc5bKvrrqoDB4kS/fpTxutItwnUhBWEIfqKGktifoqdd6WeF2VGiaCcBIMcQu2NG8/fgSpdZh2N/QKKSyJkkfAORq7nMd7NVbdCn1hgKF93JbO2Q07XosBMX2euh5y1i5o9vom4dy27dZn5pwbBpB575f5//cA+F+22/7BfphVVlll+97uoNTcYQC/yWhwHcC/8d5/dLuNH+yHmYdpOUn7PHFIS5PdH6P2lhCGXkmmTSvlhOpAnKGFgpRw3roeUVwgqDKkvXRKKT42Ezau0qE7HkP2UvmUk335TRaEyOs2K4sS0JqP0EwhdWlUbZwkqqDKaz1xJneVGL9k6KRGWor0urrjLbYZSZuBVkFEIzVdoaze4ahaGoIq0sHfLCYxhwT5lO6imgWizSgY0S2SRQGEWghiNpST3WuPn7L3TLROTbVTZUEBeCmea0C3TCQXHUKIOSdxNKVmCLE0L2wjrSfScpLwP3CWaFnjwIBJaDVJlJdW2/phXptFG5fhL1o2XwhgLUYHvSOFB1OsQ6HxUO3Qoejc703Z76B51raRuq5+DxsH7D4emon5ZjlXA/Nv5rX/Uv9T36ntFpl5788AeNNOt3+wH2aVVVbZvrdKNnsH5lsN5I8eQ6aq2ensLJ8MkUsuHxD9AXURbVMFT/3DGS+nj6lB9U9VerYvuc+wIZiJV6ydqRf5OasyFapCC0GO2szZHrd9O2P2+eSXWF8xkV4RIlt5yhBZXrf3418hxSGpoLM5QT8LfWKh2jYRVHOG6CtBK/likcYRKrAH4mqSNC6/TYnsu0X7v4+8T37+kn0lvXr6/EINAgCZVJWIMHpzhixVA8BJPTapOO7LEjlKEmeCtdKaAGDjkI2HvE/ymeVHzW8kRFsfjWh99QS3dgd0QNtWif9t0hiWk0x6pWMJyeo6SOIoqZMq1dvB6zYuUkKuMcG88aL5/uQTBBCJwTxHCIkRMef1rQ8PIbJQLWvWfHEt+oNTKohn2t76wb15CHWrh1lllVX2MFhFmt2Budwja3eDTyjrxoiM/F6OUa0GI2Dtk0WN+IIEEP0fsZIQ0ZtIoskEo/SPGhOOR79uUS/VRuxMM+1lJJJylbirpGEpB29M2sWWTyuNgK4fsxl1YI5+MKEuzr4piht5kak1JA8HuaBxohVXrCOw5X8gJIbnRI8hLSixFNkBiJIzSp8qfw/AHTF/lfw7od5BL0n0F5IhWs6UsiUyrdDW6vZkzqxM5E2isc15Rm5LFc3rM4YA5UNLhRanPzOrzgEAutN2PUR8DseZL/rsrEGOt/yF9M+6JOneU0ixwdfJ8xYdDZH0p0wQU7I8thPHg6hZhFuheJ9cU60MQh0IEbf1+1Bb7dj+wKvWlylXTEHbrVUPs8oqq+yhsOphtgPztQzd8YE4ayX8JkkIh4RszoaSVOlNUdZ6IIlmlmWCOcP1xgwxLT0Wo0RDV+xYrSv0WyiiV0I69YWI/IKIIStnt/j+yFXKHTN5WbIytpO9bDDFaZh+HaUx+VYCFxmVCxWJ5JdSGo3GYiz6X1RlPUQXNWNfpjRzUgkJJaQU/FNlH1nqh5RootK7FKHsba3+FKq3E+EFHyNlmrb4x/qYEHeoxJRUZwojJa4Y3649Y6hx4KptO/WF+dig/LAUVGxP2nWoX+N1DueV+LTKtVKVME9fmk8EKct1UYNP7qKNf/6YRbrbU5FnNvda9oHVxCZfougmfbt5IufjW+ZHqxHth/Ggn7O2Qj9qKhvEa9Md3ptE8+phVllllT0Udgc8s9uyB/ph1p7IcO77hzD9JZuhxjcjsspWDXFJ8E/DGVjlTz1i7xMkFZj/lGsW/+jKO2zWHbkU0YTcT11WjG5IFJCz4rU320x6/HeiOGCm2Y++uYAJSyhFkVAAyFmvUXLH4napqIfvpLPwNpeTs/6WKuyICd/ZASXXsw36WFIs5JN6jwCQKYom5CFOVLKNfGUo+buE+IRMgHgt5CsDI87qY8qfCn3Quag99j8k85eqfwMISehLr7O+jZy340hCCYn0kCSKevRD6jr0pg0B1shXVII4kAheEq13D1H+e3WrHzJUeFfyeJDRJlqnX6x1Ofohu2+zazX95WKdzI1jtm/q81PxliFVbeeY9hjlXTtp93ZjIiK/Ov2yrfmtmRy7se7DLM5YWWWVfeNYtcysrLLKHgqrHmY7sOZijkc+uoHrbzD4PpGnVYHoDJ8gE5Pvc6a0tCdJiF2JUFrLy4xVhnIGBw5+wd5vTiZLOl6fy9+mepb2evwTBuMnX+KSK02Xkla7NLC0vOQ2+ZAoFXEZMvplOuJVGUkpQ0qsTlKHguKuaA+eiev17S9zWKZJeZfLnKDRn1QqCktGWq76jTpHko0LCrHqtygAamNpqw6YAgpaNjnRW0XFYMK8PxmTx0FH+Ranu7TQUoKtzoVqrsMXOZaiUKjfaUCDQYnaNWr/L6nOAsdfJNRkSRbIxbwf69dXCtumpNmgU0aHvPTxGtcY/FD5zIF4fo/+mqU65dfmiufIZfjQfHRt+OOx0hcQ06dUq6K+wiT1haRuJpfoteGty/rdmK8eZpVVVtnDYFUAYAfmOj00riziIGfWpddGhdCxrxiyaJ+yNBRRMhap/75y3Ga+oavR2T75AmdQUgEaTHxe/WYjLi48niR1U4G0Qe7k+Cs2w60w/UUZHNmRGLJvkiybrfAzknI7RygJNEeaR0I69VS9xUKJpKm+vf3R8FnrBkmhpKqEFBXWbRRKVfUgAIF6oNtNBExZSoC9FTUiILJU476UwiOKRt+W+lU5R1LViH2tpelUSohXNfJzptrbWyCSmoqJ8mUyb7j5Jeej9KCk7qQoPaoRWpu1dmvLts/604Z8Bi4l10fjoHapYKxrlqbQocnaqY/ZPda4wCR1ErVFpRDVBIikbinyBhkkod2ElBtqdgbqENP5SA9yw3acXpIWh5zj3aqoGZVVVlllwXpVNHMn5gDnUGOa0GAyAbSPEIEd48z6uM1sc2+yeezR32bF64QYuHHIZuTh69TKP8YE5DZ9H2vxojRW7WAHv2AzXH3R2ls7YX6ZwRlSEZpxiNvjTD4fss86j7IuAeV8QnJ3qvdOcmx+yhBAtkD0oFm/GzHOhe8y38npX7fZXZV4ekfsPEQKLlTPJhk0UDLkB0v7cAsLYoyqB5lQTUKSuKgTTO3pJ+MTUpEk8qhqT0ozm1/fso/8dCG9iO3XeNzgYwSQS+YoEWwEElKxLKVmqNq50KDQKfvUmiXVZG1rjcnuQfPB1tdLEkDteKPmjxy17ySnJHqLalNIMLRcEwCRKiPKiu6EPBFcKI9/XUnwRG/BP5umWKmWAOkcd2qVz6yyyip7KKxaZu7EvIfrdIOvoNY6EL46+xcpmfO0fVf/EitEL9NnI5BVKKnNj1RVWnJBa/ZFcylu3FhjdK7DeoqsdrM5wejXxtZoqf5XFR0hsuZVIhpFIUdjBE4zfo1Rwd44kQeFFwcvRWR56gqR3WJRADHU05xg2stGn1leqEjS1f2QGaOJIdFZfZQfjIjMjST+HUqah7Sdkt8qjWYKtUnQUv4i+ZoyihL2ZiOqq0kuSMKLqpo0qqpH8ZqpL2VLI7ZbjPeCKoKrilGHCecSBSjIWjNqrbS1gHrDa5T9rs0zVUsy7yXkl/NeCJWxAOT0B8ZIqGqEspJ6GoWUYKOQscQAFIUNRO7kh9DhfdQHbe7GdpCFtif2YD/MKqussn1vVTRzJ5Y5+IFm8KnULsfiHkc+YzPQ6lmbQSdftFlZCbf5kGR4okRPe5yz/BgF64ic9DpxJvpSlk/afhsH7XX+Kdt3+susHj5DBJIIF0qqqBZ8V5wViWgUZXQriW+oVPjDkQcWZHJWkrqTjFR1TxiXqH6W9Tiv2qzeWKJvJfEjaebuPnkMANC8nlTmRgm1MMobKnQrzYipUGDdzAICItctm6Jvjj6hrFzkAzEaKv9XSD0T8ltj2tGpE7F9ooeAyIh65R8siE/KFydfU9l3xushKaC0vfpyUYBS9TOVupSmTQUhRY2XIqKMTG8ejhHugVeJhLltiM4Ska08ZuM/lkhtQ7VTXzXBy1ycOqGwgSQayyrquYrdXLJ7wmtbobhEpMGJB5dEde/E7lUAoCo1V1llld1V877/307MOVdzzn3OOffbt9r2wUZmAJD74G+QOCAADF6ir2yNvhPJHF819FYXs3shIo/6YZvFNieJoBjFzJjAnpZoay7bDHrhXXZVjnyCEU+iOPmnsnaMEoVEZiExRv8UDQz+o0Qi2XOmditFqWrfJ/layecZZV00y4cMAPGbsqR8HL9TwYsgwazZeTHyp8IxWV5tS/ZAnxJzYZtusf/9BBY90Y1v8zzEixN603nMJRI95KDhwoy9cl+wj4XiKuXjEaGpj/K7lYUXAUQpbI5PfZbcMWZdFDIZJF0kP5jEGYkwm3MpMq4V2g0SQCzfN7ZKQc+kME6oTn7MItyO5QZDEZ/BuNoI6F/Rb10Pjkso09jY6rtEH5HN3dgdRjPfB+AFAGO32rBCZpVVVtldNe9d379bmXPuBIDvB/Avd3KcbZGZc+6tO9i/472/w0JUlVVW2cNsd0DN+KcAfhLA6K02BG6+zPwEgD8DbhqKOA3g0Z32bM+tl1sYXAvwdElBx3WLCdMBxkvNVFA6IRg26UxucjnQecSoHmtHmfLRSByiHJXBi9be+AtMn+GyYOMRI3M2UgJpKdFclo2XEHSS1qOaAhmXLiHdqEx2BcLSUBpZcVlZPGfX25qQ71SzkhSDTATT+tYlaWi39D6cR9KnkGZE3f1AzeijTaZrVr7hRLDNtBQaTPZdLyoKu5LT2pcrSaXfifLBpVctJK0nS0alAalvos/o82N2jxQqvzP4ED7r+cLnWSc6jERsDn2S2ECJOlFLalVkpQRwVa1SjczuWFxmNi+Vltm8VkFXTilR6X2kOg3rd5ea4Zx7L4D3Jh89571/jt/9JQCz3vvnnXPfsZPj3Oxh9mfe+3febGfn3B/s5CCVVVbZN67l20Qz+eB6bpvdvhXADzjn3gWrDjjmnPvX3vu/tt1xtn2Y3epBttNt7q55oNeLKTKJQqs7f8U+O1iqMFPSiK8lIe+QgE1n7upxmyWVYH7kT2MCdXvMhu7Q8zZTB/Ij923OUSk0nZbKEjNymDdKzv3xGJRoMxgxwOBB75xVm6odKdIKrFm2W5IWCtJG0n/vbU3XkWUk3HZOsZpSI96IIgjXVlXlSKRWtsFZ309EqSBRJoImv86xpFoLJMq10sov18kUtaIdr1lPMjg815DE7eiEH4goJZyH0n/W+qRHAUUoERASHfElpdxstbWlTyKgOtax3Dxl92DrIqvFr8TgRwj+jBYlhQIJV33pA2/S+x1I0pASxWXdj0KUIchBSSOf96nFQOSrV9xhVtNuOLPe+58G8NMAQGT2Ezd7kAE7CAA45/4T59x48n7COffuXfQvbXPCOfcR59yLzrkXnHPf4pybcs59zDn3Ml8nb91SZZVVtt9ttwGA27WdUDM+4L3/zdgxv+Cc+wCA//cOjvvPAHzUe//DzrkmgCEA/yOA3/fe/wPn3PsBvB/AT92sEd+om5a55HZWInLSjKy6hIHCwJkqJO4mPiEhJlUJ7wzZ+8mXKWC3FGff1SPWzsCNIqrSDCgfR6oNHxDRhH0WZmEl/dKftHYq7jN0jlXOKQFUE/lURMxEGiZTorr8KxI5VFVyoi6fJ34U0jRELVDlJiXFK2EeQKzVeclQlT/EauH1olSMCMkAUGfV8BrrfgYipmgFiS8oJJar3xwPoaFUaDF0n5+FGgYLNl49kmXTn4x8ZK4koSMKgkitaRV3XCcNhKlcYfYvCV661I/H8ehQ+791RQn4RUEBAIAQmEQsx4o0joAMU8FL/i/UJTL28ikbi/pG7H+DNSlaF4rJ9L1DhholXODTimAk7OaUB8IF3JndYTqT9/7jAD5+q+12Qs3ot82u+WnOuTEA3w7ggwDgvW977xcA/CCAD3GzDwG4I/RXWWWV7Q/bT8jsz51z/xjAv4A9Y38cwPN3cMzHAFwD8EvOuTexrfcBOOy9nwEA7/2Mc26rUwjFCMhAYyxUKwIi0gGAnP6K4LOSpLRIjtzOJ7OkpJB7lCgenrG2B1+12dknNTZHz5kPZZVijPUF+jwoqdKlfFDWSWZ5+W+EHhjVVHqOfERCYwAiIVWJ2q1W4VxVsdoOytlbhMgpegdCbcciMROIvhmJ86laT3fA5rDBmdh8qHikSKfGe6mY2F67FtPK/EHKNB8losysLxKQrKXJzOzXxmnbR0g4+PpuUildEt4ScqxxDAoVnUTCVTqXItkc042jFCNYjAi8McdUKvZNPjnH9KDgLzwQfbNByHFNsj7lpO4+94TuwxIi07a9y1fCLvKXSjxRhPCxV5YL74EoQdUhIbxxtSi+KSugUfYhe/Vy321v1/L83uRm7gSZ/TiANoBfA/D/ANgA8Dfv4Jh1AG8F8PPe+7fA3Ivv3+nO3vvnvPfPeu+fbda2Ljsqq6yyfWbe9f/bY7slMvPerwJ4P4MAufe+T67HbdlFABe995/h+4/AHmZXnXNHicqOAtga7ipbuwN/7hJ6b3wCANC4kdSoFDqRhHOTfikikVwoJfEViAfUYDpQnQgtcH6SpPE6uVvjL9pn6xRlHHqFidRCZGmRlVLhj0ySOUQnQkn5YMJz4sya+fFiG+rTchJq0ndBItnOrcf2uuOGeNpjW+V9Vo7ZPoNz1qeJz1lyesGntVJCRkRKigoqolc7diRs0mMajhBZLjSxTsSZpt5wHPIGUTX9mmF8+kj1BN7XKZOdDvLiPJ5PE/3pE8uul64Nx2voa/QFloujAAGthPZUb1UJ2ykPjOlWQXZHNTHFA5xOYltXyW0bHS32SX0etnuidihGiHPKQAl11W4Q4bfp/0zSyjqPG4prXaQ8uvyDlBHK5eN9/FQ8qHiJfdLTdmP3SgJoJ9HMb3LOfQnAFwB8yTn3Befc23Z7QO/9FQAXnHNP86PvAvBVAP8WwHv42XsA/NZuj1FZZZXtI/Pb/O2x7cRn9kEA/633/o8AwDn3bQB+CcAb7+C4Pw7glxnJPAPgR2EP1g87534MwHkAf/kO2q+sssr2ie0n2exlPcgAwHv/x865O1pqeu8/D+DZPl9912011GoCT56KYeU+KTJKbwll7zvF9I72geh3U/g6OLaXqfZJR7Gcy0CkKYh82GoaxO9SgdRJubMWAwybJ2ypGEL1pAgERQM6iFO4HJzGXDaBy8pAFE716kVHEVGSaTTrR20MVg9z+ZAsrdeOUPXj09bOyFeuIrWCHhgtUCS0xDpAEir1zFIN/Q5TaxrUA9PyO2Nlqu6hSENRdarWdaY+icpQWqekgZhAfp6h4oYIx1wS+5OxbqRIvmUrK0e4hEsbaCH6QDQaLcXkrkgCUVqK5lQc8R2eO5eSBb03LUVDH+z96mtsWdkZsuNMfCpeB51HNi/VFWt/C0EcQOu8jYtSneq8n9xJ06+r635KltaiZGQLPLf+MYOd2z5Smv1T59wvAPgVWLf+UwAfVyK69/6zd7F/lVVW2QNu/h5FM3fyMHszXz9Q+vwvwB5u9y+lKc+RLa2hqVSiNAFXxEKqbnbH7bvGLOkFozYL1zYSZKNZipQGVe0JVbgTh2hISq4XnbBh5pYjOq1oPsFUKtXPJOk06r7fJHVFgYzNojqtFGJTa09aH0Sv2Byz1/k3K5ARsd/UF0jBuGKzvRLDhXRSNValIIUkfRJKg7KqdNISJ3aNjv7Vk4bmRs7YuKw/bue+ORFvwca0IYLmPJGMiM1EQUrez7pbxycjRULKqhqfbDZqn5XJtzmRZHDUi84xEgMNGaklvkXUpuuqa7RNrU8gIuXwypVDgS6iVYBSwXicoa9ZAMb3qTIVlGTVX+mnBYGBeE/rHIXIAgWkHORIzkO6e7kqx1/BHdo+eZh577/zXnSkssoqe0jtfi8znXP/w8129N7/473vzm1atwc/vwg3SdpCPfE2SX6llKjbYT3NJn0JSCVipFjLmS0/YrNyToqGqu0ACDI6ohYopcSp8pEQWTJLSh6oc9TQQ3vKZtjmPH1l9IG4NPldKquimiiJvGdtNc7EaTP40Vj9p3WNxNTDhgQmvmB9mvpaTPuqUz23N2zfNWaooDpB/2AyPp1nn7T+XmLCdInwGXx2rUS1lNdk4Fqb52x9WTpl24gKAgCDM0RORGQ33mwUhpFLdl7rh2yf1mIc09YcfUCUHwoUCZFYE5SYiehKBC9CtfyqnnSFQo0EUUeYFicV4HzWkJN091MKSEhb4vGUaqUE+kK9AKE0kXEX5GfjdT9mq4JCtaysSELoHWUql+7/pNJSXTVHRbquGyrVSqXO+7NQO5TjkV2NFaHuyO73wwzAPwLweQC/C2AT9worVlZZZQ+X7YNo5lsB/AhMtvZ5WADg972/VxS4HVijDhyajgTStYg4gg+AqKF5kdEuSd5ITibxs8UKOSSqUtjRyQ+TzIj5AaJBIsB1op+Rr9Kv1Ke6NzjjN4keNp80cmkhJSk5frpPrlQeRTU5+6YUS5F6mzqmomrHTwKIKCivx5tr5RGbqdcO2rkdYAS0cYUE5KTStaRl5LtqzXIMicR0HimBtHlG0dhi5HZ6w8YvpPwkJiQ8et72bV4yv1d3kLJESf9DGo7Is0pWFyJOUUyzRKs8QPKqUqpUwaiZVPeWj0yoTpWQ6EvLmbrlksRzRb1F8vVzRd9iSr4OdRW0f0CLxSrlhUpJZYmkUm0JCW0CCcKjuEGPYp9CcWVpJiCm9WWsPYqtxedvy+47adZ7/3nv/fu992+Gcc1+EMBXnXM/cG+6VllllT0Ulrv+f3tstwwAOOcOAngLgDfAUpFunWZ0jyxv1rD+6AQGzxBBrSfITOhGn0m0b7zkC1pIUJESspXcXaopmCayg9/l5JetT9tr64ghjoamo3QfRZI4M4dq2EJxh02COU1nCrI+26SWpLI4QTiwXYwCDl9kbUcKLdavJRWXnjKEMUxEpj7pXHtJdfWAdpUsrpqdi4wCy4eWyoILJR6UvA4rXa1pTJNosvw28jVJHofjNXTWvu9OxepV3RFDUTVdb/kqhbZSuZ0SOgyy1joOt80TNIpXTf8moDWhOZ5HQGFJ9akekVg4slYBOm7CfROydPP0VZYrjjMhP72Pgn8uVzpcMaE9FZ1UhSioyrp4ZgcNXa8fs7EcWk8FG+ifTTiSd2LufvvMnHM/CuOUDcDyJ/+K937fPMgqq6yyB8Tu98MMtrT8Eiy16D8G8D0u4aJ47+/7cjPb7AV5HiAmagMxOblsSrSVfHCW+Kc049ev0l8knpnqH6YRKyKA2g2b0aY/Z6+KEvXIKatdj8nvIbJJBJMtM7r5jPm0GvNMRE5mSSVzhxlWqIuzZlp/MlQJp59NETAJ8DnKLHcPxkyGXsvQyvIJIkBvvpWRM0Rfq3EcM1Zaz0aISkdYfOOyOVVC0neKIuWHEoePaEJCAKlPK6AbSUgrqkyulKSx6/NJwRf660LWw0IpOSX1TwklqmgIfay6J8pFSwAEgYLQt0uMHreKctkuqXVaU7EZ8RWJhpTtIDRvX/Iw3WJmipLJC9I82oX+r3A/8Rx7xw3ZBw5k2m9F31V8huM1VC6+AgQE3pnaKjm+K9sHAYCKX1ZZZZXdud1vZOa9/8S96UJllVX2UNtWcHlX7GY+s+e89+/d7vudbnM3zdcc8tGBQAnwSSWeoABKCykggtms45g6iHNC7aCxRX90WJ745Kpo+eeKCeAN5mnr89QxA29r4QAAIABJREFUHGLU46IYMA2lV5y6CkslkSXLFYUYnnfNrdpk4XBcruUTfFWlpeRwQwwOjL5g7a2fMsdwb9iWV/VkmRnqbbKZLsP6zbnSciRZugeKgYITClhoeZlWhyL5ucMgSvNCifagGg1JgMSFmpQ2zu3TloLTPLuVTxCUdqk2HJZym81CG6kDPVw/UhxC/xUs0rkmS1OvGgMKPvEc6wvF2gwAwlKxN8FaBnT4d8eoY0aXQ20uCQBomaxlJl0QG4fs/IbOpaqxHGc6/EU/kvBCoB8l1I/NRy0otPAEXRu/hzuzfbDMfLdz7mZVQB2qpWhllVV2C9tNNNM5NwDgkwBasOfUR7z35fzwgt3sYfZ3d3DMP7r1JnfPXKeH2pX5SDxMvvOc5V2pundw8qq6eFKjsibtdCWpK8VEzuWBhLgYdiKyk9qq0kbG+lSUl149kYXj8erXlwt9LaA5UhrkbM/mOesLsaXbyuiA3jxhs3GdoXtJ7KQUk+U3GJIZIgl38JzB0aXXstZjK6qirh0uphPV10vjRASVn47J76qnIOJrQGKl6wEA+WixVmSoWqXgB4MG2dlYmEDjIGd787L1v3fYzl11C4AkSKDgUDlJvNQ3IE0K56uc70qTUvWmFCEr9UzSTs72DWIHV2NQKBCOF4ryRI2LhPhKTUrGKVSFVxqWjkOU6pfjOWeqeyC5KSWns8KW6jkgCSQ1iNpai8W6nLu23fnMNgG803u/4pxrAPhj59zveu8/vd0ON/OZfWi77yqrrLLK7qYx00iExQb/bvpY3HXJuH1hDkCWhQRniSgCCZlUyEkyKdKTHykm3AKJT0gpJURSriSIaB/SZyISokLzrAoVZsvlOOPlFEUUWlBlqIAUONO6JDk6LxMXeR4STUx9Zm6I0jZEOI1FnqtmZR5v9fEEbR1S+7bvMP0tG5Pcx8VbZPkUx/BVe785bvvWlw3dicCaVnF3lOuROCAcJYYomZRSaETSEDUlpOUIgUj8sd7nthX1QOfKcVp829GwychZO6ZqmkoeSCk/qnzlD8TxcVfpt1O7qvJFRO/6CILmqroldE401LjC+7OdElSJAlWzQP4wrgJCfYX5hOKja857rHuAVaWWWD3+jU+GTbMZplIpgV0rFiWY13mPJPf2jTfYNuOv9BezvF3bbpmZVlqjPee9fy75vgZLpXwCwL9I6ob0tQf7YVZZZZXtf9smdYkPruf6fmnf9wC82Tk3AeA3nXOv995/ebvtb+th5pzLAIx47+9USHdvLMvgR4ei3HQSzQz+D81i6yX0w6hONhr9YLUrNouVpaJzIoQs9aUIHSgKpJlOESz55BJfh5DA5muskpASt+tzJX9GcmyhnHJ+v3whhWo+/L92mXUry3LT9CsNXItoqLFq59G4brPw0jOGLDfeZWOw9tnxsO3QFWtv5AIT5UmqXGWtzbVDdh4H/zyOn9uUHA7PSn4qnqt8W0AkbtbmiQjku2SF9FA1PPUTSrpbIgG8B9S3wdlImu0ROWZE5ZjlOB2l+CZRdIos/VFWbV9PCNOI11k+UkViAcC1iXLUjvxSuj+TdKmy6KML58z7lOOUJyli2aD57bzIrRQabbLeZ201IQqzQnoQBF1K5I2QROqTqH5jzfpdu7GKPbE7r2i+4Jz7OIDvBbDtw2wn1Zn+jXNuzDk3DKui9DXn3E6CA5VVVlllcL7/3033ce4gERmcRVC+G8CLN9tnJ8jstd77JefcXwXw7wD8FGwd+7/vYN+7a70cbnEliuGlCcKDpcijRPTEIVL17SsxHapvZBCIssejSeStVCDFsWZnQGJMNvatOMSq21hfoW+DaS2hKIf4ckktSflXugdthm2uMjWGFcIVCQUQxf9UHEPnrGgjk+znXh/Pw7N7B1g9fIDpWWMfVjQ2EU+cpW+JKTYDs0SfRFStBZsbVR0dALIGkQU5dfUF+mg0ppsxjUfpXa5UmR30F/lSSg4QfVbi9em6tGbFI4zz9bW32HkfoJR3jYg4Z397LEbTGYvXbPjzl4p9oSkdzomvdSOJmuofCSMGPyrRT5KilCm5vsQXDAiNvqx01SEBhN4o0ed5imXKP5sicqF9Co2uP2b3oKLWisKnyfuNpa11Yu/IdofMjgL4EP1mGYAPe+9/+2Y77ORh1mBo9N0A/rn3vuPcvcqDr6yyyh5428XTwnv/RZhaz47tlstMAL8A4CyAYQCfdM6dwp0Xn6qsssq+QWw3y8zd2E4KmvwcgJ9LPjrnnNsfzP9aBj8yFFQOtMQA4lIoUDG0dJMSqRy4nSRMLrqFnMoDpTSdhGy6ZXkpRz0rGEnNop5qtyvFiY74jKuCvMnLIH2qpAaAyLKbk3TyXiTR9hrnkyQdKFQbEjlUy+5mSU8rmcKmv2RL0uzsZduUFaNa56S2EJdXIp3mIyKB2vjUV1hNacPGQMtFAMiHWefgLEmgotEwlUjpNECiSsKlY6imJA19jf9QH/LympaoGkMqeCQ1Nqe+aksqjV3v0aNpC0HBtrYRl4Hd4zYeSjMKNR5oqmGZT8f6n2GZp3tE96DOeTk61qW6IX08KbX446Z55um4T2udhiX7Esf5OpeMQd0l0fVTP+fsnAeZniYaUo81BhYfjxSTgRtc4o4MYU/sfpeac879Ne/9v75JYZP7X9Ckssoq2/d238UZYctKAOiTl7NPrNezkDwRT7pmlnqr0jXKmmRKGO5bw1Da7arIk22dWZzScUQ2ZCUbkSudNONT1U8es32I1YYO2Kw79hId/6KEuORMqMNeaxNxlIi9KfVDfdJ3IZwvoi0pDqOXIsLMlMJFekJwhhMRNK4mHgWG72sLiZIsAC8FW6bpFMjLqrsphEqai5K+C0RkoWQhSyXXixx6lMnSSfWhgvZb0v9IvI3fN/S/SNFtJnWTOJ1tFp38QKQ5KPG+VtIXC8nqM0klIwVclGSv1QBRUaq7J7TmrqpGBQm9pPboOvgk9UrJ5450EVeuhTmY0ESI8jsnpwv9blxipXNW5Zp/bfyuNWftDL+KvbH7/TDz3v8CX/9++Tvn3B4lbVVWWWUPu+0HZAYAIFntb3jvz/L9NwH4lwDedFd7thNzmVEw5DdKQ9JMts5KVXuyRaqKClkl1ZkiaZNhdiUVr28VDwkJ61IpVZeYhhL8eMm+2Unz0XRGbNgn/iwmTAOAb21V9uyN2GcDs6RdEKmFGpupNrz+ke+vpFvfnmTF8IXoUxGikJ+wtk5y5UCzcH72JdEmiZhSnvVUsO1N2XjVkpB+rnqiktcZKM6D+Xj0y2REdEJi6rfkcITIfIKUpRbbPWALiOBLVPraapEkmpqQTZO0Cvm9lIQNICb0jxrCFLk165ASwnMtIGT6Y0PK2ZDtGyg4afJ7g+0Ml/xTHC+pxtZS6ka7iDCDb1GIM1WEph+2fp0rFZLE5Q9rzFpfxl6Jv4ORv2L+094fJX7AO7H98jAD8L8B+Khz7ucAHAfwfQB+9K72qrLKKntozN1vcUaZ9/7fO+f+awAfA3AdwFu891dusdu9sczBNxshIVkVqYEk9UW+pdEiuTIQStPmiDSCcN1yKSUpFR1UhErfCY3QN9d7kyX7Nl6NQ7V+1LZtznM2F2rolFJlRuIs3zhLlCC/FxOchY4KSctEEWXSbKgdqnSXr12IByP6UdJ7kBoiOTdFHAH5qi9jRf9aTT6nJAos5JRW8QYAJ19aKrSoSkd8L0SWMSoYEE2awsXruXHIjjl6hbVBGUXF4nKyadH3uQVBCWX1IYvKH6jq4dlL5+0LrgDSymAhcb1EhJV0kU9Tn1TrVeek66nk+tWtKVbyP+asgenOqrZqscandZiySao7oePy3stZE3boWvQXrnzYVhD1wa1R0f1sO0ln+nsA/g8A3w7gfwbwcefc99/lflVWWWUPi/lt/vbYdrLMPADg7d77dQCfcs59FOYz+529785tWq9nPh0lj6ezl2ZFVQdiFEpy2oFLllR/Vg1Nt1SSDVJaSiqxTaQRKiLxc0UkG/nWqzVwif4cficem/clHJ5yuxT9U/qP/CJCdWn1Ic6yqrwTZvVNSi/Pc98EGfTIGVO7IaVKs/x0lMMpVAcHAirqjPN4a9LTjgFwRT6dEuOJWnr03awfjb6iEVUVahB9qgI5o445/ZtZ6gejT3LoYjEpWryqPJXAVmoPfZOuJCUUkFRyPQKCZwQyRE+JLBUJrS0mfcp5Xa+wapUqmksSqLs1aiofWeA2cmzbbzoNAGheS86P7aqmqlfqHs81S4RBxTnTvQCidlWd0r09eDX6dlePGOrfmN5ekv12bN8EALz37yu9PwfgL961HlVWWWUPl+2Xhxkrmv8UgNfCCgIDALz377yL/dqZ1WrGpg9Cc0lSt9jviviQf1TjrJzTv+b6REAV8RFKCbyk+YRzJQ6UoppCSEoQFrcoyTCQP6ozan1pLHG2XyplJazEWVjJ4Z6+nyCyJ5b3VCKho6TqUNVbyEa8JPr50ghui8iVLP6AyIQU0mimzpmzuZLfB67YrB8klA5E5BfOneOdycdE/1p7JKK9jdedsG2IottjzHqgHHSNaKXXSqTOrzLqJ0QpUUzPquhJPcuATiS0qOI2zDDwfYqTyD+lauvK3lAbEgvoHo/nPPCS+Un9Yye4DSPFkmbq9UFmqoPKwiMZq8RLmilPkvfX/qMn7Dizdg80zjFyLt9xKjEkXqV+G+x3QPzMYOkeiWiusWrjPfryHmUt3qOH2U5yM38ZwAsATgP4+7A8zT+7i32qrLLKHiJzef+/vbadPMymvfcfBNDx3n/Ce/9fAnjHnR7YOVdzzn3OOffbfH/aOfcZ59zLzrlfq4i5lVX2cNi+STQHIMw9wyjmZQAn9uDY74MhPjHz/iGAf+K9/1Xn3P8F4McA/PzNGugO1TH/xkmMv0QiY+Kg1zJJZMoaqQbBic3XwpKOulNheUkorrQg3y6mzgBx6RM0tkqa/dK9Sq2xROXadRF6S8/tVM9sqUjy3UItqZdqBCDSRnqPkP7AfWozrJGYONDrSjie4NLthCU4i1DanYgO+uDk5jHri3YeSonZfIMp6HYH4xw59ikek/UUVp+ytJqhszbGWbLiypiytfAkgwOHrN8nPmbXSHUDfEKRCRp04g4rbW2ZboREgy6kSykAotoOWppqSZ3q4omyomWy2ifpNNS13IjkZZFvPZeX2SsX7XO5BpJ7JBvntloCM4ij9kU2TrXFGqTENC5Ti4/L1loarKGFWgKqI8tUufyxI3Z89j9rxwsxRDbR+nGO3ee3NHt7to+WmT/rnBsH8HcA/AQskvm37+SgzrkTAL6fbcHZL/SdAD7CTT4E00+rrLLKHnTbL9SMRN1xEXtX9PefAvhJxCT2aQAL3ntNbxdh2QZbLK3o0hqYwNDVDtrTDNm340K8edFmrcZFc7r25JRWRSQplKYKniJVCm1J5ZOzcZpAvcWJS4QTyLqc5bMERUiHPajb6rvG9pchBBhEXi1LF/US1dIlokw66DXrKiUq9DlBBqI7KBDSC6qrhgxUKxMARs+pYhORHqV+5p82NNEet88nzkSUsv7GkwCA1lXr2/Dz5wvnNf7Zq7EvJIEuPaaTZ/CGar1CwYUULlUvUpCmV3TGtJME68YsryPHo6bAkciyIrum6UCjJFAzSKAqU50JQzytK8WkeyAJxKhPoYo7+5/WX5UwAQMVoZ7rYDENLJWfas4RIWt1IdRG+SaXVldX0r7oOjyefjOuZ31JaRhZx/o9fPHuVmfaa9sJMgvmnPvsnR7QOfeXAMx6759PP+6zad8h8N4/571/1nv/bKMx3G+TyiqrbD/ZfkFmJdsLlbVvBfADzrl3wageYzCkNuGcqxOdnYD55m5q2WYHA1+fxerrbP0v7XIA6BynXEyH6S0MoQcEQjmZ3mAcgsYNQxq1Bc7Q/Fx69VmaDKwEZCE9vRdi0uyepsbQh6Jka83Cm6cPAABaXzzLfeMcE4i18qPpeEr9mYs1DLx8Jr2iKGDWKPnV0rQeoojOpM3Q9WXr7xLF+tKk7u5IMW1p8bU2hhNfN7TYfMF8QyHBHYhpOkIGoppQNDFP0pk2p+0cT/yB9WH5pLWzetz6NnFlsdgmEFCtqhjhkCEoUSmaL8fbSH6obIXJ6Ycpkim/kfypCQrKh4mQiGa7Y/Z+7RDHYp3jdj0iNKdbgFWOdO2CxFOK6nWtWblefjtXruyUUIgkaBlQu/x42iYhguvqBZL3QUNvDYoNLJ82QFBrJxWpeLu4F89iL+xe5WbeFjLDHrD+vfc/7b0/4b1/FMCPAPgD7/1fBfCHAH6Ym70HwG/d6bEqq6yyfWC7QGbOuZPOuT90zr3gnPuKc+59N99jZ6TZ/w7AL3vv5733P7OTvu/SfgrArzrnfhbA5wB88FY7+EYd3aOTGDrHGS9JE3H0i1x/i/lzRi/YdCkcUL/GCOhIJJAGSZsJQw9BEntcdRCTqkaMQEo2OdQdLEU1XdInSQsF+WoihfaEvW+JUJpU+Q6+Pk47iuj1phmlTf0j7EP7KNOaiEp1rrEjcQ6T7NHmRKPw2lyRCGTcTVWlJFg4cV2630z3khhkUhnLk6ibTRE1so9LT9t5DV2NKGLoJZJKJbbZtnPsDdg+PV2HpOKSzj9IPEt6ieRfn6As4VP52eqrxYrjQSgySRBvT7AC0it2jzUlXEDEGhBZGlWWL07RUvnOJASaVJdShDyTRFKIWisKyfFJU69y3lNC5fLTklidpxF67S80qOgor2t9k77epDBZjxLd7phFtu+04scufWZdAH/He/9Z59wogOedcx/z3n91ux12ssw8AuDP6C/7RQD/3pcr0u7SvPcfB/Bx/n8GwNv3ot3KKqtsH9nuqjPNAJjh/8vOuRdgQcHdP8y89z9D5YzvgemY/XPn3IcBfNB7//Xb7+beWXsiw6vvHsYTHyT/aDgighDhFHBRJEwFHTh71q5H2SAlE4doFGfqULwkkXkJUaaScGN+iKKQrF4e+D2JeRaRyOk3CjLQSpGZiqklLvi/eDxVLZccTpqUTpQl+WclaqfRP6CY7tIbsltg7vW274Ev2baNJR43mbfkd4SjD3GNvkVFTw+TU5fsI19i56T5BWtr5K8N8HokEWghGaGQxhctobpx2PYVZzAt7tF57XH2l2k7TEoXEkwL1uSsWxrQtFCzkviJiNVXAGjNFrloOreBVyjNJGQ4sVVdviCflH4+E8Ufg49V95GisXwffKajfYJdivZqlUDJKpcIAgilic8mH2O2wOgyRQhcL16z2XfYOM3/EHmK/2vf09i5bfMwS5kJtOe898/12e5RWNm5z9zsMDsKAHjvvXPuCoArMPg3CeAjhH0/uZM2Kqussm9M226ZyQfXlodXYV/nRgD8OoD/3nt/0wXvTnxmfwvmkL8OI7n+XRYCzgC8DOOLVVZZZZX1td3yzFh8/NdhPvvfuNX2O9Uz+yFK/wTz3ufkjN03q68BBz7nMf+sweHJ56+F71qzxdcQztdyQUuaVBVVSgJ87RwzuF1fLBIZgT6pL3L6bhSpGYFECwTHb1c67HQid1tFMuq1b4pKGFNftSVRg0sjr9C9aCKJsz2TdtiKlDC4D5c77UdtSaYanACwMS6Cp71cf52Nz9TXrC8jX4+Ug96Ines6VV0Hbtg2jUskE0uVYypRzSAptztqx5x7g/V7+is2po3z8ZppeRlUPUqKrzkVPvJmXEY153nN6ArYPMg0oAP2Wl+Ny8zV49buyDkux7nklVshLLUSOkpISVrhuWno2Delurn1mCLWeYOxftsMpnSGrP2sa+2PjMV7wn35FWvXF10NIT1uorg8tAbpxOf1DSl6GyVCNRKCrgIXovboPiot7QFg+Op4Yds7tl08zJgV9EEAL3jvd1TW8pbUDO/9/1R+kCXfvXB7Xaysssq+4Wx3pNlvBfDXAbzTOfd5/r3rZjvcLml2X1nW9Ri62sbyI4YUNk9ERLN42lDE4A1DZMMX6MhtMRVHkvSJs7p3cLzwmbTnMWuUgULSskwzNKkAmWgKShNKteFF+VDqE5sfPWczbIdoYvhK6tRnUECET82wquCdzNjBsc+Qvc6sc9hm99ZZO4/WS3HmHhOZlcGP9ROscrRBpJkQVFXJpz5fVOkNWvSsxLTyRERmtXWeK4Mch//oenFcktlf/Q+Ig9/1JhiIIXIafDmiuUBzoRN88Iydo+o4XH9L7MvEy0RPSlcj6lo7Yf0fuG5j2jgfa2AqGLN5iiidZFOnYIIIyMl9pErjqsI1fIW6Y1eoSZeQWnOhKgZycunWiT4j9djDUbBA5xYPSMVZBQnSdCymgilVL1KJ+LlSuBK6yMhXLMVs8OreVGfazTLTe//HuE2S/gP9MKusssoeANsvSrP72nKP2loHbVbxufG+SBas12yG6/yKzaBd+nuUchNm5wTZqCJRj7ru9Rkqp/ary1kmOYoQqboB8lclM17nkJEQ1w6TiHnNUF1j3tq/+J30bY3H44ydoZ+obu1PvmSzb33V2hUKAGIKj6r/KOFYiCr4tBJfVEjZIpIcmGGC/KKQ7FZZObWrcwy+CtYkHVtOdPdFQCUdIQ8kWqabpalPqpuptCyhXFIxGnO8vmlFKqEQ+SpFZblo6OLgWkRBq09bKo/UbVeP2uvkyzaWm1OinCTkaNJdmqRo6LpvPMJ0uJOG3AZnEo1+gtnBC0TrVAeWz9QnyfBKAA+ITFQMorfaQdJE0vqr+p/3XH6JyravsXoBG4ejT662YcdSQnwgNk8WazKkvledY7a4R4nm+6XUXGWVVVbZndi+KWiyn811uqjPzMN1bSat16OvaeELNqMdYrqGhAShRGeigFSo0LdZV7Jrs26IHCYRz2Cq8KO+KGokpEeh3Hwykh3rC6wkpNzri0wYltwLP88i8ECd3Ru5TEFHkkxFPvWJf2T12VN2zKZ9NnyeoobXmJbFKGNvLImAUlRQ0cBcSIFijVkfUrEQVNDHl49LKC5BEQH5lSpoiUwb6n8CQZRSJN9Qw1N9pMRRWsMg1E24xlQo+QCPW4Q7S1Di8IvmCxvhPqMkx4aIqvx3yZjKj+qIOnvU6F86RR/dPMUtk4R5kYvXD9pnE59n/1XDM63+NGzn75+xa+e+yOhmt1PsU2Ly4ykKu/jNJrQweM32WT0cf9aqh9m6aucuVJ6pVoWq3U9FP1xDCf2LpTS43Vr1MKusssoeCqseZjsw5+AHmjj8aZtJXvq26Os4+rzNfo0VVgGaZsUlSQ5z9pXwH4AoFX2QET35v9qllBMg8NN6p8wPFiqAS27n61Y13AkxIKaU1LIir0x24vfsPNqTETkNvmBVsFV5ujfKyO1h1jaciv3fmLD2lp60u+cgqxgNsxrU8knxwyKCXT1i/T34p1FKCIiIzK8mScuKuMn3Jt9ZvZjqk6bTBD8jEXE4Y/kaU2QsKRtxoVilKR9kVJPVxNvj0c9WZ5pXo4RgnKrRLySkcYoXqnqU+IOBg5gzcpygaXHRQreZeB74f1e3yhJJ0klJ3Muvs+OOvMIo8KWYztRT+puO89gjAICaOGMcS7UJAJ0xpa1RRPGK7dsZtvaHZuP1bS4U+x9qaiohX4IF60na1zhRml6v4I6sWmZWVlllD4W5PgWx74Y9FA+zkACdjNn6AbKuyU0aOmcIQ8U3Aj+pGYegQ7+BGPK9RxjVXC+iPABo0AeXUTo6cIcowOfL1b8B5PwuSNGQm+aE2FYNt7SSpN+Q/E6fTJezb48s+PZwRHcLryEie94+m3x+lvsakhnf3FqvMW/Ql7XJCNZykYuVCkUGKRv6eQIXStvKZ5aee6eEwCRiqcheIrcTELBQTpvt8tzzepFJ3888/Xe5EHMqd8RxUHRUYgOh8ruk1DcSlDImSXZDZI0bdh6SF5dfz81EBN68aD7F2qqNk+45VT9vdKKUt77LupRvIupsLrLO65z56jqjEXmuTVO2aZIy5V+3c1RV8mwzRtDFzQsZI3qwiEu5xOT0pD7qdgnyu7YKmVVWWWUPg1XLzMoqq+zhsOphtgPLc7jlVWw+YbB98pPJknHEIHhz0SC3VF6zTTrXucToTscwf5eO5u4gHemnDL7XN+x17Gxc0jVIs1C4PaSSkLIR0lzSOpqiDSg5WelRTGzO2cbayQjzGyvWXzl3RbtYOmntrpyOjufHf4OpNqRvhFqLXFLX57kEG4oO9MFZOpq5BAv0AVIpUtKvloaFWgjJOfo+1AZkpG8ofYlt5FrapTpdvCaiHihxvjM+wHNnQCbxwXSHuTybMCrGwIy1O/cGa2P0UgymzD9p5zj5so1LiwGGoAmnBPnZuGSsiRytWgMk4da7RUIvEj0zz7QuIRIRbv151iN45FjYtnnJAi/t45OFfRT8wLwFGEa+Es95mOrI6ktQM1Zfh+I5d6eLKsmic4juEvTYkqWllpx9NdR2YRUyq6yyyh4Oqx5mO7CsBj82gpzJ49NfijSCtWM2e9UoAROIlpq9qPKKxNm+esSG48DnbDZsMK1l6RH7XCgGiLUvQ6IukUfOxOCgIz8SUYzbLNW6FNVBSepEUoMJARNCAEQ7XVdj35i4/em4aWOGwQeirO5hJt4T+dWY+rT8WJyFh2YYyGD6khATVrdPZRHxMtTyFEWjs9WpD1IjNC5SgFUdSp86nlnx263x2KQRtOhQF2WgPZlIMTEYoDQsT5R48P+z4Eea6F9fpWItEXggBB+yz/0MybNJ/3PWHZCCrcizUndV4CSt9eDmeB3GmZ52yNpoLRC9JTUedD825imJNKd7g+haAZM0Beoa074UMJFCse6jxqGwbeOM8So6p1nBjHVkAxl3fasSsu6B2612tJ1V6UyVVVbZQ2HVMnMnludwq+tBSidN7Rk+ZzNolyTTLomjqguZMfweUjcAuCcMva2cthk05+gc/AKr+bySlPIMdTNJ8ZB/ikjNh6qCSmkBAAAgAElEQVTiERkEykKzuG8gner9ZpLPxFlNaUDtUWtj5YSd66nfjWhUyGWTBOHGMqspsU7kylOG1MZeSM75EiuKi1YhpEm6iEtE+1TVKLxXmpfSvtRmSs1QmpcoHpKrod/QlwQYASBnezUdmwhE/qvWZvRzrj1i/RStpnHDUEV3miluC7EvWdtulDp9bhvPHLWuEZ032bdAGkWstZAKcwIRycoKKVa6D9XfF+2+8RJw7CZtKTFe/tQWaSgU8AzVpxKKSbjLmRblR5nedIIyQRejRFKPn62QZjRxjT7MDUpXUcpIhHEgIrLgM5vBndne1D+6pT3YD7PKKqts31uFzHZgvlVH59FDqK9xxr2RoBRGlDQby/Revo5Ujnjoqn0293qRNLnJqv3THEoksCWLreOREBsQjSRVUqE8HUt1DUXS5Gv7cfN1rByPM/foWfpSSNYcYERv4iWiuE7iq+koqZgzNmWm148xrems5H0SP1Wt5BlR8rjONem/KmfnQ8Wk8VABSG0lkT2JO7qSlJBj1DeN9paR3xaTJFBSob3HKk+D9P3puiraqOR0AHAkSNeXiMp5vSWA6JkyJv8YgCiHrnY0Hr2SIyjxmW2Rm5asD8fJpaRURQ6VwkW5oMyVhBHnI5qG7rFSBXZVZk/lfJQA31hVfUzrZ++I+S5zjmU9QWbaP/UD3pFVD7PKKqvsYbAqALAD85lDZ7QRfENpfchMtR7oG+gcstmwO26zZE5+2eZEHILB67bt1AtMK5pjpI+cnBQ5hCgT0YiqP8vX0RsgCljYGi0KSdaKJHG2V+R1PJG11nfdCcpaH7R2Ry9Y39aPRS6Q50Q9wPNoj7M6+XzJL5VyxyTDLV+f0rJKfUu/kzhg74jx+7IRytiwHmhnOvqc6kuK+gkRU7qI0t6KagJxfB2jr8FfJ6SRyE3LRl4l2lRamZCOUONyRKFB0pznmp+wa7b5BGt6so+1tTg+qijvXjlf6G8hgR2AX9xaBU39F/rV+KWpQ6E+Jrl78lOphqfk0l1SpTyneEEQcKSYZS7Z94RHuHHA/h95OUF2iGlMQtPyMQJxhdM+RnR4Zsup3Zbdq4fZXkVfK6usssr6m/f9/25hzrlfdM7NOue+vJPDVA+zyiqr7K6a8/3/dmD/CsD37vQ4D/Yys+bQHquhNcclUapw2iYFgMRFKc3KudthmkeWkGY7Q6yRSCKm1FxdOe0FSFQl6DDnEldk2kza6umSTrr3E+OF73JpWr16mU1G0uaN7zRd96XTNu+0bljfuoMMSiwkevgi2DJlZegcgxKqR6A0l3RWlN7+vG2rJWNwzKfObCrxhjoHUrcgRSAjmbM1G7XResdJVOUyuTvMSkgM0KwfjsvkgZqUTqQvxvNpik7AZdWNrcu0sEzWGEuBNk0nE5VEQQneC81rXHKtWlurT0VVi5EvU8xLy2Gp4C6bo7522II2BWU6UW9CsID3jVLF0tQhBVp070rHTN8rcFIrBRUAZKdP2j9ctkpVRPVFgSTos1pUQwkuGbo26mmKGH8bV5/l/fIHWw59e7bLAID3/pPOuUd3un2FzCqrrLK7atshM+fce51zf578vfdOjvNAI7NeC1h4PMPQZYa1p2PdTHeFxEGikUyhdSKNxgyDBnmsqzj/tM2SB//IiKRywgrd+SOxdmGY3ZWaolQeIQQ5s1O9LoW6hdboIM6EZJi+k6cETM5qA9epKnqVTmrWtQwViwDkDMNnS0SfJJLW+HkggCZoMTijRWYlPSFMpv18G0Rm0mxT8KOjqlYJRUaO5tosU8RIPpVOWGs2SZv6/9v70hjJrvO689XWe093z9KzkjMURxQJySYV2qRpIXFkBaKUxI4RG5YS2BIiRQkQwbIhw6ZixHACG1CAwIscwQkhy3ZiRpYtCxYhWJEVmoRCyFIoSpS4z3AZzsLZp2d6erqnu5abH985992qqZ4Z9ixd1XMP0Oiuqvde3Xer+r3vft855xPVQJ5kktUoOa45TYaihHng511ih26RQUtpZFxvL+TIDEBxjN5v9Gwxfs1HpF5UO4jOQuI0K081tLhPifIpuu3iaNGXU/ScSFBl9Bu7VjGaa6XFJ0WqZ9slaCJ1t3ZujtuWT1FOpp4XihZ5HqLVlJPvaXnWo8NzNyUdm64Ay5kzhhAeBPDgVXkT9PnFLCMjow+QeWaXhjWAoeMBiyw/pzSC1qadAIBQ8ecGD7J0LudTykTmdhR3n0F22pHUI7VSAYBSIhCuT9NHXrmHhY6+hMr3NC8kHtZvd5/32usUDA9SGL7Jo4CFTQVpduA0xegVkRv9uLXjJGAmpNDy+XYX3dpeyq/UI3SqvZ8mkMhwjnunKFNuRuNO7tjxXHh82SqJ8Fk53t5PEyiioJboKKRZlEjNiDk7oMjPqbO5okJRMzj+kIq6JaWSLVGSbwQ6XFMZ5ZQ6aBBRjsXIO5XF2SmPKBdv3+ZDe4nuvZyn1gxF8D9wa3EaB0WdEO2Cc8xcYCuVcCmSf91XAy2J7Cn/0udhqZxJ9BB9NuwYZexWrmgYKD7fSO1QRMnPo7zVo7iUdqTOTZsf9+O+hivD9VIA5JxZRkbGtUUrdP+5BMzscwD+DsBtZnbQzD50se37OjKDAaEMjLziUVdK1jy/we9aIweZZ+EdNjCvpuilOlfkOkSSjaTWeTW4bDdVBADb4PmP2MlJuRTtq76QyV04Wrbwzn/m7Z5rEqlQuYWQ3GKUGzu32Y+3yO5SG2naWEmirBZN+0pnWb2UTEd+713yX4psWrLfUV/RMZI1ZxIyqO7quovH3pe+bWn2QtsgvbeiOuWalLtpG5E6snMeYndvycC6yGsuyJmNtI8l7VivzyRKzzQ2zdNp/46UJoo8qnJVtade9cf6fJXTqihKTaJREYNFiB1oF6mnedRoLjDsEVhZUiIRblUVTyLYwiiSOV3aEbX0OcwlRO0lnqPmlOMOyt8pZ7ouMZeUtOpqNSJZeTXz/W9k++semZnZDjN71MyeN7NnzexjfH7KzL5mZnv5e/JSx8rIyOh9XAHP7A1hNSKzBoCPhxC+Y2ZjAJ40s68B+CCAR0IInzSzBwA8AOBXL3agykILU8+dR4lmeLWEM2Z1df5mTkLyFsqMJAOqnCvu9mX2vowVvJjvYgSSVpS4ycJGv+vWb/aoaN0LrCgeZG/EJNfR2r3Ttx1jTmuW+TBaLzcHfdvRfUUEqArk9tPtcqylCUlWijtqFNWrKrjDLW5KiniYP2qNFLnApY0+7sF9zGHJeJG5uFQOZKPtNsqS8DRudi5ZkyaEQ3uLvpA6e8W/MQrS64koOuY8OR+dtjvR/DF9XkJwVQU7OgtZaoTIiCbafivSrDHaYs7M0mqmfndy7JR3Uz4siXpNQvwOXptygjZYnLMq5TH3qWiRYn3x5c7tLmRfg68zCqyxyl4lP29RkX2R85vf7ec0vI8R2RGvpJoiPpkHjCVjkkTvKsmQrleruesemYUQDocQvsO/zwJ4HsA2AD8J4E+42Z8A+GfXe2wZGRnXAGGZn6uMVc2Zkd17F4BvAZgOIRwG/IJnZpuW2ecjAD4CAANDE1icqqIy41XAUnJHrXZYOYv9Pb+dEZmqgqeSaIsNImJFTHcv5T4SC6A6+x0O0kq7SvXAwjZGJyUyw5NelY1RdlOnBc1cB49n8glGNIndS7SbFvu97FFQi3fPyulk/DKC1DmrazsF4VIGpLZBFYr0YxMO9ZJUXufkqeL4ipwUTTH/KHPDyjlGTondT7Sp6exNqTlOVBvRgryjIhmhzyHdRxE3813WYSNkafMVRdoav47DfJRyT6ktlLho4mNZFI1z3+EuXCydMyPM1tHjbc+rsz1QcAPLZzm3nPfFzVRK7PH3Hd5bfA6yfFd/T3H2ogJktPieDh5RdZTM/wm+d0ejltKZIud3fqdHgXNbL1QdrAR2ncwZV62aaWajAP4SwC+GEC60HFgGIYQHQwh3hxDurgxcne4xGRkZ1xCtZX6uMlblYmZmVfiF7KEQwhf59FEz28LXtwA4ttz+GRkZ/QMLoevP1cZ1X2aar1X+EMDzIYTfTl56GMAHAHySv790qWOVlloY3n8uimhD4vlkQ0wIi7jI5PXoHk90y9/s3M1FwnieflAbvkcP+gMkSHLiUwlL7RDJkgzpmxR+l9mzMnZvWiw8uAYO8/rMRPN4o+P2JNLoZEENiCRWkU3prVZtqW9n4hqrJZYSuBrvywf8aS5vmkcLj/jKm13IruVOdJ7lWErri8RzlDFpCcp5qZz0MTQnhttfB9qIzClESA6Hi3uWZDnlW3d2bMtEuiQ4iV+XOjUN/N2LPkQtK7nUTccSBfdaRp5u7yvaGOeYysWYaxx/LFSIdiE3XRVMUufZhC4DAHazE26h7k9JUSgSXNU7VfI7fo/qO+gZV0+6M7FHhMjP5cM8Rgf9JX2v+L8xLHIx53LC/y8WthTL8QPv8n3W7cHVwRpWAPwogJ8D8LSZPcXn/j38IvbnJMbtB/AzqzC2jIyMq4zrVc287hezEMLjaNcKp/jxN3SwkqE5XoMF5s7GEo9+JrljiZv0hNakP16c8Dv27M3FFDSl7d3tx5uo+12xTvsadf7xA5I+QLuV6oxHSmWKxqOYO7VuGW/vKl2WIFuRlGgEyYcvom0knfL5ssi+XeRSSnqrQ3qMAxhtlVMCJn+3aqQNqB8kk/opDUJ38dJpJsM1NkU/pfY5SRHdaFUI6Eaa1fhTJ1YkRRwm7MuzRbQ1dJhHmGonQ8dIKbEwii6uOr6oKvw8avs8cmqtK6J19RsIVZ/T5hBlbOxBGrtaJRSQKIPjOc7e4fM9riiVPv9A4Q4rCo4+LBkIyCwgTdArmm3e6rI4zXfsAZBEw7E3qAjZg+1FjvntfvwTb0suBWx3tni1mJ65O1NGRsZaQO4BcBmwxTpqe4qmfs2thamehMBnb+Xdt+p37iolSrM7/NTHXyssYoaYexAhVlSKxggjmvkLKQOSsZjydh15qzT/pTumSL6Keuxse/l8/o7CwkXGk+XDXppvTjBqYF4nNSqUiFuRQYyc5CMvAX09MXSkzKuivKBkXxR1I6FZqB/nsN5T8iIJzfe5WDolriqqqt/CjtrssC2qQxpZljfy81M0u0y+Dcn4ZWzZIl2hfEbSJEqW6gkZlH0rG6S+lBnNqZO8xl06UZhLaixhnFEdSdcx2uiwFQIQpUGL2/3456b9GItjnn/c9FgRxSnK1XdBBpfKkdbH/HOo7UvIv8y5ima0NMpIn9Kl2tmEDrTTz2nmVh/34EmOm1NbWfDHWx9PIktG2Oe2LLeAeoPIkVlGRsaawBouAFxFGFAqFZWl5E6+OO35syM/4XfdDX+jKpHP7E99+DEAwGO/cl/cp3KA1SZFOBvU2Vx5pCJebrNuRpJbEvFTY+kijo5C50W+D6MgdcMRqRZIupNX/W6saqZyXGFTQcCsHGVUxaitxC5DymlF6Ux6p1QuTtFhR3/L9K5am/HKbIPzoigo5qCGuhBIiRL7WdZ3urhe0rG2zuCaM1VNNU+NDlufxHCxdNbHsLSRZGiaQMpmJ5UOYZNHfmWec2uny73KRyn3Yi4q/d+TjKnMMVVkx62oWkTlhJwrCyGZHWx6ghbbM4zCElKucldhPy3TKXJXbqtCo4FI0gUAvibL9JGXaBYw0i7/AoDGEMelIvV5P7vxl2nKeI4V3WMFKbe13QnfE906i60A1ro+68w+v5hlZGT0PHLO7DIQWgiLiwgVv5spWgGA+ojfijZ9xe9WY6/5HXxx0u9qD33lHwAAtlSLyGnpTczrHGcTCMpFBk90VIuAopEG8yyxH6SGxjupqndtw1b/R0YIJUZOA6+wF+buQsk1P+2vTTJ3JilShc042nhmMuKTXCmOkTkzdq1uy++oR6VyQ3N8TWNMeHKlnc6XaowxvyP5T4kV4xnlqS7kOWlMzXHfd+Z2H9PwsSKyHN7HpiqSpalCqRyW8pBJhNzcwGiWDWuiZIgGhq0TiRxLuTYZH3IOFX1FuVpyzkZeVhhQn9Ry2+Nzt/qcjOwp3qcx5VHi+B6eDyuKscqZRGbi9+G2m31fVbqZi60dY+U4sRFqTvp3rrb/RNtxY0U0wTCr0sMHuS85euITRjnVaCL7Uj72cHFOV4LrJWfq74tZRkZG7yNfzC4DlQowvSE21pDdDwCs+ybzX6qWMRdUPeR3nal1W32f84noes7v0M1JNgJhTkh5BVUsgaQiSPvn1vn2yCBW3BLRdGzQQdF7vNsqp9D0sdVOF5HB4GvkrZEF32KFNXLU0uYkc+1V0Wj8R6VBfZNHULXUMJJRQnMdz5md1WJ+LYn8Wh3VxSicX2q0PZ9y02Sno6hwcK9HD4PPMypKmrdElNpVCNjs4vpYjU24Y+c3soXdkO8zosiGPLDUaDHa63DOYlVZ35HYXi/lXDGKY26syeqpopfRb+/nMZPvUWxlRyH4ehohHPVIJ632llQN5eer6KrVYXjZ2pg066ECILL6+b1SnrCU5O/KHNfC7UWFHAAqZ9vXfkvbClJZ7QBtvztE+ytGvphlZGSsCeScWUZGxlpArmZeDlot2Nx8TKC3dRKS2JfdtpXcbVHyNHLEX5egFyiWHQtv9ZL9gGRFSpKnSyK9F/sRlsZIzpVcR6F+Ik0qS9KjsFuUCSXuldhOlnOzP+iUjLG9PrbFKV+O1DimtN+hEuciyR6/x5PgQ6d8qTH0OjslTSRurCLLMkG/+GZfjrToXjq0UCx5JZtpDpEkK/8ySn4utpyIHcZJXI1i6NOJ+xOXdFoGmuRfmicuL1uJX9fIc07UXbjV52nuJj/3sVfYZT2lxuhctawsFYJvABd2F0/GEpe83KUQiPOJRlI00OfAVEOnNU2UZwForif9h8Wr0ixpNeqnwPF3pbDwOxe7oCt9kFBkVBwQrUaQQF8uylqmA0D1KOe70Z4+WDFWuMw0s/sB/B68telnQgifvNj2uTtTRkbGtUUI3X8uAjMrA/g0gPcAuAPA+83sjovt09+RWaOJ1qnTKEkGM3+hJAOMvHRXV7FgYD/L/Ulk05ryu2R1zu9IIqwOsut2JJYiSXJLtM3IQ513JNcpJb75YbGdClAS9YDFiTNvKZK8wsJ6H+/wYSaIFxlx8FgqCADFHb9Vk9jan5ecZvCYH2txohhT7SSdbOd8/JWzHBuFzaI+AMD5De2k2IEjpIuwwBAdYpNoKLrHaoyyZlKENp50BRL1QiTcaLdDmREJsqWk4KACxuDTbnM0uIHyJlEekmKBIpoorTrEXqEd3ZPSjlrx+8EIO5KltXRSf9S0ECP7Ia0O1JdzophLoXzI6RWN27wgpZWDHfEkfLQT2lhYMamQE7vZSwrVjbTM70T5SLtsqswo+8yt/n6jB4tz1gohFrWuFCtbZf4wgJdCCK8AgJn9Gdxa/7nldsiRWUZGxjXFCs0ZtwE4kDw+yOeWRX9HZtUKShvXozlJa5qU1Cq5ibreKHKS0R/vuGn37fpNTgE4fpffrYZO+C2lskBaxEAhZJf3fuxdKEKkOlJfxMOpNMZoRJ1+OIaxl/zx2d3FHXzwpzwndKrpMqCJl9kLgNKY5roiylra4vtVzrF0z6Bk/dPtxN1qQv0ozZN2EnsmMLJk9NYcLL4ih9/hB9x4p4+p8VsSQTsNJkp80gh5mxOAYzemU049kMwoHX9JpoPD7XQN5Y+UX0p7GBgDitin9HWXJJXYn6CVRKGyXiqRwrO40z9vWTspwkRiKBBze4zwlrZ7hFSRlIvEaktIp0GRWLkjVpDwfzyxe490GrSNIUa5skpKiLYVyoya63yeJPBXrqy1rss5k0KkCDlUKaEb92eqs0k0qj6ropCcwJVhmQtX2s+DeDCE8KBe7naki71Nf1/MMjIyeh/N7utMXrge7PqiR2I7ksfbAbx+sbfp64tZqJRRny5IkeVjRVejSFrtyCPori5hcn1Xsf/ihN/F6ryxrX/WjyEyrexagIS4qNyZcjPTfrcvd+loHsetat1Cu5A3VrBCEZmdeJJdz92HD+te5Q1Lxy0XlUlFGKfu9LzR8DFKn874+yxO+7b1sSKPNKwWobQRr73muRpVPMtJWX3nl5hL+Sqjnir7fU7TSmehw6wRiPkS5fZKM+09MdvyXwfczilGcTKZnPVorsQ5rk8X81M9eLRtn1h55j+Qvfha3DZaDIlcup9RtcitlBSVThbDj5ba/J41B0jKpZypRDPOViJbU9RpjMA75Ws4lrwBI6/aEUasivQHZW7A/OdsUs1kzrVyjhGsup/LirxafL4NdhGr7iBp9pwfvz7q+wzM+Mw1hxMmwFb//lS02rhGkdkl8ASA3Wa2C8AhAO8D8C8utkNfX8wyMjL6ACu4mIUQGmb2UQBfhVMzPhtCePZi+/T9xcyaLbQG2vMaAIoJZGShPIJyEue2+J2vPloszRXJjPNmroqn8kltjSqY72pRBiTRcoyu1GE7vWMrSuSdtLnduVGSsOjuP/Zi0jeTppKnd/v7Hb7XI5rNRi7cseKOLYPCJZ5Tk9yl4X28+7NH6MDRhHulphgnGUIxiiid4DaJeaLRkqfC6p+qcy3x73ishTclJpmcXtkSDdVpE31itm3MQCEOX07Ej+Me0dSSamlLvTQ1JlUSFc2lfTN1XOW31OyGkWSJwurF27fHXaonmE9j/9Ohox2icX6WaS/MogLaajt+rICmsjBFkBL4ax8J2mUDXuyBoHytcouK9GkNX54rKtxN5oHjFNzicyxf/okXPb9X2V80lmnQpunsm1ldfwZXhhX2AAgh/DWAv77c7fv+YpaRkdHjCFkBkJGRsRawTAHgaqPvL2ahXIpl8nC6S2P0rU4NaI75smBp3EP+Gl0DRg4XCWglRUcPkMbRIUtp62fJAoD6Y2qJK3KuErm2dbrY50y731T5ICkN7BoU+wkkZMWB03R1Pe/LzMk9Pt6zN/lSYvbmYkmx/mlfMmz4vh/ntff6MmTjN/312KnnIjKVKMeSD9lIUeZvkegaCQczvqwxJatJfxl6JfEQ45JwaQfJxfxeS8IVfcKKl2BnSW2Q7Efut1pCJq4cpU2+jJKDRIv7Sl6WFoAaG0ldobMsEsIuUJB2a8eLpfv8Tb7PyFNOeVIyXx5xgY4YcgUBgNa4n5skT0EpDm6Typnktxa91jqdfrU0TYpFcSktWsckl7iSeyVzWp6T04if2yDJs5FSoiV85/sC3ckRK0F2zcjIyFgTyBezS8OaLVRmz6M54neVpXtuja9JBL04RRfXk34HWpz0u9fEM4wqDhWJzyoJnhL1RomKErZnizt2YAeeGL0NKmrw3yLyRo91FDSNmLiN4uV28mzsaZCcx/hrvm1t1h8PHfU79bltBcH05A+QTsG3nHpWQnn/pd4DKWwLXW0ZNSqRLblRWw/GUSbkldCWX5p8xkY6zguIErPqYb73Mdb5tzlVwBJqxom7PXrb+Hi9bd+gJLnm6XxCd1ExgCJ1/eNE77jkXCvH6Gcm3/0RRuvrPcpS79NWSlOosNM4Sbh2yKkg8iYT6rs2J/twnCLh8ndjPecvWXaVFOHNczUgYb++K5r/pP+qDcrbTsWn9g7qad/Shc2kiXCaR16gz59IuJIunSo6UpVonjDS3Imrgnwxy8jIWBPIFkCXgRCAxSWUSBI8P1nckWq8oy6Oy67Gfw+dYBm7UzYCYJFEWmMX6YGXKBPRXSy1hqE8JLDsrFK6to09Js8Ubp3GaC7eqRhVyMG2tcFzco2JIs9T20930rLTHUSMVa4p7ZGoZqsSoy+RHLu42SOCoZMkcSYe9zgusbVHlIreFNmUK12+IpLYKLfFx1Eqc7K4yweSiEUbCBJ109dfrrsAMPWcn5OE1I1RWhi9yrnW2ycSNDnihprPnR22tjF1ywXN7/Yxnb3Jz21yD/NgimjSjlTsgNRk34OqjAPqkib5+1dOJr0YKJmLUafE3ny9lMi9ovutcq7qecrvhigmqeurjAoUrZUmJ9rOtZxEfgP836gdZCQpYXy1nc4Uc4woVhCl4wkJ/UqQI7OMjIw1gVzNvAy0WrBzC2iyj2NtLrkjnVzkb39cPcxoQXcJmeolomaJbSW+jsRIIpxICLAdhEVVPJUvGd7PHFEiNhZxVxIYMCpRFbZEgmrtZBFhLr2JeTySNpXjW7pzlx9+vsg5nZ/yj3PoGMmrG/zx/EZ+zLc70Xbo+aILfOzHyKqfqltWunQpS0RVHSONcuM2ikKUX+N8BOV9BoqvYGlOMh1GqrtI+FRlNR6rmB/1ipQgvrpEWyX+Ay1uLyrQc1uZW6W4+sw9fryhkz7uZo3Rabk494FZn/fRPYwOz7IibcsbzkgyF7vDM3pTxN3cWkRB5QVKzmZYKT7N4+t7pShpqvDoj9V1baPcLnNoqqYCQKvm41QlVwYFkfTNnF0j6QGg7mToyMWtFCHzzDIyMtYEVqgAeKPo84uZdzSvHPF8zPB8YsPCfIIsklVpq0/67+ox5iKGipxK6ZVDvo/4RzI7ZKUnNfFTZBY7Bck+W5VQ5i9it+90HxkWKkpUD0PmRyyxewnmPLVSlGfRJHD+Qq7YyCFKkZYky/LIQ9y6/e/2MW2vFrZQIy94pFenuLiqPJ7yMkknITBvo/yNzPuULywnnLQIcaqUG5L1kiLAlHvXIZgeoPi6xV6PZVU3UyE7j1thNyaJrMXlataKCKrEHpIbvufjn9zr77dEmpbyqQPHkgicVcaYN2X1N77PYvv5AUl+UBEZq5iyxpZAHyi6JOl4MggtzZAvRwPP1PCyxe9aWRxG5TAVbVWTc6Y5aeTWEYERYIlzXZ0v8qiNjT6Gyok5XBXknFlGRsaaQK5mXgYMnj+RmdxcUm1UlNPRm7KmnAQjNkuoVy0ZFEqQrAhKEUey9o8RmThLEjzTFlqRWkiSnzL6i0aCZP4rxxE7Uie5Cgm0W8zFVRsjxAMAAAqDSURBVCicrr5+kW7TvIuXVaHc5ZXQoeP+/sfuLiKb7bOMyA7S+ue053kk4K4k3Do1aYmKBb3Ac40C6/NJtU7zw8pnYMUWDW6b5iXFJ1M0y/HHpiSqmiY8MzWzaYyTMzapfXyMskAHgOGXfc4aG2jDXef7NWkrfpAd2WeKL0XMT1FUH9hMRZ9LZPUvLG8xrUqn/tkUqQFAY4xV6fM+zgYNF4Mq6vucl9eaLITsJeXIZF00TfE4VSmyAweAxnS7NXvMN6pJDD+fVDVQPcIqZodF1YqRI7OMjIy1gJA4r1xL5ItZRkbGtUUuAFwGQgCW6ghTJEyeLpLJWu7JBbXFBGvlFaclxKXewoVkxFa9vSRtt7jNa+rgqcSwHEBtM2VBWqqShKjlLoALBN5aTpXVhUjLtKQAIGdTLWuaW3xJUX6dnJOL3PXCjC8XBpmwv+klUhN2FH5jDfbALMs3/qjLXeTKqiUlUEi3onyJ4m4tvyMRc31CIzjTvnQuneZyUzSLdUmBoUNQHpcnIiiTgJtKrJRsr9Qp5Zrn0prdmcqzSeqB81t95Yg/wYKMNYrxAgkptQuMTq2RKCw5W9s8kZjdUfyYe5tLnpZGiwT9ur38Dhz382gN+jlWzpLqQ5qKnSoS+Av33QYAGDrgy+EGCb1ljqmUyqVYDBIRWYWSpS1+zvp+La4rvtvr9rIn68Hif+OKcJ2oGT3VncnM7jezF83sJTN7YLXHk5GRceUIrdD152qjZyKzpOnnP4I3M3jCzB4OISzbJw+VClrTUzHxmfbAXLrJIxjJgeQfH5ProkmcKaKw8pZpPsfiARPeTVIDYhdrFGLhSBi1DpIp78opkVQOp5HAeI4yGkZ86l3ZVuZvtlMyRA5tsPN4SN534Huv+j7DFCCX25O9GlMkRQIIW30s6olQeYYRlOQziYwmdmmXzVGHtCfSB1Lpjf5gVBU7F5E020y6J0VhtugbimwkqdK51i+kpQiL00z8n5aNU9KZipFkYG9NFS5qjG5bjK7aPkkVivSePEdFyCV9V7oUABSpqmeBihEDpy6MVPQ9Kouwze9E4y3ueqsICkikeIzaqurUzsepKF2Fo8YIScXy/CdlpVXzsx09mHTsUpezblK2leAGjMxi088QwhIANf3MyMjoY4Rms+vP1YaF61Q2vRTM7KcB3B9C+DAf/xyAe0IIH+3YLu2191ZcuUP59cQGXHmvm+uFfhor0F/j7aexAsBtIYSxS2+2uuiZZSZweU0/0157ZvbtEMLd13pgVwv9NN5+GivQX+Ptp7ECPt7VHsPloJeWmW+46WdGRkaG0EsXs9j008xq8KafD6/ymDIyMvoEPbPMXEnTTyzf2r1X0U/j7aexAv013n4aK9An4+2ZAkBGRkbGlaCXlpkZGRkZK0a+mGVkZKwJ9O3FrJelT2a2w8weNbPnzexZM/sYn58ys6+Z2V7+nrzUsa4XzKxsZt81sy/z8S4z+xbH+nkWZXoCZjZhZl8wsxc4xz/Sq3NrZr/E78AzZvY5Mxvspbk1s8+a2TEzeyZ5rutcmuNT/J/7vpm9fbXG3Q19eTFLpE/vAXAHgPeb2R2rO6o2NAB8PIRwO4B7Afw7ju8BAI+EEHYDeISPewUfA/B88vg/A/gdjnUGwIdWZVTd8XsA/ncI4S0AfhA+7p6bWzPbBuAXANwdQngrvLD1PvTW3P4xgPs7nltuLt8DYDd/PgLgD67TGC8PIYS++wHwIwC+mjz+BIBPrPa4LjLeL8E1py8C2MLntgB4cbXHxrFsh39p3wngy3AC8wkAlW7zvcpjHQfwKli8Sp7vubkFsA3AAQBTcObAlwG8u9fmFsBOAM9cai4B/HcA7++2XS/89GVkhuJLIhzkcz0HM9sJ4C4A3wIwHUI4DAD8vWn1RtaG3wXwKwCkCF4P4HQIQYruXprfWwAcB/BHXBZ/xsxG0INzG0I4BOC/ANgP4DCAMwCeRO/OrbDcXPb0/12/XswuS/q02jCzUQB/CeAXQwizl9p+NWBm/wTAsRDCk+nTXTbtlfmtAHg7gD8IIdwF4Bx6YEnZDcw1/SSAXQC2AhiBL9U60Stzeyn08veiby9mPS99MrMq/EL2UAjhi3z6qJlt4etbABxbrfEl+FEAP2Fm++BOJe+ER2oTZiZSdS/N70EAB0MI3+LjL8Avbr04t+8C8GoI4XgIoQ7giwDuQ+/OrbDcXPb0/12/Xsx6Wvpkbm72hwCeDyH8dvLSwwA+wL8/AM+lrSpCCJ8IIWwPIeyEz+PfhhD+JYBHAfw0N+uJsQJACOEIgANmdhuf+nEAz6EH5xa+vLzXzIb5ndBYe3JuEyw3lw8D+HlWNe8FcEbL0Z7AaiftriBp+V4AewC8DODXVns8HWN7Bzz8/j6Ap/jzXngu6hEAe/l7arXH2jHuHwPwZf59C4D/B+AlAH8BYGC1x5eM804A3+b8/hWAyV6dWwD/EcALcKuq/wlgoJfmFsDn4Pm8Ojzy+tBycwlfZn6a/3NPw6u0qz7H+slypoyMjDWBfl1mZmRkZLQhX8wyMjLWBPLFLCMjY00gX8wyMjLWBPLFLCMjY00gX8wyrhrM7N+a2c9f5rYfNLPjZvYZPv4xMwtm9qFkm7v43C+vcDyPmtmcmfVN85CMlSNfzDKuGkII/y2E8D/ewC6fD2wtSDwN4GeTx+8D8L0rGM8/hPPRMm4A5IvZDQgz+yH6UQ2a2Qj9tt7aZbt/St+t75rZ/zGzaT7/KTP7df79bjP7upmVzOw3FEWZ2S+Y2XN8nz+7zKHtBzBoZtNkzN8P4CvJeB4zs981s2/QH+yH+fyomf2RmT3N9/vnVzZDGf2InmloknH9EEJ4wsweBvCbAIYA/GkIoVsz5ccB3BtCCGb2Ybizxsfhwu4nzOz/AvgUgPeGEFp+/Yl4AMCuEMKimU28geF9AcDPAPgugO8AWOx4fSSEcJ+Z/X0An4U3gv4PcGnN24Ao8M64wZAvZjcu/hNc43oebiDYDdsBfJ5i4xrcRwwhhHkz+9cAvg7gl0IIL3fZ9/sAHjKzv4JLji4Xfw7g8wDeApfa3Nfx+uc4hq+b2TgvlO+CL0nB12bewPtlrBHkZeaNiykAowDGAAwCgJn9lpk9ZWZPcZvfB/BfGfH8G21HvA3ASbi1TTf8Y7iO7+8BeDJxibgoggvJ63Azy0e6bdLlsXV5PuMGQ76Y3bh4EL48ewhu44wQwq+FEO4MIdzJbdYBOMS/5aIAM7sZvty8C8B7zOye9MBmVgKwI4TwKHxpOgG/cF4ufh3Ar4YQml1e+1m+xzvgS8szAP4GwEeT98/LzBsQeZl5A4L0iUYI4X+xn8I3zOydIYS/7dj0NwD8hZkdAvBNALsSe6NfDiG8TirFH5vZDyX7lQH8qZmtg0dNvxNCOH254wshfOMiL8+Y2Tfg9tn/is/9JoBPmzflaMKdKr64zP4ZaxTZNSNjVWBmH4RbyHz0Utsm+zwGv4heNt1iJftk9CfyMjNjtbAAX6J+5lq9gZk9CvcOq1+r98joHeTILCMjY00gR2YZGRlrAvlilpGRsSaQL2YZGRlrAvlilpGRsSaQL2YZGRlrAv8f/PqzLwOCtf4AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "perturbed_field = p21c.perturb_field(\n",
-    "    redshift = 7.0,\n",
-    "    user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100},\n",
-    "    cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n",
-    ")\n",
-    "plotting.coeval_sliceplot(perturbed_field, \"density\");"
+    "plotting.coeval_sliceplot(initial_conditions, \"hires_density\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now it finds the initial conditions, but it must compute the perturbed field at the new redshift. If we had changed the initial parameters as well, it would have to calculate everything:"
+    "### Perturbed Field"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After obtaining the initial conditions, we need to *perturb* the field to a given redshift (i.e. the redshift we care about). This step clearly requires the results of the previous step, which we can easily just pass in. Let's do that:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 73,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:10:47.697138Z",
-     "start_time": "2020-02-29T22:10:46.332331Z"
+     "end_time": "2020-02-29T22:10:43.461429Z",
+     "start_time": "2020-02-29T22:10:43.079489Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "perturbed_field = p21c.perturb_field(\n",
     "    redshift = 8.0,\n",
-    "    user_params = {\"HII_DIM\": 50, \"BOX_LEN\": 100},\n",
-    "    cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n",
-    ")\n",
-    "\n",
-    "plotting.coeval_sliceplot(perturbed_field, \"density\");"
+    "    initial_conditions = initial_conditions\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This shows that we don't need to perform the *previous* step to do any of the steps, they will be calculated automatically.\n",
-    "\n",
-    "Now, let's get an ionized box, but this time we won't assume the saturated limit, so we need to use the spin temperature. We can do this directly in the ``ionize_box`` function, but let's do it explicitly. We will use the auto-generation of the initial conditions and perturbed field. However, the spin temperature is an *evolved* field, i.e. to compute the field at $z$, we need to know the field at $z+\\Delta z$. This continues up to some redshift, labelled ``z_heat_max``, above which the spin temperature can be defined directly from the perturbed field. \n",
-    "\n",
-    "Thus, one option is to pass to the function a *previous* spin temperature box, to evolve to *this* redshift. However, we don't have a previous spin temperature box yet. Of course, the function itself will go and calculate that box if it's not given (or read it from cache if it's been calculated before!). When it tries to do that, it will go to the one before, and so on until it reaches ``z_heat_max``, at which point it will calculate it directly. \n",
-    "\n",
-    "To facilitate this recursive progression up the redshift ladder, there is a parameter, ``z_step_factor``, which is a multiplicate factor that determines the previous redshift at each step. \n",
-    "\n",
-    "We can also pass the dependent boxes explicitly, which provides the parameters necessary.\n",
+    "Note that we didn't need to pass in any input parameters, because they are all contained in the `initial_conditions` object itself. The random seed is also taken from this object.\n",
     "\n",
-    "**WARNING: THIS IS THE MOST TIME-CONSUMING STEP OF THE CALCULATION!**"
+    "Again, the output is an `OutputStruct`, so we can view its fields:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 74,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:12:50.956807Z",
-     "start_time": "2020-02-29T22:11:38.320762Z"
+     "end_time": "2020-02-29T22:10:43.467710Z",
+     "start_time": "2020-02-29T22:10:43.464305Z"
     }
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-02-29 15:11:38,347 | INFO | Existing init_boxes found and read in (seed=521414794440).\n"
-     ]
+     "data": {
+      "text/plain": [
+       "dict_keys(['density', 'velocity_z'])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "spin_temp = p21c.spin_temperature(\n",
-    "    perturbed_field = perturbed_field,\n",
-    "    zprime_step_factor=1.05,\n",
-    ")"
+    "perturbed_field.arrays.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This time, it has only density and velocity (the velocity direction is chosen without loss of generality). Let's view the perturbed density field:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 75,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:12:53.928243Z",
-     "start_time": "2020-02-29T22:12:53.712484Z"
+     "end_time": "2020-02-29T22:10:43.658615Z",
+     "start_time": "2020-02-29T22:10:43.469373Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plotting.coeval_sliceplot(spin_temp, \"Ts_box\");"
+    "plotting.coeval_sliceplot(perturbed_field, \"density\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's note here that each of the functions accepts a few of the same arguments that modifies how the boxes are cached. There is a ``write`` argument, which if set to ``False``, will disable writing that box to cache (and it is passed through the recursive heirarchy). There is also ``regenerate``, which if ``True``, forces this box and all its predecessors to be re-calculated even if they exist in the cache. Then there is ``direc``, which we have seen before.\n",
-    "\n",
-    "Finally note that by default, ``random_seed`` is set to ``None``. If this is the case, then any cached dataset matching all other parameters will be read in, and the ``random_seed`` will be set based on the file read in. If it is set to an integer number, then the cached dataset must also match the seed. If it is ``None``, and no matching dataset is found, a random seed will be autogenerated."
+    "It is clear here that the density used is the *low*-res density, but the overall structure of the field looks very similar."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now if we calculate the ionized box, ensuring that it uses the spin temperature, then it will also need to be evolved. However, due to the fact that we cached each of the spin temperature steps, these should be read in accordingly:"
+    "### Ionization Field"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we need to ionize the box. This is where things get a little more tricky. \n",
+    "In the simplest case we are using in this tutorial, the ionization occurs at the \n",
+    "*saturated limit*, which means we can safely ignore the contribution of the spin \n",
+    "temperature fluctuations. \n",
+    "This means we can directly calculate the ionization on the density/velocity fields that \n",
+    "we already have."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 79,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:12:59.214838Z",
-     "start_time": "2020-02-29T22:12:55.760472Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-02-29 15:12:55,794 | INFO | Existing init_boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,814 | INFO | Existing z=34.2811622461279 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,827 | INFO | Existing z=34.2811622461279 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,865 | INFO | Existing z=32.60110690107419 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,880 | INFO | Existing z=32.60110690107419 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,906 | INFO | Existing z=31.00105419149923 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,919 | INFO | Existing z=31.00105419149923 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,948 | INFO | Existing z=29.4771944680945 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,963 | INFO | Existing z=29.4771944680945 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:55,991 | INFO | Existing z=28.02589949342333 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,005 | INFO | Existing z=28.02589949342333 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,033 | INFO | Existing z=26.643713803260315 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,051 | INFO | Existing z=26.643713803260315 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,079 | INFO | Existing z=25.32734647929554 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,094 | INFO | Existing z=25.32734647929554 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,127 | INFO | Existing z=24.073663313614798 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,141 | INFO | Existing z=24.073663313614798 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,168 | INFO | Existing z=22.879679346299806 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,182 | INFO | Existing z=22.879679346299806 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,205 | INFO | Existing z=21.742551758380767 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,219 | INFO | Existing z=21.742551758380767 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,403 | INFO | Existing z=20.659573103219778 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,418 | INFO | Existing z=20.659573103219778 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,620 | INFO | Existing z=19.62816486020931 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,635 | INFO | Existing z=19.62816486020931 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,784 | INFO | Existing z=18.645871295437438 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,793 | INFO | Existing z=18.645871295437438 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,931 | INFO | Existing z=17.71035361470232 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:56,941 | INFO | Existing z=17.71035361470232 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,085 | INFO | Existing z=16.81938439495459 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,095 | INFO | Existing z=16.81938439495459 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,243 | INFO | Existing z=15.970842280909132 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,254 | INFO | Existing z=15.970842280909132 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,399 | INFO | Existing z=15.162706934199171 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,408 | INFO | Existing z=15.162706934199171 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,544 | INFO | Existing z=14.393054223046828 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,554 | INFO | Existing z=14.393054223046828 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,691 | INFO | Existing z=13.66005164099698 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,700 | INFO | Existing z=13.66005164099698 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,832 | INFO | Existing z=12.961953943806646 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,840 | INFO | Existing z=12.961953943806646 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,970 | INFO | Existing z=12.297098994101567 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:57,978 | INFO | Existing z=12.297098994101567 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,106 | INFO | Existing z=11.663903803906255 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,114 | INFO | Existing z=11.663903803906255 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,244 | INFO | Existing z=11.060860765625003 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,254 | INFO | Existing z=11.060860765625003 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,394 | INFO | Existing z=10.486534062500002 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,402 | INFO | Existing z=10.486534062500002 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,529 | INFO | Existing z=9.939556250000003 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,538 | INFO | Existing z=9.939556250000003 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,674 | INFO | Existing z=9.418625000000002 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,682 | INFO | Existing z=9.418625000000002 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,810 | INFO | Existing z=8.922500000000001 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,819 | INFO | Existing z=8.922500000000001 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,947 | INFO | Existing z=8.450000000000001 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:58,956 | INFO | Existing z=8.450000000000001 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:12:59,086 | INFO | Existing z=8.0 perturb_field boxes found and read in (seed=521414794440).\n"
-     ]
+     "end_time": "2020-02-29T22:10:44.909517Z",
+     "start_time": "2020-02-29T22:10:43.659834Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "ionized_box = p21c.ionize_box(\n",
-    "    spin_temp = spin_temp,\n",
-    "    zprime_step_factor=1.05,\n",
+    "ionized_field = p21c.compute_ionization_field(\n",
+    "    initial_conditions=initial_conditions,\n",
+    "    perturbed_field = perturbed_field\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see the fieldnames:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 80,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:13:02.551178Z",
-     "start_time": "2020-02-29T22:13:02.342852Z"
+     "end_time": "2020-02-29T22:10:44.914928Z",
+     "start_time": "2020-02-29T22:10:44.911001Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "dict_keys(['xH_box', 'Gamma12_box', 'MFP_box', 'z_re_box', 'dNrec_box', 'temp_kinetic_all_gas', 'Fcoll'])"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    " plotting.coeval_sliceplot(ionized_box, \"xH_box\");"
+    "ionized_field.arrays.keys()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Great! So again, we can just get the brightness temp:"
+    "Let's plot the neutral fraction:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 81,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:13:03.853019Z",
-     "start_time": "2020-02-29T22:13:03.815626Z"
+     "end_time": "2020-02-29T22:10:45.120787Z",
+     "start_time": "2020-02-29T22:10:44.916354Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AcNZ16ThP3JeOR6tckcmKIAiCIAQI4gbyjLiBBEEQBEEIaGRlRRAEQRACBFlZ8YxMVhQizBZE/B0imOs2fP9co8LD5Nwmw1/q9ONv5LoBvp27Ck/Xne02hATx1gxSZ2MBb2ZQvYJKHmuj/fsNXo/1x/aCCL3cyEZDrdPdNLW3w+w9nbg/Bu4x/OUzjlxN6sYlfkZsNbSWh3g6WFr5bDcNm1TZ2oba2w/S8OS6VsN3Hs71OsyOsRh6pKTRVKPii48bUd3MdclUN2Az0fvOw4pVTrqoxuDqBf/Vyz/e8TqpS7Lyz2PcSx5amspCS+uFphF7XW59vTxw+5W0vXtCiV3neUMXdPRZqkUa1o9qPiqCEQ2WE/vzFTQE/9CZaL3cs8pOUpfPwoJDTPT5iDYb9n4nHZcnXVnEptsW8LBm72OAh5lHW1g4viOb2G4Yz3QeC3NOp6ciodcNQmjKA/4t195ujMUTTG/Sd+sgYsc/xto0zzhbzN/pHhyWS0mzIpMVT4gbSBAEQRCEgEZWVgRBEAQhQBA3kGdksiIIgiAIAYJLHB4ekcmKQpjJjrC/NSTBypbmuW7qa7YxDYtd0UFwnYk/P7WaE4Fvz85331SvW5fdOT4bP6tsN8/T7Zcnk+q1KJfzpA/sTOwlL1INhUPRgHRvQjUqTqYhMsO7voKnF08rjEBJiWJ5P1SNAdeo8Ou6yikduLq1gycylTwyr6ZRPcWmNvRLsD7W6uVvb2hO6gZEUL2Fqtvin6WamY7pzqH7iT1mt7GdQ1LfbfS8UZHE3vOpIRQKcVANFNcX2VbU1MvOa07iYhBvO0PsYbV/InZoHeO5S3PRscVzI10bTHUo5DpWWneUjVOHyagPN9MvA7vJ+7YcNrYdCNfjcb2LWTk+mP3BH2qmY7x1cLJefmrfXfS6dej3Yg9ljKw9l0jqIj+inzXl5ihiv1nL+G6o/vfzYBfNyiWPTOEEQRAEQQhoZGVFEARBEAIE0ax4RiYrXlB3LXaw9Sfu6iF1LDQwlYXlxbCdWNXdbPkSLE8Vn6+k21fDmIuuQ5d+v8kyltNXt6LL9IHI/BdeI3YVtvWA2jch5/Es21la+QIl3PLQeOqKqraVHjs/k7r0RlbdqJf58joPmTYrbVbDsAFgVqMEr+1tTTevLeaSdJjpfVfdZf2rrCV1HfbRVOotglL0criZdmo2c/XYlDHP3ZMO5tpsFURdJS8paeYXbaDx4A0dtC+G2Y02H2Cp3/fkxhK78HEjTHjvB7XoeR/ciPJi7lEjnDqHpbLfVlCN2GdcRhp5M3MF/prdmNgd7b8TW92uIoOF/acUUndZI5vhBuL3g7ueVRdlsZ23/fwwqlt88DHNd0tuEmQcu6Dpp6QuzUWvc8JluDP35tUkdfe88h2xbwvbReyaFvrdUNS2SweXdil9mvJDekUQBEEQhIBGVlYEQRAEIUBwyxqCR2SyIgiCIAgBgmhWPCOTFQWLyaynv/e1zXou06zkKXYw8wnXtlBNAQ9pzXQb4bD8vU7WhqlnOil19FieGj7QOfwK1YdEmmkK+lyNbl2vwvUhdqbtUbcw4PeRh2q2tB/Xy1/1fZPU5d1Dr3PYSfUJG5Vt7m0mOiYOMr1FD8dBvXxrKA2z7XjkKLF7T3lKL29ts5rUfbmrPrH/FbaP2Op2ArEsnDNb+4vYkYpOhW9DYAbVPaiEmWj4NN8ygodXx1mNEOSWofSz3hC6h9jVlPTvHe20vemhVLvw2+eG3oWHFB/dXZXYnzeOQ0kZe3AzsXcqqe8TrHTLAkuxdAMGVwafJnXdQzKIbTdR7YWauqC65Rypy2ChzOo4djN90Wk3HYs2k6Fv42Hm/Nnh6RPU7yA7u8/8u4yMC/Z7awEN+/8pyxjHm6+vTuoeXEN1TLzNqu7mn+fbn/ZGqPzIZEUQBEEQAgQR2HpGJiuCIAiCECC4ZZXIIzKFEwRBEAQhoJGVlRLA8ws4mLbkSKHh581jS3j1bdR/zP39at4PVb8CFN8m/tdUw88b0uOQv2YHNFsGvEVs/tdEqovmkcl2G5qK2laqp+A6FNXtztPr57IcFLWtah3116/IrU3shkF023s1r8Qv56jf/c9cmvejZ6hxv3g69Op0OGH0w5/r5Wfr9CF1ma5l1HbTNueZjDblswz/8SzHT6SSy4bnDuIaFhWuawDTBaW76HUy3Ia2p4E9hdT9ybQ9NSzZenlvQQypq2WjGha1T/kWAFxLcnBOT2K7TxnPlrUm1aHsKzhB7GCTMWZy3PT5nX+mPbEfrvGLXuY5cDhn3FSXompCeC6RYBPVrOQpn5elyCFaJI6/7SeKbROh9CPPIcXTwpuV9nMNiY3pXXqG/amXc1dQTUotK73P6hYSRecyHphLUasiewN5RiYrgiAIghAgiGbFMzJZEQRBEIQAQfKseEYmK15QwzHdGg8FpOv2MZYCpY6eh4fZctRlzHAWopfhdhB7aNLPevkDJPk8b6DDQyb5cvTNnz9J7KRRRmjzCwc3kbqWQWz7AyWMmC8he98oAThcGEbsOrZ0YqtuH05T5iJyRFBXiLps7yssGwCuCDbCez/u9S6pizLz89LxFa7shBtl5iHe1C3hKw07D/kuVHqOL70Xsl7dVkDDhh/5aYBe/vzG6aSuBdt5OEO5bLqL3o9YJQQaoG4T3v72dura6d2Y7vZ8PCFKL0cH0WMdrI+7BBvh7U7mAXssZjmx013GM5vmop+NJ0MYduhOYo+M/0EvN7XlkDobvG+HwN2gvnZZ56n4U110LMZZ6XvVXeKLhzUztxD5hPQHl+82X8dqjMUR0XSH7+LQNqnXMfv5fhUuHWSyIgiCIAgBgku79HQ45YFMVgRBEAQhQBCBrWekVwRBEARBCGhkZUXBpbnh+tsta/ERhudkYZJqmnyeip9H1vnSsJwopL7yR3//D7EbDKBpwCsb3x333v7TTFuialQ4WW4a0p2rZRDbqYTzprqoTsPO0uL/lNNEL2/ISiB13aL2E7tVELXVcMwMN/XJv3rwFmInRRjp4F+K+57URZnpYxiuhJ5WY+HGfAsGHm6tDjgecs91HfS8vvQH9Fxcu8B1QRkuqrWyhBs6iRiWRt7G2hhnMexHouhWAryNql6h+Gej9jkXvT/HJjXQy2tupu+M65JB7MaKHinaTLVVoaD2QUVn9s1ZGr4eZaHamJxC2iY1RPqH3DqkrlMwTUF/uDBSL1eznyV1vsJ5eT9FW+i9tLJ+U78H/alD+JgpKXyc8vbz7RxUfG2LUllxSzSQR2SyIgiCIAgBgriBPCO9IgiCIAhCQCMrK4IgCIIQIEg0kGdkslICuM/U7rPbqP8+3c3SvYP67LOVgbn2XCKpC0SNyhfHqJYkWMmX4s/3rGod+NYCff4cQOwIHPDahvW59YjdImgjsY+6jLwMP2a3IHV/ZFEdQWa3M4pF82JM+PgmYre+8j1ixyp6klAT9f13q0HbP2dVF73c9+empC54CE3v/ka9L/VygpWel6fBtzENjkXp82Kp033kTuHkaXy7A+PYjzPakbpfT9cn9i0xO4j9egfj8/A8RDlcc6DYMRauN6LjS81X83EmzTv07t6r6HUORRK7/jdr9XLDb2gTPhl3PbGb32fkvXGzVPCRZpq3RE3H/+ZWep7vuk4jdq9Gx4mtprYPNtE6dbsJAHjpsUF6efq7dOuKhr6z/BMcLP9JaSiem6fkP7I0V4qZ1fnezoHoaP4uWy6h33dJCucZ6RVBEARBEAIaWVkRBEEQhABB9gbyjExWBEEQBCFA4DvQC0XIZEXBYjL7jOlXj/OGHdxhTH3/TlY78tBdRt01J/1eO9BQc19wTQHXRKS7DI2Hje1nMqfpR8T+/A+qi1jZMkQv3x6xldRx5cXBghrG+9IakLqH41cSeybqwhsN7qeaoaN76Z43sRZDa+JiXzAOM9UuRewx/Pun2lKf/Cd1FxC7upLLI5/pADJZrpFgvi+Mybtggd8P9d65mE5gVwHVMjy64169nH02hNTVjTlN7KtD9xDbpugTglh7HUz3kKe0aa+T9uH0tGuI/etx496FLIgidc4kep3IdmdQUuq8sJrYI6r118sNHltX4vPE3E3zAaErNfleTbfWomPeF+lfG7lVblk5lNT9cT3dT0rVhDjZGOCalWI6Jp7Gp5xwqfmBTL5zpVxu+//IyopnpFcEQRAEQQhoZGVFEARBEAIESQrnGZmsKKjp9n3hyw3Ew3UjzXQJ87SLLcUHoOvnqQPb9bKaAhwAct20/SeUNOZ1rTQ1fB4LOUxzG8OtiY0ugVcx0357qAp1wexba4QRP5nYidS9e2QVsZ/b2Fsvx1bNRHnx9KJ7if2fm5bp5cxCmmI+3RlK7I79t+jlSXE/kzq+FO/UDJtvQ5BXLDyUhzZ7DwndUUCPPV4YrZfrsJDc44VViN23rhEefjQvmtR1j/yT2PWtLNRf+Xz+3Kw2zRhvNhN1oT5SfQWxVd/+9c/tJHUNgk4RO5eF/vaf86BentCeuuGCmQtvwekwvXzMW8M9kNWXhsI/ltDVy5Glp2bvXV7r7kQnr3X+4KkJHD62HSmJy/wf+FYJuep95qHLbAuJCPPl5QZyS54Vj8gUThAEQRCEgEZWVgRBEAQhQBA3kGdksiIIgiAIAYLsuuwZk6YV22P+siMrKwuRkZFI2ROPiPDiA8XXtumA723Kz7hpev3tBRG03mX4wz9oSFOGVxTNNxmfr5EjhdQ1ttPU8LUsRghlnkb76ZXjPYn9cM1f9HJXu/cU2gCQyfrt7tqd/TVbZ9j+3XrZxkIxq1pyiP1MUocSn5dz8gkjhf4N964ldf+p9hux4yxG34QwjYqv8cRTj+drPPjdO7lMJ5Diovcn1mLU+wp55vDwV64xsJu8/w1kYaHLLvZe9dxcy6Om1wcAp4/nzsb6NJ8dW6BcN9rMNEPs82UqOq3Bdbp5vealyOLjm/Ry8e0bSn7f8zWewsHoU34f9ztpyHcb9l2hfh//83xkZbtRo9ERZGZmIiKCfsdWNt7cdaPfYx5v8uNFaElgISsrgiAIghAg8JxNQhEyWREEQRCEAEHcQJ6RXhEEQRAEIaCRlRUFp+aC828/bLbb8LFWs9D04nzL8nOKL53rBA4y/2sjG837EW03tBk9jlF9SGl0GudD/i1XELtXlff1cqiJ5/mwMtvwH6/IbUjq1uyuR+znay/Wy27Qfsly5RK74+8PEzsJf3hsuyeqmg1dSrQlj9Q5TCzV/dHf9XL/eN95MA7NbUns7ztP0ss1LVT3YDPRz8dzVPiCaFjYWOMp2nn+CpUwpttIsNJjs91GX5wupgeh53IozfevO3F6reeKmxSXnbXJ0HA1YM8Kz4FkUZpxopA+o3ka7acsN70fnYLT9HI6y2XDtzBwmC7fZXl13OYy3YnNRxp8rrXaywZUbasxri3s2UiwUr0aQMcI178AvvVLlQ1xA3lGJiuCIAiCECCIG8gzMlkRBEEQhABBNjL0TEBPVlwuF55//nl8+umnSElJQVxcHB544AE899xzMP29NKtpGsaNG4f3338fGRkZ6Nq1K6ZPn44GDRr4Obtv1OXqs26aRt7MloU/zmysl2cf7EjqpjX7nNgxzKWkLrPeUavsYbTng33JBmJvm5Sgl7s49pG6YJYCPUUJvd53LobUje7yHbGjlWeQu0X2OOlSb9I93t0++9+k6cSn3jab2DaTd9fIurw4Yn+Xbrh24tbSNnWLpJ+9V9jvxI40G/fSn5tHde3wUHfuyvF1Ll5nM/HtHIwldHUrBACIt9LrRpmNx191ewJAmpvtunzwX3q5SjB12Y2M+4HYTW3089iUdOl8C4BwE322jirbUWzOjyV1sRbqFqpvM1x8PLzVzBxObtDrAMbnO1JYwOqoy4KHNl9O9KzV1mvdlMN0d+q6NmNs7nHSMTDhBE1jMCl+kV6OZun0HWbqwivm+iy2z3rxXdyF4kybNg2vvfYaUlJS0KpVK7z99tvo0MH7b86UKVMwffp0JCcno1q1arjrrrswfvx4BAcHe33PhSSgp3ATJ07E9OnT8c4772DXrl2YOHEiJk2ahLfffls/ZtKkSZg6dSpmzJiBdevWITQ0FD169EBeXp6PMwuCIAhC4OGGye+/0jJv3jyMHDkS48aNw+bNm9GqVSv06NEDp06d8nj8nDlzMGrUKIwbNw67du3CBx98gHnz5uGZZ545349XZgJ6srJ69Wr06tULt9xyCxITE3HXXXfhxhtvxPr16wEUrapMmTIFzz33HHr16oWWLVvi448/xokTJ/D1119XbOMFQRAEoZS4NLPff6Vl8uTJGDx4MAYOHIimTZtixowZcDgc+PDDDz0ev3r1anTt2hX33nsvEhMTceONN6Jv3776b29FENCTlS5dumD58uXYu3cvAGDbtm1YtWoVbr75ZgDAoUOHkJKSgu7du+vviYyMRMeOHbFmzRqP5wSA/Px8ZGVlkX+CIAiCUBnw9BuWn89dnUUUFBRg06ZN5HfSbDaje/fuXn8nu3Tpgk2bNumTk4MHD+K7775Dz549PR5/MQhozcqoUaOQlZWFxo0bw2KxwOVy4ZVXXkG/fv0AACkpRaG+MTFUKxETE6PXeWL8+PF44YUXir1uN9lg/1tbUNNi+Em5poD7TNVQs4TIv0hdXRt1R/3FIuz8hctWBDZFl3K0MNprHQDkuA2tSaGb+p7rBaWy9xpzYx7aeMYdSuxb/6T9OPl3IwX14E6/kLrGttPEjjR7XyatbqUT0+1naurlKrdQjcqXtjrEvm7/fmKHm42bafYRxsnh6fXdbHwVKuOLazy4ZoXbalhxjIVqMWzsXKrexWGm9yOvkH01XH9ML9I7A2zemUjsuta98IadffY8pt/Znh+vl9WxBQBdgul4ijQbvnN/mqF8/gwr1812U01EdSsL0TWV/N5eToxI7OK17qVDVAf3Tp1via1GMvN0D5F++tvTvS5NaoBAx635/yyefsPGjRuH559/vtixp0+fhsvl8vg7uXv37mLHA8C9996L06dPo1u3btA0DYWFhXj44YfFDeSNL774Ap999hnmzJmDzZs346OPPsLrr7+Ojz766LzOO3r0aGRmZur/jh49Wk4tFgRBEISy44LZ7z/+G5aZmYnRo0eXWxtWrFiBV199Fe+++y42b96MBQsWYMmSJXjppZfK7RqlJaBXVp588kmMGjUK99xzDwCgRYsWOHLkCMaPH48BAwYgNrYoWiA1NRU1axp/IaempqJ169Zez2u322G3273WC4IgCEKgUprfsGrVqsFisSA1la5Mpqam6r+hnDFjxqB///7497//DaDotzcnJwcPPfQQnn32WZjNF3+dI6BXVnJzc4t1isVigdtdtISblJSE2NhYLF++XK/PysrCunXr0Lnzxcn+KgiCIAjlhVsz+f1XGoKCgtCuXTvyO+l2u7F8+XKvv5PefnuBosCWiiCgV1Zuu+02vPLKK6hTpw6aNWuGLVu2YPLkyRg0aBAAwGQyYcSIEXj55ZfRoEEDJCUlYcyYMYiLi0Pv3r1LfT0zTLrvk+sKVLiG5YbQXXp5YOQuUmcD9YffUbticqn4IvnLFsRelWG4xQqYDqVX9a3ErmU1FAz7sqqTum0hCcTubPfsHwWALvZ0YncPySb2fTcb/cq3oreY6F8Yqs4jX6P5NuItZ4k9vP7PevnlcX1IXZ0XaB6J2zc/ROwf283Uy9EW2gZrKTQsxTURhu0rnb4nqig6jkw31Uvx/EA8Tb5KAxtNeT724Ga9/J+t/Und+29F0DcPX0TMTiEH9XJDloPFwXJsdAo+4rVNkWbvf0lyDRQng+WRyVcO5+nNbT6e/S+OUUHixdoSo7IxJolu4TH/2Fp2hDEO0ly0/3NYXqskG/0O9ZWz6FLAfQHWEEaOHIkBAwagffv26NChA6ZMmYKcnBwMHDgQAHD//fejVq1aGD9+PICi397JkyejTZs26NixI/bv348xY8bgtttu0yctF5uAnqy8/fbbGDNmDB599FGcOnUKcXFx+M9//oOxY8fqxzz11FP68lRGRga6deuGpUuXVljiGkEQBEEIJPr06YO0tDSMHTsWKSkpaN26NZYuXaqLbpOTk8lKyj+JV5977jkcP34c1atX1xcPKoqAnqyEh4djypQpmDJlitdjTCYTXnzxRbz44osXr2GCIAiCcAFwldLNU1KGDh2KoUOHeqxbsWIFsa1WK8aNG4dx48ZdkLaUhYCerFQk6vJisfBQZsdZjGNtrEtzNb7PbMXzQfIqYjs1ap9wOfTyh6euInXZLrpiFW03Uq/fXnMbqesaQkN9ofSbGyw9up902WoKbn8uFtUlwHdjjbNSV8JdYUaIu63fZ6TugxeSiH1ffRqOqbowuLvGylw76njiLsbSLGUXgqfmp+fy5TZyM7ePRQ3x1mh7uWuqqRKCv7T9e6RuVOytxH5z+/W0TXk99PKzHegWDFc6DhC7tsV7antfn5WPJ+72yXbT96a5jTFe10pdg/xrUd1uY3NBuNf2Cd554RR1l10TYbh1WwXR1AP86eauXE8U4tJxB5VWk3K5IJMVQRAEQQgQZNdlz0ivCIIgCIIQ0MjKiiAIgiAECDw6TShCJisK2e484G/fdpiPMEmOqrfI16ivvF8AptPnW94X33Ld0KGEs+0CVmfWI7bNZLw33EzDXevbmEaChBzSOoeJtYlpLyw++pjrK1Qfd4aba2PoezPdxnU+aEg1KpyfW9AtAXocMj7PhBM3k7rxtWn4bnWL8aiZNe9p7wH62fm9yXVTO9xMH2G7ydD2VGEh3lzvomp/7GbfOhq1/x2gTKvzPbEXV61N7HcOXqOX56e0JXXv5V5J7OlNDd1QvJVqFaqwflLb5GTjaVtBNWKnFdLw6rcn36mX737sJ1J3V8QWYp9R0v5bWIh0390niP154zgIxfmjLe23P9BYL++b2pHU1Z9HQ5fN63YQ+9BnTfXyvA7vAwDOFohm5VJH3ECCIAiCIAQ0srIiCIIgCAGCCGw9I5MVQRAEQQgQ3KJZ8YhMVhQ25UcgNKjIL97WnqG/nsf84cEsJ4iaLtrXtukVxcJj64lt4TlNWI6NaMX8b/UVpC6U5QjJUbQNqS6qO3Ex/76aNJtrMWwsuwLPm+FWTsVzp3BUDUiUmR6730mvM+itJ/RyLGh6fX/cPXeEXk4aTdOwX/fBCGLf1srIQfNItZWkrq6N3g/1sxfT8piohiiXaXtUzUpp8JfWX63n+U44XLsUFlSgl3vHbiV1f5yNJ/birNZ6uX3oQVKXqGztAAAORS/1yAG6VcLIOj8Se/xXdxI7caZxv2Z0vJrU1exKr9PYflIvZ7lpnqHNZ+mWErOTf9HLD9TpBsE/DYat81nPN1JI7POHXv6/iSMAAO68PADPlm/DhIBCJiuCIAiCECBcqAy2lR2ZrAiCIAhCgCCaFc9IrwiCIAiCENDIyorCAWcMQgqKuiTNZeRlsLHcHF2CjxM73R1y4Rt3HuwvpLqN+lbfc1R176MgP3v2OBWHcrCJ6h5OFPI8K4Z2IY5tM873X+JajBSXcXw006HkaAXEjlJyjziZx/uns82IHTuldDoVFa5TUWn44EZi769SRS+7t9DPyvc64vsKqTiYJoXvPUW1PiX/W4RrhDi+dCp8z6GqFrrXzsFttfTyulCqBxkXt5TYK3IT9fLw9feQugYTqF7H/cduxaLP5KhF/yK2tUkWsffOaqeXt9/wDqnjn1XN6xNlTiN1s3OjiJ2tLOHPP7aW1N1ZuxMuV144uInY4+q283Jk6dAsGvn/pYDkWfGMTFYEQRAEIUCQaCDPyGRFEARBEAIEWVnxjExWFL6/IhpWfZm9qv766W8bkuNaVj9J7KZhNOV2IPDukVV6OY+FJqe7qdsk3Ozd7cDDttPcZlZvhNbOOk1Tp/etSt0kL9alqdbLSkGP9sTOeJS6Hea1/kAv57rpEM920dDTw68YW9cnPuvdreOJOuuM9PvJHXNInXs5DcndeyBWL8dZvG9DANBU97zOwv7qCmYupFy34RYyMxceD4Om12Rh5twVZfLuJuLvnXbyemLXXWikT9/ZJIbU5cTSz6eGCQ9u8Tupm3vVDcSu8Qe88tfxSGIP7krDxa8O26WX/YV7q31hM1G3W79YGnYb7cPzNveob5fjPfGBl/agrAzcc4TYta00nP2Rffv18vQG9ct8nXr/LXK1FWpOHPFzrFC5kcmKIAiCIAQIEg3kGZmsCIIgCEKAIG4gz8gUThAEQRCEgEZWVkpAvSpniP37L82JffUdexQr/CK0yD8xFuPWnnDRMOCRyb2IPTRuObGrKunS3zt9Lalb+ekVxG57z3bjOp2ySd2LKB+NCifoBxoWXOMHWv8Yunp9b/XVUcSeePcnejm6L9W+HHVWJfb0QzQte7twRa+wLpHUtQ7fSuw/I4zw3XQ31X+EmX2kumdbC3B9CN/SIE85PtxEH+98Fuasku6m4fmRTMekamX4NXNZm3g0w4luRmh/wTaqGfo+nj5L14UaWpJuoXtIne1h2k+5DxkanO5hf5K65kE0bJhrcDIV3ZYbVMvDQ5fVrQZSXbSfXth5C7EXt31feR89lm9zwfVgUw4bmpZA3LbDH+q2Hvnssx910bG4MSdJL58aSj9rdO9jxB6TtJjYG84Z752+vEjH5D6XBzz9TRlaHXhINJBnZLIiCIIgCAGCuIE8I24gQRAEQRACGllZEQRBEIQAQVZWPGPSNO3SyVNcRrKyshAZGYmX1l2H4LCi+dut4dv9vMsg2GR04eAK2haep/ZW09X3j/eu4QCAvR/SvCWfXjNTL+e47aTusc//Td+srM2VNk9JRTBDyT8DANUt3ufrXMfxTTZN1W83GxqQHTm1SN3iHS2JreUZmonOLfaRuifiqOimeVDZv6xUvQVPoZ/HdAQqqS56bIab6jgSrYbGw8Z86mlupqNhX7Z7nDX08tObaRr86Aian2Z8w4V6OZjlNDGzXC/xFiN/S6SZtrdYnhiGqkMpriWh/bTHaYwRf2GlV9iNc7nBtTy+9UdOGG3a76Ttfyapg8/rVgT8OyfERx6fv9w0z8rBQuPYPI3muWlgpdqxaAv9DspW9EafZzUtOsfZQozqsBKZmZmIiIhAZea23x7ze8y3V759EVoSWIgbSBAEQRCEgEbcQIIgCIIQIIgbyDMyWVFoYD8JR3DR8qu6DN7IRpeFbSzFOQ/Tqwiymcti8MH/Uyy6PQDnvas+InabIONcNhNdqt408E1iL86pqZdnPZtQkqZWKLWtdIdsdbdnvmxfy0KXte8Ip+GxKveGHyT2S7G/Elt1JfT5YQipe6Yr3Yl430dGyPeSq+mOwAlW+shydwf9PBSeqt+phDk7mXsjnLlgHMp1eGhvtJkeG8y2CAg2pejloc1p2vtduTWJXaBsDXGgoAap6xRyiF5XcQ/wNvnbRdoXx9jjPOTPfnq5XpXTpG587UXEtpjCjDawncO524e3MV+5HymuKqSOp6+f1ajin7UM9p0TojwvfJuISDMNWW9uM/omV6OuQJQidDc+qCitRK7NR/h/JUNClz0jkxVBEARBCBBkZcUzolkRBEEQBCGgkZUVQRAEQQgQZGXFMzJZUchzB8HsLvKZxwcZYZEW1k0nXQXEznb73mL+QvDXkgbEnphG23h1tb16eeacK0ld3Xu3EvvJtwcT+7XHjJThLYKoniLcTK/zR258yRrMOD6KpthufMteYr+d8DWxf8szQoM/aJiEspLpziN2sKLF4KGX3O8eY6F6FxUz8zMXgvrQ1dDf/93wAakb9sx/iB0WafT5b+fqk7rYsP2sjfS6ZngP2eW6Dpvy1mAT1R+Em+l57SZjjPO0/U6mxeD6KfU6XPfTN2InO9ZoY2t7BsqKlfUD1yNBCYPm/RJnocd+1mKWXt6eTzU2vJ9KQ5rLu9bNxsaPzUTt95ONEPy7xj5J6qrMvjgpBNLd9Lsg2mx8HjtKrq3idU6m9Sk+bg070VqkWTlrLbtGKdCQyYpnxA0kCIIgCEJAIysrgiAIghAgyMqKZ2SyIgiCIAgBgiaTFY/IZEUh0pKDUMs/eVYMD1k286H2/WMQsbP/rKqXFx6eTOpqsx5WcykAwM6CcL08vh5N0e6L6HE0BTXeo+atYcZ2AX27bSN1qYeoNuOMi+oIVJ0K16io2gUAaB1q5H+o+WcUqXNq1BfdPczQJ8RbVpM6h5me1wyqD7kjNF0v1ziQTepK029pLvpFEGsx7oed5ZTh6g+uS/EF10xUNRuf56pgqnla+chrxP4ht45e5n24x0nve6Q5n9jhih3M9CzHCum9XJ9XVy+vyaxH6gbW+I3Y7YOMNnNNyk5nJLXzahO7e+guvVzdwvuYtlHNYRTMxl6x9PTKcxnCdA9cbwT23KnaHn4sH4txil4kOoTmLLLx65Br0M/G9UW+9C71bFQrlu2mKejDles2/g99flNnez1tubKP5cE57DTGxRX2U6Sumg+9F88342R6ndNMIxij5Nf5Z7ybzJeOZkXwjExWBEEQBCFAkKRwnpHJiiAIgiAECKJZ8YxMVhSaBWUjPKhoeXVzfrT++pocGj76V3oYsRvON5Zoj99Fd/yMNNPlXM4jm41U3nXge6fnR/YZYasOE136jTDTkNwgJTQzii2nx1i4g4O+1wyHXi62nM5Q3TNOLY3UFd/51nuIN18yLxZqqtAqiC6J3/on7WN1N+SF7WmYc1XmhghT0oDzNriY64CHI6tuCbuJ9rGv9vNl7+BioZuGHWXJJXV1bfxeUXztNuxiny/KYqQ5r+eg9y6X7bZ9WnFDrM2jO0w/8829xH759rnEVseihbU4w037wg1jyZ+7SRzss3GXpC98jWN+n4vVK/eS3yt/uzurcNcgT0GvjgsHc0nWLHYZY7yNrrmU1IxAF37wBaGejY6ZcOW548nv/2IpA1Ts7N5syQ8l9qixDxH7i/Gv6+Waf6f4z7KIG+hSRyYrgiAIghAgiMDWMzJZEQRBEIQAQdxAnpHJiiAIgiAECLKy4hmZrCgEmcy6//TaEMNH396+nhyX24KG/q57OVEvNy2Wnp46m08UUl90/0bGue84vIXUHS2kIaFBSghljIXqNoqHhBp+4Dw/6au53sKfTkVF9dn7C+3lmg+K72uq545gvv6How4Se0uB4b+en0PDKw866Xvb2NW0675TtPPQWV96Bd4XZzUjpDjVRf3rGW46no7kV9PLJ1ia9QZMJ3DAWZXYjYOM+jimTWoZxG1Db5QZcoLUnXDRY9NcRhvrWNNJ3au95hC7nf04sasp7eChyqFm2qcpynXzXLQujukSQkzl86XOxzvX1ahaE99jmOpf/D1HfPS4lB+pPNCQ9Lv23E3fq+iAJiQtIHV37qJhw/Ob0GegvCgo9j1ilKtbWGoFxrFC4/OlsB/nPI1qkTqN3EjsaLP8bF2OyF0XBEEQhABB3ECekcmKIAiCIAQImvdAwssa2chQEARBEISARlZWFAo0N/L/ntU6FH8zT6n9dPVVxA6PMXQnPE08z6mRYKXT5rsiN+vlg85oUmdm762u5MVIsPI0+N5vpa+cH57qCzUlXwKb5vu6jj/U9OhcNwMT/azFUqsrWoFMN9UNzEzvTOxfXjHyTIRhHanbWxBD7GCToa9IsDpJXfHcKTxle8n73K18ntdSbiR1yR1z4I0Dr9HP9tWB64itsVwkz434VC8XTw1PVRLqPahipuM20lzyPDFt7KfZEVSvoN53Z7G095QocyG8YWbt96kfYc0vjQ6L511R76U/3VJpruOLbDbG/y9uE7HrBaUqbaDtvcGxl9jzcWE0K+Em+ryo+ZyKPd+MAs2o524PB9tColPYAWKnK9s9/LOlRI6fXDmVCclg6xmZrAiCIAhCgCDRQJ4RN5AgCIIgCAGNrKwIgiAIQoAg0UCekcmKghnGUpPqZw8zUR+8xUwXpFQf9xn3OVKXwVypMRb63uqK5uCoifrr461Z9LqK7zzVRX3a3Nev6mxcPvQTHuuVclgp9l/hGgK+F4ovPzY/Fiy/yH6n0U+9lo8gdQ0fpHkYuE5FZfI0mq/i0Ue/1svJ1gxSd3XIGWLnM7+4zeRdg8N1HQ6z0Y/9qq0hdePR0mt76z25xmudJ2a+W1cvn9lF97DqHbaP2Oq9LY0WyZ9ug+eYsSr1To3uEcPzrlRT+omfl9vqM3rWTXUOdra3jsNEc9mUF772kzof/QrPzxTLxqb63VDbQp9RqiQBphxerZdHJJbfvkHb8ukeUVHmo0YbmI4mzEy/Q+OtRt8cKaTHniioQuxuIUeJreZwydOKvvfyL6Hfd4kG8oy4gQRBEARBCGhkZUUQBEEQAoRLQWDrdruxf/9+nDp1Cm62s/pVV11VpnPKZEUh260B7qI1OEfJd34ncLdJGDuPhaUIz4PhvmkblE3qeGBmpnLP9znpUmmEmS6vm5Vl2N35NUldVStN1X+Fnbo7qijp7PkyN1/aVpe9uSunWPiu4hrJcFO3VZSZDuh0Vr8zP14vf3rtTFL3ItrCG1l9OxG76k7qLlDDPHnwYzq7ATb2HZLrNhbcVTcPQF0UgG8XWOpjdGl+2uPv6OVsNw0pfm78IGJX/cC7m2hFeiNiW9gnbGw3Uuy3Cirw2d5Mt1HvMHt3x3hCdcHwNAB8HGQo16lu8R06/o8LAACymYvOzu5VvkadI77uBx/zqn0+W1OUBu6qfWb7HcT+7Yr3vb6XuyvnZnYov4YpzGqUQOzYA5l6mbuwHez5VkPYf8ttwI6lz+iOArqlRLjZcLXHW4uOzXZfOqHLlX2ysnbtWtx77704cuQINDaOTSYTXC7f3xfekMmKIAiCIAQIlV1g+/DDD6N9+/ZYsmQJatasCVM57eElkxVBEARBEMqFffv24auvvkL9+vXL9bwisBUEQRCEAEHT/P8LZDp27Ij9+/eX+3llZUWhisWCiL9Di7m2RMVXOm7uV+e4NW4bL/DwPh6O+fs5w0c84VMagmtt/xexE6sYdqcqh0jdlSGHiR3Gwjq5z16Ff3Zf8L7IVbQNqS56TQuoZmJrPk0RvisvTi9zX7kvHhn3FbFPF0YQO1QJHY800zapmhQASGd+8SCT9zTsPK28qq+IYqnsVz79BrHVUHk3ckld5xcm0zaO8+7/PcH6+IfsFsQ+kGf0cWz0alLnYENgX6ERBt3YRrcH4P22JZ/+DdQ8yOjHNBfVqBx10fDqKEV7FcP+luI6E5uikbL5Sr2P4vdS1Rj5quPXLaZ90Wib1O8NeynC/ovea5wrg421wh103DqvMMbQPbUvjCaltKjbgXDNEEfVLrUOTiZ1o/bfSWzLRKpZyUw0xtu7z00FAOS4RLMSKDz22GN44oknkJKSghYtWsBmo89By5beUzX4QiYrgiAIgiCUC3feWTTZHDTICAYwmUzQNE0EtoIgCIJwKVDZV1YOHTrk/6AyIJMVQRAEQQgQAlyS4peEhJK76UuDTFYUwkx2hJUgbwLPH6LiL7V9hJLDBADCzD7yPTDdzEubb9XLdV9ezQ8n5PxURy/3DP+D1PH8FTyNuS985VnhcB3HPqeRM+RHpp9IKwgndpaT9lNal4wSt9HcuqlenrSzGan7qM0sYtvVbQlYe3lODYeJ6mrUrRQcFp6Kn/aTUxkXCVa+DFryv6QizSHM9n5stIXqK+pW2UJsdXzls7/m+DYRtSxGbp4CNvzVHCwAMPXkrcR+pfa3elnVCAFAYzPVvziUsegvjb+NHEvruF6KP0vqvXayZzbVRbVikUpeGb49wGn22Q86DW1JGzv9bMW27WBjJFc5F9fyJIyl+XT6j+2KQGNkYudyOY8dh9kr1FYVLHHj/s6zYrl0NCuXAgcOHMCUKVOwa9cuAEDTpk0xfPhw1KtXr8znlGggQRAEQQgQNM3k918g88MPP6Bp06ZYv349WrZsiZYtW2LdunVo1qwZli1bVubzysqKIAiCIAQKldwPNGrUKDz++OOYMGFCsdeffvpp3HDDDWU6b8BPVo4fP46nn34a33//PXJzc1G/fn3MmjUL7du3BwBomoZx48bh/fffR0ZGBrp27Yrp06ejQYMGfs7sG9XV4yuUt9j7mCuBLz9zF5K60M1dKjz8dWBzYyn4/Q+uJHUPtKduoSYhK/VyNQtPNe57B1pfodj886n1PLT0UCF1d3x2xtgT4pyLhrPVCs4g9t3R64nta2dizqFnjF6d1GI+qYu30r7IVz5ONksbH8RdB+w6R5Uw6MNO+tkb2GgouUU5lZ25/oJ97HhcGhedP3g4rLp9AL/Kzzk0Vf9JZ5RerhNEt2dIZunQq9npdg5Ryuf1FRZcWtSxx50A2SyNPw+vVt9rZ+6YKuaSh/LHWGjPLcg23K/fZkSTunuj1xK7uoW6kAbX6eb1OoJnst1F9+7sJeQFulArJ9OmTcNrr72GlJQUtGrVCm+//TY6dPAe9p6RkYFnn30WCxYsQHp6OhISEjBlyhT07NnT53V27dqFL774otjrgwYNwpQpU8rc/hJNVqZOnVrqEw8cOBDh4eH+D/TBX3/9ha5du+Laa6/F999/j+rVq2Pfvn2oUsXYF2fSpEmYOnUqPvroIyQlJWHMmDHo0aMHdu7cieDgYB9nFwRBEIRLn3nz5mHkyJGYMWMGOnbsiClTpqBHjx7Ys2cPatSoUez4goIC3HDDDahRowa++uor1KpVC0eOHEFUVJTfa1WvXh1bt24ttmCwdetWj9cqKSWarIwYMQK1a9eGxVKyv/KOHj2KW2+99bwnKxMnTkR8fDxmzTJEkUlJSXpZ0zRMmTIFzz33HHr16gUA+PjjjxETE4Ovv/4a99xzz3ldXxAEQRAuJhciQ+3kyZMxePBgDBw4EAAwY8YMLFmyBB9++CFGjRpV7PgPP/wQ6enpWL16tZ7ULTExsUTXGjx4MB566CEcPHgQXboUbdL6+++/Y+LEiRg5cmSZP0OJ3UAbN24s8azofCcp/7Bo0SL06NED//d//4eVK1eiVq1aePTRRzF48GAARfHcKSkp6N69u/6eyMhIdOzYEWvWrPE6WcnPz0d+vqH4z8rK8nicIAiCIFxMSuIG4r9hAGC322G324sdW1BQgE2bNmH06NH6a2azGd27d8eaNZ53bV+0aBE6d+6MIUOG4JtvvkH16tVx77334umnn/a7aDFmzBiEh4fjjTfe0K8ZFxeH559/HsOGDfP72bxRosnKuHHjEBYW5v/Av3nmmWcQHR3t/0A/HDx4ENOnT8fIkSPxzDPPYMOGDRg2bBiCgoIwYMAApKSkAABiYmLI+2JiYvQ6T4wfPx4vvPCCz2urfupCplZwMm2DL797tpseG8xC7Fya9+sEM71Cx1Bjv4UzrUNJ3cAqVOPhVGbnm1nq+ivsp4hdzULDYVV/Pv+sNvYcqfVmpscZ+Of9xK4fdVov94uhD0mLoNPEjjbToflB8iq9/I+f+h9SWZjnJ2mGnVIYRepOFFItyf9OGzqam6tsI3Ud7VSbwR/RVWcNXcfqVlTn0Gkb1cYMrrJOL/vSqPijNNsdZLOw2u6/PkbsK+oe0cvjai0mdfFMl7I0rble/vVVGqLq2Emftdrz6Xt5CLgKH1++trngwYvqe3moNd8ugGt/1Ofj53NRpK5TcBqxq7JwcRUetp1Z6NDLv56kG7ntaHcJCSsChCCTm/z/csHTb9i4cePw/PPPFzv29OnTcLlcHn8nd+/e7fH8Bw8exM8//4x+/frhu+++w/79+/Hoo4/C6XRi3LhxPttmMpnw+OOP4/HHH0d2djaA8lnAKPFkpTSoM7jzwe12o3379nj11VcBAG3atMGOHTswY8YMDBgwoMznHT16NFmOysrKQnx8/Hm3VxAEQRDOixKsrPDfMAAeV1XKitvtRo0aNTBz5kxYLBa0a9cOx48fx2uvvVaq+UB5eVmAMkQDHTp0CIWFhcXEM/v27YPNZiuxX6sk1KxZE02bNiWvNWnSBPPnF0V4xMbGAgBSU1NRs2ZN/ZjU1FS0bt3a63m9LZcJgiAIQkVSEs1KaX7DqlWrBovFgtTUVPJ6amqq/hvKqVmzJmw2G3H5NGnSBCkpKSgoKEBQEF1Jbtu2LZYvX44qVaqgTZs2MPlYId28eXOJ2s0p9WTlgQcewKBBg4pNVtatW4f//e9/WLFiRZka4omuXbtiz5495LW9e/fq6XyTkpIQGxuL5cuX65OTrKwsrFu3Do888ki5tUMQBEEQKiNBQUFo164dli9fjt69ewMoWjlZvnw5hg4d6vE9Xbt2xZw5c+B2u2H+O/XA3r17UbNmzWITFQDo1auXPnnq1auXz8lKWTFpWum0xxEREdi8eTPq16f+2P3796N9+/bIyMgot8Zt2LABXbp0wQsvvIC7774b69evx+DBgzFz5kz069cPQFHE0IQJE0jo8h9//FGq0OWsrCxERkbi1J4ERIQX3RjVH+4v7bev9PvFNR/exUln3VQwxXO0qHlX0pmLNsZC/fmZilbm44z2pO7OCDqzDTXTk4UreSd42vg89nnSXEYbLSbaD48l0JTgt+80tAydQg6QOoeJ5sXg+PJJb8qvRexsl6ExuM6xn9QFs2fo3j399HLnanQDrker0tw1/M5d+eV/9XK9J2gOjb0zryD2hpun6OVIs+9xWaq8Pmzsqe/N12iffpqdSOzkfCM/Su2gdFJXN4jqmiLMeXrZYaZ6nF7fU9Gcveo5Yv/aeYZetvn5bL70LVzPcqTQ0Is42NiLYpqnEJP33EL+NGm+ntkHj9AEV6XZFkIoPwo1J1bgG2RmZiIiIsL/GwKYunNe9XvMwXufKdU5582bhwEDBuC9995Dhw4dMGXKFHzxxRfYvXs3YmJicP/996NWrVoYP348gKKI3mbNmmHAgAF47LHHsG/fPgwaNAjDhg3Ds88+W6bPdb6UemXFZDLpohmVzMzMMm/97I0rrrgCCxcuxOjRo/Hiiy8iKSkJU6ZM0ScqAPDUU08hJycHDz30EDIyMtCtWzcsXbpUcqwIgiAIlY4LkRSuT58+SEtLw9ixY5GSkoLWrVtj6dKluug2OTlZX0EBgPj4ePzwww94/PHH0bJlS9SqVQvDhw/H008/7fdadevWxYYNG1C1Kk0WmZGRgbZt2+LgwYNl+gylnqxcddVVGD9+PD7//HPdn+VyuTB+/Hh061b+GRhvvfVW3HrrrV7rTSYTXnzxRbz44ovlfm1BEARBuBQYOnSoV7ePJ/lG586dsXbt2uIH++Hw4cMeFy7y8/Nx7NixUp/vH0o9WZk4cSKuuuoqNGrUCFdeWZTy/bfffkNWVhZ+/vnnMjckENiUb0JoUNGs1qWkpHdr1BXSwZ5HbHV5mu/cy5eQefr6XLexpJ7Dw1KZd0k9k1PzHeuuhmYueuNaUvdBV5qqv8YqOgys9xpCrDcbzSN1GS6q7v49p6Fe/uxP6vqoh63EPusyVruCTWzpnYWlhjK3UB2r9/DRahYaOnui0Pjw0RYeskr7uFecEa784ykq5t4YRsVndW00vHribXP08sqraXr6fmELvbaXj4Hi9QZ8PPkO7QXMyijhLpV7w+lfNLlh+/Ty9gK6dD5sK81RNLX1XL3cxErDdb9TXFwAkK3RlPp56mdgzedp8EuTfj9Ycf1Em0u+hQSHX5OH56u7MOewsHlx+wjlTiXdG2jRokV6+YcffkBkZKRuu1wuLF++nCR1LS2lnqw0bdoUf/zxB9555x1s27YNISEhuP/++zF06NByya0iCIIgCJcrgb6rsjf+Ee+aTKZiqUX+iRR+4403ynz+MmWniouL03OfCIIgCIJQTlTSlRX33xulJiUlYcOGDahWrVq5nr9Mk5W//voLH3zwAXbt2gWgaLVl4MCBsrIiCIIgCJcxhw4d8n9QGSj1ZOXXX3/FbbfdhsjISLRvXxQSO3XqVLz44ov49ttvcdVVV/k5Q+AysWUrWE1F/vb9kzvpr1vy6LLc9D4ziV3Xmmkc62cFj/u83YoT//7tg0jdX4erEHvp7ZP1cqSZaj7y2dJhmhK+W+Ujmtq+yke+24iPjeLO3TQsuKn9OLGT7EZqcjMLHz02vxmxu4cZG1JGm7lmheI4jzj9YCXM2QaqZdjmpNqX6lYjsu3QaapeX+JoRewxNX8g9nUhKR7LgKeQb6PMdSg8xDhb0dXwfnCA6kGKa6KMc/MQaLuJvlcl1nKW2FGhNPw41GToVPh2AXXZaVUdFgC4lHbwfuF9YVaanK/R83C9kUt5K9fyWFmgOe9j1eYpAo6xKPqRieUfOCAI3qmcbqB/GDZsGOrXr19sH6B33nkH+/fvx5QpU8p03pKr2f5myJAh6NOnDw4dOoQFCxZgwYIFOHjwIO655x4MGTKkTI0QBEEQBAFFbiB//wKY+fPno2vXrsVe79KlC7766qsyn7fUk5X9+/fjiSeeIGl4LRYLRo4cif379/t4pyAIgiAIlzJnzpwhkUD/EBERgdOnT3t4R8ko9WSlbdu2ulZFZdeuXWjVqpWHdwiCIAiCUCIq+cpK/fr1sXTp0mKvf//996hbt26Zz1tqzcqwYcMwfPhw7N+/H506Fek61q5di2nTpmHChAn4448/9GNbtmxZ5oZVNA3mGD58S2oGqZv0TAtiHxvdRS/Xu4nmstj3C705Ca9sJHbOrW30crWF60gd11Ivu76xXr43nO6ZtN3pIPa+fCNHSNb39UhdxM001b0vrnPQzxPM/PuhpiN6+X8dP2LHUs2BTdGSZDONTXUz11fQoanmJimWy4bpE+Ksxnu5dqGFLZfY2W4j90ti/32k7qc36fiddOsyYjvMhljDXz6Uo4VG+2Mt3jUqHK7xKJ6jhX4+X6nheY16bBLLnfJig2+IXaC82+UnHT3XgKjaGX6v0t30uqddRp+uyG1M6m4L20HsGItxn3muFAvrNzv7qlPbnOmmeZOizQH+ayBc2lTS0OV/GDlyJIYOHYq0tDRcd911AIDly5fjjTfeKLNeBSjDZKVv374AitLce6ozmUzQNA0mk6nc0+8LgiAIghC4DBo0CPn5+XjllVfw0ksvAQASExMxffp03H///WU+b6knKxcqLEkQBEEQLndKt7VwYPLII4/gkUceQVpaGkJCQhAWFnbe5yz1ZCUhIeG8LyoIgiAIggcugcnKP1SvXr3czlXiycqvv/5aouMqc56VOzamIiSsqEtSnUaeiZUtve9LAwAFUcboeq7Ot6TOeT/10W/+P7o3wu8Zhk5ib2xnUld9Os2PsqipkQdkEbqgpESg5BoVzuA6vnNMDNxjaFZilXwzADD7DN2DaOk6Q4BdNfEvUvdL64+JzbUZbuUB9qXL4PA9YbieokWQoU7fM4PmhYn/mp4r+xbaJpui3fC3p01dm6HF4HlIwnnuEaXNXOPBtTA8l0pZ4Rqhq4KplkS9H6kuWnfQSfcVirfSHC3VlMjBXJY75f2/OhLbpezDZWP7R1Vj+zypfb6lgN6b5jb6Xt5vTs27i/oBP2NeEATffPXVV/jiiy+QnJyMggL6fbF58+YynbPEk5VrrrkGpr8feM3LOpXoVARBEAThPKjkAtupU6fi2WefxQMPPIBvvvkGAwcOxIEDB7Bhw4bzysVW4slKlSpVEB4ejgceeAD9+/cv97z/giAIgnC5Y6rkbqB3330XM2fORN++fTF79mw89dRTqFu3LsaOHYv09PQyn7fEk5WTJ09i4cKF+PDDDzFp0iT07NkTDz74IG666SZ9xaWys7B9jJ5u/5Ojv+uvN90XQ447mF+D2NHW+caxbPnZzNwQbYJoeOz9kbv18pGnvyN1T07vhEBnViNfGiYaEtoA67wcBxw5TPspxUUFWWoYdPOgfFLnMNGU+sXDew2y3S5mG66FsZ2oC2/W/N7EjjR7dz/xENxwdqzaRh6OzFHHDP8sNpZun7u53Ip7ozTuMn/kKenp+ZYRA39+kNjmYBqaHRZujIPqU6hLNaOendgTnzG2smhko27FXHZbc5XP2u/LJ0mdNZd+Jw2+i+Z9uCPcSLEQY6HjRxAqlEo+WUlOTkaXLkUyhZCQEGRnF21p0r9/f3Tq1AnvvPNOmc5b4qRwQUFB6NOnD3744Qfs3r0bLVu2xNChQxEfH49nn30WhYWF/k8iCIIgCMIlS2xsrL6CUqdOHaxduxZAUSSxNwlJSSh1Btt/GjB27Fj89NNPaNiwISZMmICsrKwyN0IQBEEQBBRpVvz9C2Cuu+46LFq0CAAwcOBAPP7447jhhhvQp08f3HHHHWU+b6lDl/Pz8zF//nx8+OGHWLNmDW655RYsWbIE0dHRZW6EIAiCIAio9G6gmTNnwu0u8tkOGTIEVatWxerVq3H77bfjP//5T5nPW+LJyvr16zFr1izMnTsXiYmJGDhwIL744otLdpKiLjl1tKeQujgrDbt1K+GWxdN+81BTWq+GpiawlOeWX+KI3TDilF7e1e7ScruNTKRh27XXUs1K47CTermpbTupc8J7BJqL1XEVR6TZqOfbA5iHpRKbhxGrHCukWowmQQVejgTsJqo78RV+XMjazzUsvkKmXSyNP09B76vOzUJ785VzWVhzY2rR5yGyZ8k3NK26gtpv3NdDL39Q7wtSd4T18bi67fRyXdAw/9MP0fHUNuQwsW3KZ7ijdoeSNVYQBJ8UFhbi1VdfxaBBg1C7dm0AwD333IN77rnnvM9d4slKp06dUKdOHQwbNgzt2hV9SaxatarYcbfffvt5N0oQBEEQLksq8cqK1WrFpEmTziutvtdzl+bg5ORkPde/JyTPiiAIgiCcB5V4sgIA119/PVauXInExMRyPW+JJyv/+KAEQRAEQRA8cfPNN2PUqFHYvn072rVrh9DQUFJfVu9LqQW2lwvq1IzrTuoybYlTCcdyMl1DLpvkhZtpTgercjzXRFxdjeZkGVLF0GrcicDPwXI+HOt0ltit/zQ0Ok72pwfN1EF1HVwfEm2hR7uUe9fCfoLU/bsOdXOeZaniVZVHdQsdEzYEsWO961J4rhRv7QOK507hNteplBXe3gKlHT+cpdsSJEbSRE8Fv9L9QG6pbozb1Zn1Sd3RJ6htNhk6ofNJe//9mNd91rsq+V+vwiVMgEf7+OPRRx8FAEyePLlY3fl4X0oUurxo0SI4nU7/B/7Nd999h3Pnzvk/UBAEQRAEHZPm/18g43a7vf47H5lIiSYrd9xxBzIyMkp80nvuuQcnT570f6AgCIIgCJWa6OhonD5dtCnsoEGD9Ky15UmJ3ECapuGBBx6A3c4X3D2Tl5fn/6AAZMjWPXCEFy2rp7mMeZwa3goAMRYaQpmreQ9T9eX2AagLgIeltikWbll+6dMrmjrrqB8zuWMOsfd/0obYG7KMnaP7ROwkddxNZ1Fda6zPnOyvkjwY9+5oYRSpm/heH2J/fxe97rVVjK0SMl0OUjcwcgex+a7GJYV/No6v3YP5eLKULQckALp9gN1MV1mzCoKJ/XDtFcRuYDN2tu4TfpjUffT+EWKrO4ufD/3ju5bLeYQLw+gDxnYHUWa6Cp+jUdfti3XbXpQ2BQwBvnLiiYKCAmRlZaFatWr46KOPMHHiRISHh5frNUr0DTpgwIBSnbRfv36IiIjwf6AgCIIgCJWazp07o3fv3mjXrh00TcOwYcMQEhLi8dgPP/ywTNco0WRl1qxZZTq5IAiCIAglJ9A1KZ749NNP8eabb+LAgQMwmUzIzMwsdw+LRAMJgiAIglBmYmJiMGHCBABAUlISPvnkE1StWj4u3X+QyYrCuOn3w2Iv8r87lWzvrW7dRY6blfgjsS1KmCfXSPjSqABAptuYfTpZmGrTIJrG3AxjWe3dIzSs9tGEsod5ng+zk4125HI9iEY/u6r94QG23+1sRM97mAq0QiyGTuKMi+o4bKAaCoeZ+rxVeEiuw2RoitrbaQhu+DHayrQuGcT+ArFer3NXMn2vQ0mbz1Pkc22JSrab6qFOuGifLspqTezmIcf08rUhaaSOj8XSoIbV/yuMhtT3aXCA2BbWx0eVnSHSXHSbiM+TryB2KA6WuY1C5SHBamx8y0d/jos+v3s/bE/shoM26uXsPkUpHAqdecD8b8q3kRVFJQ9dPnTo0AU5r0xWBEEQBCFQqIRuoItB2cMDBEEQBEEQLgLlMlkpTQ4WQRAEQRC8oJXg32VIqd1AEydORGJiIvr0KcpBcffdd2P+/PmIjY3Fd999h1atWpV7Iy8WNd5bB+vf6dkPvWpsMb8xuQ45LrsO1RFEmmmeCRWuUeH6BJvJmC8Gs5waPFW8So5WMR68yYfXEFtNX1/Nz9xX1YvwfnGY84n912aasj3hdiNl+085TUhd2xDqI02w5urlmhbfmiG1TVXMNNRu4RtvELv/FyXP3TGYpYr/7OjvepmPF65hUXOncH9+rIXmVekXuZHYi5VU+PnBqaTOwcIM1OtwrVWxHC3K2Iw0eR/vAJDPtiUINhnnCjfTMf7vRKq9+hxxPs8tXBo8rGjs3j7yO6lLYfmOTBY6Fq/bbuRkmrGxaKy5zzmB+eXcyAqiMkYDXQxKvbIyY8YMxMfHAwCWLVuGZcuW4fvvv8fNN9+MJ598stwbKAiCIAhC4JKVlVXif2Wl1H+ep6Sk6JOVxYsX4+6778aNN96IxMREdOzYscwNEQRBEITLnkq4shIVFQWTn2zbmqad10aGpZ6sVKlSBUePHkV8fDyWLl2Kl19+WW/I+WxSFAjM2rUO4eFFi00nXIa7w6nxZXrvo4kvn/N06HzJXw3z5HU8zLZQCX89URhJ6o4904XYtV9d7bWN58PnGR2I/UL1bXrZYirFQh3bHfgGx2Fit+w3hdhb8+ONNhynbbi63h5iR5mNYZ3lpomJeFiz2Uc4b467/L41DhYaIdJt6A4MPl0uVfy4XKqwLr8vwgizz2PjlLtncpWx6SjmnuTbFKih1/y8NBw5T+M7jRufZ4+Tutq+7E5Dl4HjemnfRzTN+vrr3ib2vOzGerm80vQLF5/HEqh7dci+vcR+qRMNSe7hSNbLI24s2gIjK9vtI5FAJaMSTlZ++eWXC36NUk9W/vWvf+Hee+9FgwYNcObMGdx8880AgC1btqB+/fp+3i0IgiAIwqXE1VdffcGvUerJyptvvonExEQcPXoUkyZNQlhYUfa0kydP4tFHHy33BgqCIAjC5cKlIrDNzc1FcnIyCgpoQErLli3LdL5ST1ZsNhv++9//Fnv98ccfL1MDBEEQBEH4m0qewTYtLQ0DBw7E999/77H+gmpWFi1ahJtvvhk2mw2LFi3yeeztt99epoYEAqFmO8LMRSKAGBhaB0uxMFs7sVUtQB7TqNiYjiOP6QYyFV1EjIXeDlWjAgC5buO9TYPOkLoGPWjK8/b3GO1/qMomUlfAtAzZbqajUab2aS6qMairhAUX4dBLXJ/D8RW6zEmw0nNFmo007Hc0OkLby7Q9qt6C3w/eRpfSjkyW2n5dXjyxta6tiW36fauHlnvmsbHD9PK7L75F6uKtdEyEKSHrLNK3mL7Fxe5lmDI2w0DhmhX1rrvY/ch20VDyGX8Z4vlj56qQukdjfiZ2jIX2454CY6v4VTkNSd1/mK/booQ5W0C3uQg3U7HPTaFG/Z8bupO6A1eU7yZqwsWjljWD2DGWU8RWt8j4ZwsJKyr3Dzyhkq+sjBgxAhkZGVi3bh2uueYaLFy4EKmpqXj55ZfxBksHURpKNFnp3bs3UlJSUKNGDfTu3dvrceej9BUEQRAEoXLz888/45tvvkH79u1hNpuRkJCAG264ARERERg/fjxuueWWMp23RJMVt9vtsSwIgiAIQvlR2TUrOTk5qFGjBoCi6OG0tDQ0bNgQLVq0wObNm8t83nLdGyg3l7sIBEEQBEEoMZU83X6jRo2wZ09ROolWrVrhvffew/HjxzFjxgzUrFmzzOcttcD2+uuvx8cff4xatWqR19etW4f+/ftj7969Xt4Z+Jhh0nUVPPW6TxR3abab5pw4WEi7ONdN9S4xlrN6mesrirVPyb9Rk7XvzUSaa3qns5pezmCLYblummvkgJOmtq9nS9PLTW3U928zeR8yXE/B88b4OjbcTM/L+yLYpJbpsVZ4T6nvaysEgGpY0t30PN1CjhL71nkziK1+vq/O0jTxJ51U15HtMtLKn3GFkjoLzhI7wWqMId5PHN5PkSbveWN47hRf9yfNTTUADrOhQ2kRfozURZmpRoVvGxFsMrQyNhNtbz0b1V7FWYx7x/O1APzZMcbBqJifSN3I33oTO/vK0xAqBx+doXlXXor9ldh8HAuBxfDhw3Hy5EkAwLhx43DTTTfhs88+Q1BQEGbPnl3m85Z6ZSU4OBgtW7bEvHnzABS5hZ5//nlceeWV6NmzZ5kbIgiCIAiXOybN/79A5r777sMDDzwAAGjXrh2OHDmCDRs24OjRo/qegmWh1CsrS5YswbRp0zBo0CB88803OHz4MI4cOYLFixfjxhtvLHNDBEEQBOGyJ8AnI75wOp1o3LgxFi9ejCZNijacdTgcaNu2rZ93+qdMW/cOGTIEx44dw8SJE2G1WrFixQp06dLF/xsFQRAEQbgksdlsyMu7MGkDSj1Z+euvv/Dvf/8by5cvx3vvvYeVK1fixhtvxKRJky7ZDLb+coIcKTR89gedvvcosZmopiXc7FbK1CfvS1PAibHQHBT7nIY2wMWSDBWw83YMPkHsfOXjco0K9xeruWB4zg+eI0TdQyaX5fzg+Wi4zTUtvuB7KvlC/TwJVqqRsJuoLsjXee8Ko32Yq9FcMOp+UnlsOGVr9LPlKFqNTBe95s85jYmdEJRG7O4hGXqZ3yt+f9Q9iPLY/j4nCqOI3VzR74SaqEaFj9LN+fS9T08erJf/M5Tu81Kb3Vab8pVk93Mb1c/nZO0fWHMVsR//nC4/R0fm6OXInvt9X0i4qOxpT78bgo/70slp5P+XBJX8owwZMgQTJ07E//73P1itZVoP8Uipz9S8eXMkJSVhy5YtSEpKwuDBgzFv3jw8+uijWLJkCZYsWVJujRMEQRCEy4lA16T4Y8OGDVi+fDl+/PFHtGjRAqGhNKBgwYIFZTpvqScrDz/8MJ599lmYzcbfU3369EHXrl0xcODAMjVCEARBEITKT1RUFO68885yP2+pJytjxozx+Hrt2rWxbNmy825QIMKX//PZkvPRwgi9HM9SRXMyWOjyaZcRRhxq8u0asatp2H2kmAeAtnajHXls+f9ogYPYda10Wf+0yxgW1SzMNcIW/dXQ37PMtXPCSa+7Nd9IX3+d4yCpi+Rp5VmbeTp4Fet5ZNpW+5H3YfGwYTOzjPfafYR0A8BZGH0Tytqb56LX+eZsE6MNGr3mZ6/eTOxmj+0gdqdaP+hlnm6f3x+1S88wd9OpwnBi3+A4rJdtLDSZuy/T3TnEjkg2npcrHdTlwu+zW0m3X5pQa75VRYwlg9hbr3yf2FsKjOPf/I0GBkiYc2Chfu8BgKtYSHvpXL/ChWXWrFkX5LxlTgqXm5uL3bt3448//iD/BEEQBEEoI5U8Kdx1112HjIyMYq9nZWXhuuuuK/N5S72ycqF2VBQEQRCEy53KrllZsWIFCgoKir2el5eH3377rcznLfVk5ULtqCgIgiAIQuVE9azs3LkTKSkpuu1yubB06dJime9LQ6knKxdqR8XKBA+7ra7oOngoZrabrjT9cLY+sTsqPnyzifpinRq38/Uy17NwHIqfN5zFEHcLppoCm4mmpHfYXEodDYnmvmE1/HVzXjSpG7H5bmIv7PCeXq5loboZHnqYCzozV/UWBUznEM26QtWahLD2lwYnS2VvK+YWNyt1LAU4++vIRrQxtMEONmZuC9ull78724ieJ5eeuHU43RLAobSDa264LkUNmwe7r04WTp3mNuxwFn4fzFLox1upPXrKR3o50sxXXnnIutF+vo2CL/zdZz6+qlvO6WXRqFQuLB6++yyXkmSlkq6stG7dGiaTCSaTyaO7JyQkBG+//XaZz1/qycqF2lFREARBEC57Kulk5dChQ9A0DXXr1sX69etRvbqx51xQUBBq1KgBi6Xs+zqVerLyz46KiYmJ+o6KiYmJ572joiAIgiAIlZOEhAQARfsFXghKPVm5UDsqCoIgCMLlTmUX2H788cc+6++///4ynbfUk5X77rtPL/+zo+Lu3btRp04dVKtWrUyNCERUHzf3/aezieNhp6HViDafInXhZrrsdVcEDe8OV/yvNnY73CbvM1Sec8Ji4poDQxtwiOU7Oa7khQFoThaA6l24boOj6neeevdBUpcweTWx3YeNNvJcNZluqlEJZn7pMKVNvF/4Zz/rVjU3vtuvpqDnbXLy9VjWF+p7oy001wg/l5qHpXj+ENp+VYNztWMfqZvUnTbputDdxA4xGe1Qt0IAgFAzbVO02dB5BLEcP7VsfxHbpjwDt2x4mNQVHKI5WW68Zguxu0YYnyHcnEHqHExroj5r55NCnfdprkbHV7rL0Gl12kY/+9pWNK+HULH0iGtN7O+OF5cbeMq9Ummp5JOV4cOHE9vpdCI3NxdBQUFwOBxlnqyUOc8KAPz++++wWCxo27btJTVREQRBEASh9Pz111/k39mzZ7Fnzx5069YNn3/+eZnPe16TlZtvvhnHjx8/n1MIgiAIgvA3Js3/v8pGgwYNMGHChGKrLqXhvLZE1LSL22sTJkzA6NGjMXz4cEyZMgVAUaKZJ554AnPnzkV+fj569OiBd999FzExMeV2Xb5bLQ+VrWo5q5eDmEsimKVhD2ZpwVX3Rw5zhajL9IDvlO58yT9XcVm4WAjo49toSPGDjdYQu1e44ara7aQrZh+ldCV2kBKKWpO5fTgfnjHe2yWcpl3/6GQXYj8d/x2xmwcpYdt+QlpVlxEPf+X9dNJl9HmeRo+NNNOl5VQXvR8pLsOddmMIDQd3mHmKcGMMnXadI3U8fX0w2Qma3vOvb3mLHevdzcVdhQ52HdXlku2mY7yjnbqB1J2gEx8+Seoa/kBdUfWC6U7Qbe1GeLXDRN1lHHX3Zwtz5fC068W3QzBILswn9scZHYldzWo8s9eH/0nq1qK1zzYKFUuWO08vR5iDfRxZSblAP6vTpk3Da6+9hpSUFLRq1Qpvv/02OnTo4Pd9c+fORd++fdGrVy98/fXXZb6+1WrFiRMn/B/o7f1lfudFZsOGDXjvvffQsmVL8vrjjz+OJUuW4Msvv0RkZCSGDh2Kf/3rX/j9998rqKWCIAiCEDjMmzcPI0eOxIwZM9CxY0dMmTIFPXr0wJ49e/RUJJ44fPgw/vvf/+LKK68s8bUWLVpEbE3TcPLkSbzzzjvo2rWrl3f557wmK++99165rmB44+zZs+jXrx/ef/99vPzyy/rrmZmZ+OCDDzBnzhw9Cc2sWbPQpEkTrF27Fp06dbrgbRMEQRCEcuMCrKxMnjwZgwcPxsCBAwEAM2bMwJIlS/Dhhx9i1KhRHt/jcrnQr18/vPDCC/jtt9887vfjid69exPbZDKhevXquO66684ry32pNSsDBgzAr7/+CgC49957ERoaWuaLl5QhQ4bglltuQffuNBRi06ZNcDqd5PXGjRujTp06WLNmDT+NTn5+PrKyssg/QRAEQahoSqJZ8fQblp+f7/F8BQUF2LRpE/mdNJvN6N69u8/fyRdffBE1atTAgw8+6PUYT7jdbvLP5XIhJSUFc+bMOa9cbKVeWcnMzET37t2RkJCAgQMHYsCAAeeV798fc+fOxebNm7Fhw4ZidSkpKQgKCkJUVBR5PSYmhuxLwBk/fjxeeOGFYq9nuM/B5S6av6kp0bnuxKJRX3qCNddok4ulKXfxcFGqMdhWUFUvrzrbkNQ9FE0HUoyPnNK5bhp+qaakr2+lc9JfOsz0eiwA7CyoopdtTOPx31pL6bH5xr3/HHFe2wcAK981dAMhj9H2OqxUr/PGsR7Erh5saAxGxSzzeZ08zfi8DW30fnANS02LoUPhIdD88Qhnob91remKRbUYXOekhnhPPk2XVJs5qEi9bXCyXk5gqett7M+uNFcIsROVJvMQ6XCmgVL1ITFMS8X1IRmFhs5m1PrlpK6znbaRh7u7ffxNlM+2rshUws75u6LN9BVVs8L1OSty6bYWtYKoBudkQZRebqDoVwAg6/t6xL4pztj+4KEq60ndA3W6Qbi43BPfpdhrhZoTwOGL3pYLQglWVjz9ho0bNw7PP/98sWNPnz4Nl8tVzAsSExOD3bt3FzseAFatWoUPPvgAW7duLWmri1FQUIBDhw6hXr16sFrPX3FS6pWVr7/+GsePH8cjjzyCefPmITExETfffDO++uorOJ1O/ycoBUePHsXw4cPx2WefITi4/IRUo0ePRmZmpv7v6NGj/t8kCIIgCAEA/w3LzMzE6NGjy+Xc2dnZ6N+/P95///0ypSTJzc3FoEGD4HA40KxZMyQnF/3x9dhjj2HChAllbleZQperV6+OkSNHYtu2bVi3bh3q16+P/v37Iy4uDo8//jj27dvn/yQlYNOmTTh16hTatm0Lq9UKq9WKlStXYurUqbBarYiJiUFBQUExX1pqaipiY2O9ntdutyMiIoL8EwRBEIQKR/P/z9NvmN3uOdKuWrVqsFgsSE1NJa97+508cOAADh8+jNtuu03/3f3444+xaNEiWK1WHDhwwGfzR48ejT/++AMrVqwgiwzdu3fHvHnzStERlPPKs3Ly5EksW7YMy5Ytg8ViQc+ePbF9+3Y0bdoUb7755vmcGgBw/fXXY/v27di6dav+r3379ujXr59ettlsWL7cWJbes2cPkpOT0blz5/O+viAIgiBcTMo7z0pQUBDatWtHfifdbjeWL1/u8XeycePGxX53b7/9dlx77bXYunUr4uPjfV7v66+/xjvvvINu3brBpLjWmzVr5nei44tSO5KcTicWLVqEWbNm4ccff0TLli0xYsQI3HvvvfoKxcKFCzFo0CA8/vjjZW4YAISHh6N58+bktdDQUFStWlV//cEHH8TIkSMRHR2NiIgIPPbYY+jcuXOZIoH+3aQTrH/76iceWqe/3oTtPh9mpjNYm+J3v3H9v0ld/F07iH36P3RwVHvP0KVonVuRujvmbCK2qnfheoRslm765g3/0cvT23xG6hrbqLuObwlQT0m1viGPDswr7JnETrQe0stND1HtRbabuu6OOo0Y+8ZBNFdHdJU8Yjs1Oo+OsRifj3/2Q056bIbb0HHUtVHRGdes+MpdwwlmkhaX4lzm6d25/iXSZPTFM9VpWL2LOanV7Q6OFFKdzD1bqdgt+xhdFfyjt5GHJQR04N5e6wp4Y/FxOtaKa0mMNrUNovcKoPqW0vRpLtO3TEq9Xi93jqC5eHqF0vGljgOuk4m1ZRC7RdBpYmeEGG0MN9P2vtV4LrHrKnqqcPbsf3aU3st+8WUPzRSEC8XIkSMxYMAAtG/fHh06dMCUKVOQk5OjRwfdf//9qFWrFsaPH4/g4OBiv7v/6EL5655IS0vzGA6dk5NDJi+lpdSTlZo1a8LtdqNv375Yv349WrduXeyYa6+9tpjo9ULx5ptvwmw248477yRJ4QRBEASh0nEBQpf79OmDtLQ0jB07FikpKWjdujWWLl2qi26Tk5NhNp+Xo0Wnffv2WLJkCR577DEA0Cco//vf/87L41Hqycqbb76J//u///MpeI2KisKhQ4e81p8PK1asIHZwcDCmTZuGadOmXZDrCYIgCMLF4kKl0x86dCiGDh3qsY7/rnJmz55d4uu8+uqruPnmm7Fz504UFhbirbfews6dO7F69WqsXLmyFC2mlHqy0r9//zJfrDLxdFJHr3ULj9HwRafiglnaYQapGwwa2qi6fTimNduIfZilum+phF/eWqud1/MAQN6nhgvglbqtfR77yD663H5tiJEu3WGmbpQc5m5SN3ROc9Hddw/m01C5XLfRpjgrDSWtbaUp6INZqns17JaHqTa08ZTzqpvC97KjRQlR97dzK7+uWTm1he0S7euvI1+p+IvOZZw4lPVDbO9d1GbnvnNo2RIh+htPKvOPrSV2cReY97/QuLsmxUXdct//1kYvb2lKUyL0aPYJsVX3mZPdu7bM7cN38YYShp7Ghk+shbreLMp95+13mGSHZkFQ6datG7Zu3YoJEyagRYsW+PHHH9G2bVusWbMGLVq0KPN5K026fUEQBEG45KmEGxVy6tWrh/fff79czymTFUEQBEEIFC6BycqFQCYrgiAIgiCcF2az2W+0j8lkQiGLcCwpMlkpA3fU9r+tdnkQbzvDXim5WttkVvz5N7YndbYfNxJ7egOamnw6DPvEwqakrkHVNGI/VdtIv89T8x9T0vYDQLjF0JIcLKChbVvOJRL75vDtxE4wGQM8zETDR3mo7DnNCDU96+bp6unDpIahcz0CD5FW07sDxcOgVbhuQ9XDFHufiV432220P90VeJqIO2tTXcx3xzeX+L08JHr4vj7EbvC0ca59r7UldSvr0n1Faim6J6554uH4mW7ex0b94UI6TjmNbYb+JdJM/+yNZPojtS961qLtF4SSUPbg3opl4cKFXuvWrFmDqVOnwu32rQv0hUxWBEEQBCFQqKRuoF69ehV7bc+ePRg1ahS+/fZb9OvXDy+++GKZz18+gdWCIAiCIJw35Z3BtiI4ceIEBg8ejBYtWqCwsBBbt27FRx99hISEhDKfUyYrgiAIgiCcN5mZmXj66adRv359/Pnnn1i+fDm+/fbbEmW+9Ye4gQKIMw/S7H4W0HwuXDPhi/c6Gjkp6nalKfIfTij5tvZn0x3EbpqUQuxgRUsCM03DHmPLInZd+ym93CKInofPmi3FHLeGxsDtZ51UzblRxUyTF/KcICq8f8+6qRDMya5Lzu0nRwu9Dj2PqlEBgD1OY7uAz8/wjI/5CDS4NuOHE1u9Hstz1Uys/xWxQ/cZfZ6nrSJ1W/PqELu61RhfwUzYxzUqDlbvULZvqM1y/vCcLWqeFa6P8jWeBKFMVIKVE09MmjQJEydORGxsLD7//HOPbqHzQSYrgiAIghAoVNLJyqhRoxASEoL69evjo48+wkcffeTxuAULFpTp/DJZEQRBEAThvLj//vvPa6NCf8hkRRAEQRAChMogoPVEafYPKgsyWbnIpC9uSOyrahr78rQInU/q4ixUywDYUVLyNCP/Q/B5THabjDpC7M5r9nk99odsuu/DWRdtr1MzdCevH7iR1J3aSvcRur/nL8S+O3KTXo6z0Fwd2wqCiH3fT8P18uIbp5K6cDPVMqhaBwfLq2JjuVJ4xhNV4+L0sb8PJ9dN21/svco68L4rAk+j4o8eca2Jre4ltOBsbVLXKYSOrziLcQ9cLG9PgvUAsSOJHolqk2wafXaymYZFVaVkuOi94rl4qlqM+2EGz9tDx4yqYVl0fAOpu73WFRAEv1TSycqFRqKBBEEQBEEIaGRlRRAEQRAChMrqBrrQyGSlHDjwupF+/L89vyV1N4TuIXaMZS2x7coW8zwM0o0QZhuL1+rSOlB8eX3BaSPF/tW1f/Ladk/s/6SNXq7ffwupG7X9X8T+uu1MvdyDpch3mKm7I1hJK39XBD3vvvpVif3a4R7Ebhh8Ui/HOGjYc/Mg6iqJq2NsU/Dskd6kzsrcQFWCjDTt/aqtIXUJVhp6neambi23ZixM8s9alz1ZLmVt18xcROEsHDbDbYSL860Sgrcl0/OmnkKgw9Pzq1Tdf5bYeVqGXq7O7lW4mfbTX24aKu8LJ/sB2OmsppezXfQ5O+qMJvbMxYbLsvPVf5K6d+N/JLa6RUOeRkPfFx/fBF/cWqudz3rhMkEmKx4RN5AgCIIgCAGNrKwIgiAIQoAgbiDPyGRFEARBEAIFmax4xKRp2mXfNVlZWYiMjMQ16AWriQeoXjx4qCP3eQcr2oYjhTQ0c+yx24g9OHalXu4WTH37POX58BNdiX3gCu9agE7bqDbjyaqGH97FnjI3G1o5ShpzGvBZnGyN6jqilSbzkOJgpvnYVWBcx8V0QDwcPNJshD3zdPu8n1Jd9L0fZ3TQy93Dd5C6VkE8dNa4l+o1ASBXo3369HFDr+Nm7X+u5vfEfrQUWycEInvfo+G8/71yqV6+IXQ3qXNq9H5EKpqWGAvVnXDOuqmuSU2b72ThyLkszDndbYxWVXcFANUs3kcyD2vmqfr5tgv5yjjwpfMRilOoObEC3yAzMxMREREV3Zzzou1/3vR7zOb3Hr8ILQksRLMiCIIgCEJAI24gQRAEQQgQRLPiGZmsCIIgCEKgIJMVj8hkJYDwl457dvIqvcyVNWNqLSG2zWToL8wsFTnP5zKixnJiL9vZWC9P/vEWUvdJ9BvEtpuMc3PNB9ewOGH45FNc1J+fp1E7zkI1BhbFY7mLpdePNNNjXcqwVvsBAKoxbYOaF8Op8ZTs9L1RZuo1/f54U718R9PNpC7ERNto86FtcLMvp4m1ftDL4UzfkkabWOlp+B+q01rwk5Hj595Ge9jRtKMcZiPvDb9XHCd7r0vRh3ANFN8qIcFq2HaTb22MOoZsJt/KLP4cqs/L2IN0PL1Yt63PcwnCpY5MVgRBEAQhQDBJzItHZLIiCIIgCIGCzFU8IpOVSsRRZRdjJ3ObpLlouF57O01Jr3JWo26TDDd1NVzj2KuXW99Od8V1sVk/3XmY+ii4G0hdbA830bDsXDd1bAWxpXiH2ajfcK4uqYu20pTtnYKNNtvY5sduFk7q0rzvjsxDlzljGi7Wy1Fm+nnczFGnnou7LOw+QrF5G0LZsTf/mUHs75tF+WxzoFM1OEcv81BfX24Vf26gHOZrU9PtW7iLiLlnrg7O8Hodfn/ylXQD3J1k85cWQWnGgr/as0rfn08QLnVksiIIgiAIAYJEA3lGJiuCIAiCECjIZMUjkhROEARBEISARlZWKhFjkozQ5vv3HCV1zy+8m9hL+r6ul7mWZHFObWJvOJtE7P+rsl4vx1pySZ2N+eHTXIb+JZfpP6qbme5E9dmbaYp5J2iK/93OUGK3Cjqnl/M0Omz/yI0n9tUhhmaFa19y3fS6YUo4Mg8l5enQuYaie0i28l4a0urrXFamm3EwHYr6Xt4GM/s817GU9N+jcqdpvy56t9e6XI1uYWBByfVG4Wws5rgN/devmY1J3c1VtrHrGs+Pg12H643sPvRG/lA/z452olG5XBE3kGdksiIIgiAIgYJMVjwikxVBEARBCBBkZcUzolkRBEEQBCGgkZWVSoqqywCA0BPUJz/sgKFheTHpa1KX4aKe92siqE6ggc3Qh0QqKc2B4nlWMt2GjmB7fk1S1zH4BLHVdPvZLO8Fz0CRVkjzxuyCoRv4dH8HUtcq5jixVUWIg+XmOFpItQAJppKnR3ew1Pe+cGkl1xxwfYtF1UGw83CdBt+moLKzsGl1vXzfsQOk7kgh/ewnCiP1ctOgM6Qu2MQ1KnS8NbSd0st54XT01bWmEzvMX34UBXUM8fvK4WOktBoX4RJFVlY8IpMVQRAEQQgQxA3kGZnKC4IgCIIQ0MjKSiUl1UVdEjFTVxNbm2qU733jMVI3+Ea6y3JcyF/E9jWD5enGHcpKd7ztDHyhhnnazPTPhxy2JP7UujuJXb//Fr0ci12kbtU7HYntqvWdck2exv8csdX06Gc1GtZcxUx3qz4ffLkELKaS/81QLCTXRNt8bHQXvVx7PB0TlY07a9MwbB6uf0Vwsl7eWVCV1DViYzGa7XodYzHuR7OgVFJnM/EAZe/4cvfxsHPu0isETSlwzEXvpXCZIhsZekQmK4IgCIIQIIgbyDPiBhIEQRAEIaCRlRVBEARBCBRkZcUjMlmppDw2ahixw7HW67H1nqB1s7+kWoDmbagWYGtetF7uHbaP1PGluEglnLeGhYfRevf9F5qov97O/LTfX/UOsVMOGun3v81oQ+oO7s4n9ptpV+nlCbEbSF20maW2V0Jcc1xUUxDG2ugvFLWklEajwo/loctOdkfeePADvfzm+CZlaF3g8nEjuq3CdclG+H51Szap4xoVh8l72Dm/r6XRoXANl2/ovUp30XG7syBWL0euohqczG6+9WAXg0+O/k7s/vFdK6gllzYm2WnBI+IGEgRBEAQhoJGVFUEQBEEIFMQN5BGZrAiCIAhCgCDRQJ6RyUolpdnI7cS+8oVjxL4p1PDnz/yrHalb+E4YsT+qSX3PXaoYac55WvxqTAtgNxlDiOsruO9f9ffzfCFmJgepZ6VDs6GSLqVrzCZS93LMemJPON1KL+ez3ClmloY9VdGpxLDP5mR5MGyg9b40LD61DezLqDQaFk51cyGxU0qloajcPFCnm15+P3kVqQs22fnhJYbfD6fm8nKk7xT5uVoBsfn42V5QhdjqNhgVpVH5gPVjutt4DvOYruwzpmFR6edHz7LouKElW5xD9TkzG9b1285LGsmz4hHRrAiCIAiCENDIyoogCIIgBAjiBvKMTFYEQRAEIVCQyYpHZLJSiVDzHKS5qAevvo3fSiOvRL/IjaRmwa2tiM3941ce2quXeV6SYBO9Tln1Fv5ylpTmvFamBXgk2vCHu5ink+sG9jkN3UCClebq4JRGo5LqonsQBStamSrmEJ/X8YWL+bMtTINz1En9/5cLhwupDsulnSV2jMV7nhWbieUHYlorVXvC89zw96r3x83uVT6oviiI5fGJtWYqVpzX9pYnrx2mOZic7IfSrXkf87ns86m7cC08RnVkHKvy03NlyElSNxOXuWZF8IhMVgRBEAQhQBA3kGdksiIIgiAIgYJEA3lEJisBxHfHN/us/9NpLDlbmNuBuwfyNWPJ2cZWcqvfvsfndZ5J6qCXeYrtbDcNx7SajTbxLe99hXX6w1fKcw6/7tFCY0E6wUpDl8PMNKT12uAsvWz2E5rMXT20jrb33TNdiP1Y1dVe38s/qy8XGHf7mFmTfvmrsWJleD3PpcYrdVsTe/LhNcTm90d13/C6fBaqvK3AcNu1CqLuvdRCmjI/TzPO62B5051sPG0+l0jsH5pH4GLTa/lQYn9zPd3m4pTLcK/FWTNInZ2N02y30W+Z7Hsim7mTkqxGP4WZbBAEf8hkRRAEQRACBHEDeUYmK4IgCIIQKMhkxSOSFE4QBEEQhIBGVlYCiOTCXGLzcMuUwnC9XM/2F3s31VvkKTqIB5W05KXl4UO9iX1V1X3E/nfkbr3sL4W+is909B7wpX/hOpoMt+H7b+6rEfCtXfCnmlHbxDVD9YJPETtXqfar7VHuHdev8GOd7LoHMo3Q5QimWQn/rRqxs688jUuVM24aHl5dyyG2Qym72Fjc76TP0qt39jOMNzJIXY0QGu7ep7oRslvVTK859WR32sau/Bm++DR8kKY12LArkdjzU9rq5d6xW32eK9Rs6HfUrQMAoHXwEWJHmQ3tD9e+XO6IG8gzMlkRBEEQhEDBLbMVT8hkRRAEQRACBZmreETW3wRBEARBCGhkZSWA+I3lXbjBcZjYW84108vpLppevIfjOLGrWcqe0l2lCssrwfO7bC4I1stNbdRHb9FoenG7kqqf60P85mQxeVeQOFiehqY2I225U6NDnOsTnIp+hKfi5210snwoZiXnCd+GoFfYAdZK41gny+Phy2fPj83XaN6YDfk1iP3vRCMvTtV9NOX8tSE09wiOGcW7a3f22obKCM+74ot90zoSu8GQdcR2djfG+JDav5G6qhbax41sRt6ebQV064Pro3cR27KbjqfPG1+cFPu++KJJLLGTn0/UyxPq0rrGE+hnd75tfFeMSFhG6hrZ6PdIpuLq6J9waY2980U0K56RyYogCIIgBAqSwdYj4gYSBEEQBCGgCeiVlfHjx2PBggXYvXs3QkJC0KVLF0ycOBGNGjXSj8nLy8MTTzyBuXPnIj8/Hz169MC7776LmJiYCmx52biGuX1crL5JsOHqWZrR0ue57gwzwlKv2ErPtKG1hR/ulZnxvxKbh91+mhWvlx+d+S9Sd/Pd1O3wZPVVetnBdqvlaeR5KLAv+M636W7DjrbQOr5Dsy8xGz9vHnPJZLiMZfw4K32UbMy1k+tW38vdQNSNpYZ1c7dPtpu61kasvofYdeYabV448y1SF2YKJnbPWm0hAEmN6K6/TTbRezm8mtGP1S207iC9HWQcR5lpKoJGDrq7eRTb0fzzctppee5RurWD6n495qLjadCu/sQOvekgses8b5zrwJzWpG7fczQ8+e2Eb/Tya4d7kLqrqu8n9upW3nfBvty5UG6gadOm4bXXXkNKSgpatWqFt99+Gx06dPB47Pvvv4+PP/4YO3bsAAC0a9cOr776qtfjLwYBvbKycuVKDBkyBGvXrsWyZcvgdDpx4403IifH0EY8/vjj+Pbbb/Hll19i5cqVOHHiBP71r3/5OKsgCIIgBChaCf6Vknnz5mHkyJEYN24cNm/ejFatWqFHjx44deqUx+NXrFiBvn374pdffsGaNWsQHx+PG2+8EcePH/d4/MUgoFdWli5dSuzZs2ejRo0a2LRpE6666ipkZmbigw8+wJw5c3DdddcBAGbNmoUmTZpg7dq16NSpU0U0WxAEQRAChsmTJ2Pw4MEYOHAgAGDGjBlYsmQJPvzwQ4waNarY8Z999hmx//e//2H+/PlYvnw57r///ovSZk5Ar6xwMjOLojyio6MBAJs2bYLT6UT37kZmyMaNG6NOnTpYs2aNx3MAQH5+PrKyssg/QRAEQahoTJrm95+n37D8/HyP5ysoKMCmTZvI76TZbEb37t19/k6q5Obmwul06r+9FUFAr6youN1ujBgxAl27dkXz5s0BACkpKQgKCkJUVBQ5NiYmBikpKV7PNX78eLzwwgsXsrllYrCftPjW2rX08q5XqCZnQ9U6xH7rM2NQRXy+ltRlfV+P2A2i0oj9dvwPimWnbWCaj7vCD+nl19rS1OMcmxK+WyyE2O3kh7P3GvNqriXxl6q/pHDdDP+sVcw0HLyapRRzfeVQHgLtq/28n3gbB7Sm93b98w30MtfNXM702EH/IPnkgOF7f6/eJ6Qu0Uq3b3AptyDNRUUqM9JoCv0Hqhmhza8fp7oNvr3Bu0dWEfuLY8YPR2lCyaccphoVJxszZkUjxVMPvNeY/gUdnkz1VAuym+tlm2k5qesVRkOx093Gz0mojfahaFRKQQm+zjz9ho0bNw7PP/98sWNPnz4Nl8tVTMcZExOD3bt3FzveE08//TTi4uLIhOdiU2kmK0OGDMGOHTuwatUq/wf7YfTo0Rg5cqRuZ2VlIT4+3sc7BEEQBCEw4L9hAGC3270cfX5MmDABc+fOxYoVKxAcHOz/DReISjFZGTp0KBYvXoxff/0VtWvX1l+PjY1FQUEBMjIyyOpKamoqYmNjPZypCLvdfsFurCAIgiCUFVMJIiFL8xtWrVo1WCwWpKamktf9/U4CwOuvv44JEybgp59+QsuWviNQLzQBvU6saRqGDh2KhQsX4ueff0ZSUhKpb9euHWw2G5YvN5Yn9+zZg+TkZHTuLFkRBUEQhEpGOUcDBQUFoV27duR30u12Y/ny5T5/JydNmoSXXnoJS5cuRfv27Uv7Kcodk6YFbrq8Rx99FHPmzME333xDcqtERkYiJKRIP/DII4/gu+++w+zZsxEREYHHHnsMALB69WqP5/REVlYWIiMjcQ16wcpyXgQSh8YbA2v8ndTXfF0I1ej03fd/evnwaSqK+rHTdGJPTruG2A6z4W8eV2MTqeM5QdR08H8WUH++C1RfEWMxznuskOo/stx0ebG1PYPYqt7FzlLbO1neknSXYfO8GLz9vjCz9lvKSQOS66b+fN5+FZ7Gn29LwPOw7CgwPl+4mV6nuoU6w48WGsc+k1Rx+RO88eqh9cT+IbsFsRcdM+wn6/9I6urZqA5r7bm6xG4bclgvc40K3/5AzZGTy74t52W2I/aPKU308vHTUaSu7r1biR3+WzViv5+4SGkDve+pLj5mjLEZ40c75Va+4s1M88Theqqfzxm5XyLMeaTu+hCaR0YdizwnUb/4rj6ve74Uak6swDfIzMxERETEBb3Wheb6a171e8zyFc+U6pzz5s3DgAED8N5776FDhw6YMmUKvvjiC+zevRsxMTG4//77UatWLYwfPx4AMHHiRIwdOxZz5sxB167GvQsLC0NYWJi3y1xQAtoNNH160Y/qNddcQ16fNWsWHnjgAQDAm2++CbPZjDvvvJMkhRMEQRAEAejTpw/S0tIwduxYpKSkoHXr1li6dKkuuk1OToZZSVI4ffp0FBQU4K677iLn8SbivRgE9GSlJIs+wcHBmDZtGqZNm3YRWiQIgiAIF44LlcF26NChGDp0qMe6FStWEPvw4cMXphHnQUBPVi53xh7cTGwLDJdMiiuS1PEQ11eSFurl4/FRpC7STENyX4ihO8keKTSWivewiOJmNvpe1VXSJIguR2czd4dFcWFEW+iSss1El415On41XJm7QvhuyRaL9zBo7trxRXm5fQDApXmPR0x10bpbVz+ql8e3W0jqujuoSM7CPk8jm5Fr4YSL1i3LpeHtW3MMe8YROgYeTvAdRn8xSLDS+9g3krokE+xGKHAt61+kLsNN3YyJQTRsWHX9nGH9lOoKJXaU2dgxuL6NPme8TcueukovX/PCTlKXDAoPZX5q3Q16+c6qG0mdmcWztlXcpA4TDQvmO3VnagXKsexZYePntJt+vmCTcQ9y3FTQ6QbddZm2IWDVBYGP9J1HAlpgKwiCIAiCICsrgiAIghAgmMonx+Ulh0xWBEEQBCFQEDeQR2SyUsF02mb4hB+uso7U8YDWcEVrYnOmkzqeWr2RzZieJ1hpGKcNNHw3X2P73Ct+7O9ZuGiTaJqe2UxCiul57RbvYcIRZvrnQ5abaljsJuofV6/DtSRcDxKi+PDPaVQ34/aRpIDrWfh5y0vDwsNSo81Um1FrttFve5rVJHWt7SeIHW72rsHZmk+zMk/adSOxY3sb6dIfRsVrVDjZTD/BP2u4oiUZsbsPqfus2Wxi17RQXYcagrz4LB3jh85VJ/ad0Rv0cjV3JqmLYecd/+4MvRxsos/V0+gIX6z+upVe7jpgH6nr4ThI7HCz8Xzw7Rp4KLy6RYMa1g8AUWY6pmNYqH9MiPE9w8Oa8zR6P/Y7je+ne9c/SuoS8QcE4XyQyYogCIIgBAqysOIRmawIgiAIQoBQknT7lyMSDSQIgiAIQkAjKysXmdnJdNfoSLPh887X6NzxqIva1S2GT7iRjfqebT7SsvO8JP5wK77o5iFHaR3zj9vKuD0B1384zPQ85ZUPxY6Sn/di5VXhuhme6+KT96fo5VDWpg35VYi9JqeB1+v8MoqmOHdEWbwcGZhwPQUfIzUs2XrZ9r+qpG7DeKrX6cLGsUu5BX0jtpG6oEh6P9RW8O0PuP6ojV19Lmn7q/xOt734qyvVnX380BS9vCkvkdTxzEHqM13smeR5hxTNSgHT0eSwcRrFxpuq/wphj85ZLZ/Y6vYazhQHqXN2p9sS9Ht7iV7+oonvzfQuO2RlxSMyWREEQRCEQEFClz0ikxVBEARBCBBEs+IZ0awIgiAIghDQyMpKOXN2Kd2Kvn8dmjuF+55zlW3V+X4a0SwXiUvRtISw/UB8wf3qXDNRXIdi2FFmug18Jtvvp6qS+6U0mg9fmg5PkDZfoPwn5QlvE9+vRSXMTHPKRCi+f36vMtxUC7C6FR0HR5/topdveWUNqVtxgulbPvfapIDgnvgu/g/6Gwfoc5b4Ot13Z0VuIrF7hRkaFr63Tmn0UhyrohfhOX62L2lM7Ad3LCV2nmY8p1vP0n2cGrP8OjUtxniyMo2K2cSfb+PzRDMdEH/2ub6tsFi2J4MThfQ6Ts1oR2idLFJnG01t9fPMOLKf1FlY94ezZ6k046JSIisrHpHJiiAIgiAECjJZ8Ujg/UkqCIIgCIKgICsrCm/+uQbh4UXzt9rWEK/HZbLU8CdcxvJnSuGfpK6GhW6jzgN9g5Ut29V0+p5QXQm+0sYDpVvK5mm0H9p9n14ekLCW1CVY9xK7vNwzeSzlfz5bQldDvIvNsX2GCfNl7osTvsvdXOq942654kvt3sNSU51R9NBOLYlZ89pjevnp6jRM/h62ncMDT47Qy3GvrcalBHeXXec4TOwwE61X4c+Wr2eJH2tTnmceQvxwvyXsWDrmt+cZ4da/HaPu5FN5YcTuWPd70sKywt0+avsBOm5VlzUArD5Xj9iqa+e2xB2k7rcXOxPb8obRb7kavWa0iT4PTtbHT+w3vmMjzEXfxTnZbqygj0LlRaKBPCKTFUEQBEEIECQayDPiBhIEQRAEIaCRlRVBEARBCBRkZcUjMllRqGm1I8JatNik+qm5poBvXR+rhBE2stFQX754lcf8s3mKT9iXv5jDtQw8fFHVi/Dz5DI9SBr7PNqsGnp56ZBmpO56B9WsnHXTlNsqPCS3NISZeJp87zoO7uJV+8JfKCbvtwuFmvKca3kK2f1R+3RK+hWkbu5umrZ84icLiG1WeoP3y/b82vRYRYKw7+O2pK7B/ZtRmXmzfhNif8C2uVC1Jvy58zUi/IXcq8+ai/3oXBFykNizTncj9uEO5/RyHHaSumxQ0pKNMRJl5lsAeN8CQx2HQPHnw5c+J5Vt/7H7XE1iX+MwQpBHVqNh809Opdq3CHOwXnaDho7z55vTIugvvfzP58m2XUJCD5mseETcQIIgCIIgBDSysiIIgiAIgYKsrHhEJiuCIAiCEChcQh6t8kQmKwrmv//z9LpKdQvttv1Ow8+7W6P+4nQXzY9wdfApYtsU/ULxXCN01O4sCNfL9W00fXVNi/e8EdwPfdpFNRJfZLYntmmg0cacq9JI3bd/Nid2n3Ajn4LDT54YFa4TCAPVt3BdB9UK+E5l74LRj/w6XEdgLXtmdZ/6BV95cM6yPD3pbnrfN+fH6uV2jkOkrl6rVGIfd1YhdmKQcb/OuOiHSysMJ3ZWY+O6CbHppG7yYao5GJlI82RUNlbn1SJ2rDVTL7cKOscPJ1iU5yeb3atcdpvtSpevOhdP6hJtdAuAITV+IfaT6OSzHSrvnjFSznN9CN+Kgz45vj3/XNOVrmyvUZ3lwR9cleqA1M/On4xgE0/zb3Qc/37ylwtJPdc/35+FpvN4kAMMCV32jGhWBEEQBEEIaGRlRRAEQRACBVlZ8YhMVhTcStCcugNyPnPP2NiSZozFiAGNZvsqR5npkj9PHe3wsbhVoPFU3kY7nGw8n9VoCLFDCQfkrpAY5sbqHka3CNgdYbghqBMI+L5ZFLHdO4wc149V2Ufqzmf3Wo7qFipNsDEPgzzNdo2OUZa2eQixv7BOdcmcu5d4iGiu2xgXPFS8OtsJ9xaH4aLg7d9V8Bexj7siid0iyHA1OFgbhkTtIva/b9mul2/78z5S991Z6u576dAGvfxTNq1b2ZJuTRGzJoLYr9c2UsP3j++KiiC9kLpjn9vUWy/P7PAJqatuySF2sPLcHS2kny3ZGU3spWda6OX1axuRujn/epvY8RZ6b989YrhVHk2gYc2cb7406kc+ssbHkb5dP/7ChE+7DLd2bSv9HrSw77KflZ2tbwtNJnU8nNoN725d/n3FUVMi/HMe2yXkBoJbJiueEDeQIAiCIAgBjaysCIIgCEKgIG4gj8hkRRAEQRACBZmseEQmKwokdNnkVl737Q89UhjitS7BSsMiz7CU1Xlmww8cafad9rt5UL7X2nQWjuxUtDJVzLR93IfN25joOKOX2/9JE33/fLoxsX9Q5Au9jtDzcG2Mel0HC688H3zdnzX51Ff+3ONDiB3yzXqv731wLw0b7hVKQ0/VUPMfcmNJXWv7CWIHm4wvIK5RUVOPc066qBbpt1y6/cEd4VRvFG02+tVfanVVN/B6wy9JXd+VDxE7uIPxWY/kUZ3GmX+3JnZ1jWpj7IreaO7R1aTut7xqxJ7WoCEuBAubVid24dtGn2e4adj/utx6xFY1XQlWmjKgLkshkFpoaIh21Y8hdVzjkcNC38PNxv2yr6TjKf0cbWOnqD/0cjDTePgKm/cXJsxTBjS0GRqv00xLMWjoSHrsc0Y/9Qo7ytrkK8yf1nE9ni/+GdOiZ7j0kcmKIAiCIAQKsrLiEZmsCIIgCEKgINFAHpHVM0EQBEEQAhpZWVEww6T7dFUvqsWPZkXVfMzKaEfqrgvb6fVYAIi2GDkDuKYgjKW3Vn3R3M+7xxlF7HhrhnKs73TiaW46DI7nGedKstNMK+8lfUXs6758RC+PPlqV1L1e5xtiRytb2ftKVe+p3ld6bo6aF2cp20rAl0aFk8Zyaji1VC9HAk7N96Ok3lmH2eb1OIB+VrXPAOChqP3svFyP5L1veCp1lTZ22t+bu9OcIKquppC1wfk8TRvPx7FN0Sc5WRt4vx0bbaSRrz2e6lvKkwaPrdPL01Hf57Efzx+olwt30jFRp8sxYh88YWhwZnWdReqCTfSzH2S5XxKtZ/XyxIQFpC6WCdjmZTfQy6kumv+kjtX7+OJ6Fj5e+NYV6vcM/8v23bffInZDW5ByLN0+w5eOxl+uF95GX+e6JPDz3Xi5IpMVQRAEQQgURLPiEZmsCIIgCEKgIJoVj8hkRcENTV9iVJey7cV2DKXLdNEWY422SyhNOf/TWRpqOiJ6K7HVNO1Olqqfo6as5kvtnYMziL2twHAP3LH2flL3ZvsviF3Lkkns7lUM19UNDpo2O489R4OaGEv1bo22adTR24j9bK3v9HIST93tJ8w2VzNCKG3Fwi3pe1flherlzf9uAQoN9U3+0qiv83/bSd2+czT0NDd8N7FtyvL0FcG0n2IstP2qK5Gn5ofJe5p/voMuh4eaqnBXGl/iV6/D+5u7qtTruIttA8GvS+vV54WnUg83UxelWXkEPjn6O6mrqFT9te80xoylOg2Bvvv/thE7I9YIMY63nCV1eez5CDXRrR9SXIbrhIc520z0XiYq2yq4ioUje3cFchdKFtsBnIdTRyluyBMu6tqpaqZh9b6u66vurJt+7xXbkoSNGZ66X7g8kMmKIAiCIAQK4gbyiExWBEEQBCFQkMmKRyR0WRAEQRCEgEZWVhRsJgtsPjQABtSHqmoB2tjp9vLNgzYRe10+DX10KX7sK+xUO8LTsKv+Zq4/4DoO1V+edM8fpG4qaMp8jrpVfRjTTGzNDyX2e9/fqJd7XruR1F0TvcfrNbjOhMM/X6bbsFNcdNiGmqj+JU9TtD25VBfQbDO97oyq0402UdkJctx0LBwtpL7yt1K66+W1RxJJ3acdPyB2olXV3FBdANd4qPoWX6n4/cH1LHbQ9vsKGbUW2+zBoLj+gF7HBe/aGH7NFkF/EftcG0PD8nzK9ew6eagIeuwwUup3C91A6toEef++cGr02eFh29luOja/yGqpl++KoM+sGfRcVwUb22DYTTR8nd93/iyRNjCNSoabP1tGfZyF6czYuVT9i7/v0Qy3ca63z3SjbSqkY75TOA2V/1eYES7+j6bLXzqDSoWsrHhEJiuCIAiCECi4Jc+KJ8QNJAiCIAhCQCMrK4IgCIIQKIgbyCMyWVFwaW64znOcOJjGw8y0GXGWbGK/dPwWvRxfawmpCzN7z1fB4RoQNfdLaTnjNvIp1LTQNrS1ZxD7u7tf18sZbvrZVZ0GUDxfjQrP85EH+l5VPxJuonkZ9jhrEPuMksbctYvmvXm06ipix1m9b3dw1kTzSHybE0fseg4j10X31nRbBZ4n44zLuD9u5l///RxN936DY69e5mOA4y6WO8VXjg06fo4UGn1c20L1LHaT968Gf+nO+XXcPnLMhLPtBCZeMV8vz2xY1+d1LhQTD60jdjWLMd6C2XOWr9HnTM0jw59JC/u6dZtoX9wRbuhUqpnps8TPlanoXdTtJYDiOXKK5fVRj2XnTWO5YHKUc0eZff9cpCs6lGgTz5NEx+l3OQ318rcHmpO6xjXotha/uRsR+xrHYb1c8+9xe0ml4JfJikfEDSQIgiAIQkAjKyuCIAiCEChIun2PyGRFEARBEAIETXZd9ohMVhTUvYHo1ui+vWWqToD7Tgt5bgWN+pPX7DX88mPct5O6/9b6gdjVLYaGohrTpNhYHoZgH5oDjppXBeC+QXrecOZLjzQbR6v79xS1gR6r5u7g/eIrrwoAVLcYfcxzOIxIpJoPXzyc0M1r3aLjNIdGmJnuhdIn/CR9g2Lz9qt5JDhfZVMffTTbQ4bqaHznjyiNr55rFxym0rzXeB64JoWf1+njy9bF2pvL7nNF6VRUsjR635PMxufhz1Xxz+49pwnf04brguKsxr32lecGAAqU6+4rpHlWcty0/dluo76t/QRtLxtf4WaqB8tWtGKhbH8ivofP6nPxermpnT4rn6TT5+7rHzvp5ZfumEvq+HunnbqW2OoeSrX+/l64pPKsyMqKR0SzIgiCIAhCQCMrK4IgCIIQKEg0kEdksqKQrzmR/3fonhqCyJcY+dK7mt6aL97y7c83nGtA7JjlxtLwwapVSd1Pkc2I7VTCJA/k0K3q/1uTuoyeTuoIb9ReG0bs4i4lwy4WLq15DzHmS+R82Vt1JfgKpwToUi8AxCsusH7xnX2+t6zcXusKYs8/tpbY3B2oLuNb2Tb2IRbqAlPHTK+wHaQu3EzHlxV0Wd8XpVn+trE2hirjlqeCt4C3yeKxDABW1gR+HZVdTvo8DN/Xj9hBOOL1vReLINYXbmWscjeWrxBvDnd9nnRRt6n6HDqYq5Pf5xiLMUaCTedI3bp8B7FXZhmhv1FV6HYgNhNtUzBLC6BuXXGQpcFvYDtD7A/+3dto729bQKH9FvSM8XnqBp0ideFs+4zro7ynBTj3t+v53KWk85AMth4RN5AgCIIgCAGNrKwIgiAIQqAgbiCPyGRFEARBEAIETdxAHpHJisI5zQXr37Na1etr9rPduarF4Nuzcx93z9BdxO76krH9ebSF+osPF1JtyWNvPaqXY99aTeqehneNCmdADRqq7Obhl4ofe0t+KKnbnV+T2I2VMMPmQXQrAauZahfUvrGzoVcsxNtN/eMPJFE9ycXgztqdiP1+Mu23GEvJQ01V1NBkoLgWRtW3+AsT5qnW1XBr/l5+HTUte57mPdSaU2yM+9ELqOG8rx7vSeq0N2uwoytes2JmIbr8GS4pXNvG752daX3SXUY/BVv5lgWsjcq9rGKmGqdrg7OI3aLGL3rZyT5KtJney6Muau/Mr6WX12fTsPIDV+SxNnGdisHBOa2J/WSbr/Vyuot+z32f1YrYuWwbj2ZVUoz3/h36ni0/8Jc8MlkRBEEQhEBB3EAekcmKIAiCIAQKkhTOIxINJAiCIAhCQCMrKwqnXWbk/e2zjTQbPvwYi83bWwBQ3/Q5N/Xj5jN/fm0r9S/XVspm0Os4NZqGnetUykpdKz2vjeWKULdzj7bkkrp/he8ltjrbdbC8Ktxn71bOy/NG5LJ8NJPqXXyNij8imQbH3zYM9FiTUqbnKdZPikKBp2/n+gme8rx020QY9cV0S0xDZPaRS8gf6rkGx64kdfNfoOPrxrcMDVeBRq/09E99iN3w0fWlbEnJGMP0UXOPGs+dxUTHLddaqX3KxzjPPxPOxtMv54zcSTGWdJ9tNPtIr8Nzv1RThkGuRp8zVbcEABYX1S7luY3626tQTcqbaOK1DXfsTCN2YtDn7LpG3qTtefGk7tOlVxM7fhnNR/PjY4318nNNvgMA5Oa5ANB8LZWWSylnTDkikxVBEARBCBA0cQN5RCYrgiAIghAoyMqKRy6Zycq0adPw2muvISUlBa1atcLbb7+NDh06lOocp10OnHMVLc2Gm43wP74Uz5dz1aV3vttuPpskV2Er877SpR8tjPDb5pJwfAFN25/qosvnoSy9dYbyrNhY0KSdha2GmYxQWb4kftadT2y+W7JKdiV4QNXQUgBwK0vZEWYaas1dO5mKe5D3A9+lWLXCmGutWA+y4ePLDeHL3ZTD2mBhdp4Szh7Mxr+v9PoA3Vm5RRANq20Vu5zYTsUdleqin/3pa5YQu8XBo3r5xbptfbahNGR9X4/YKS7DDRTP0tOnMreJmjJffTY8wbenaGQz3BhnmVuu+Dgo+TYLqluIh7NnuqmLZWt+HWK3CDb6uKmNurifPbiV2BkuI+FDLtv5+dfsRsRu5jiulxODqMtIq02vY/uJup9q/GSUZ6IonLpQcwLYBsE7pf2N/PLLLzFmzBgcPnwYDRo0wMSJE9GzZ0+vx19oLgmB7bx58zBy5EiMGzcOmzdvRqtWrdCjRw+cOnWJ+DAFQRCEywLNrfn9V1pK+xu5evVq9O3bFw8++CC2bNmC3r17o3fv3tixY4fH4y8Gl8RkZfLkyRg8eDAGDhyIpk2bYsaMGXA4HPjwww8rummCIAiCUHI0t/9/paS0v5FvvfUWbrrpJjz55JNo0qQJXnrpJbRt2xbvvPPO+X66MlPp3UAFBQXYtGkTRo8erb9mNpvRvXt3rFmzxuN78vPzkZ9vLOFnZmYCAHLPGoMg22aUg8x0cNhMfHldeZ+LHlvAJsFZLDOlr+XcnDy65FzIlPwlxZVL3TFns2kbslmbziozd5dG25fFtth1K9k+C9kS8zmWVZLvzkvawI4t62e9kGSzfrOoU30zd+XQG69+Pt4PhT7cQG6T7y8mJ+vzQuX40riB+Ljljh11zBeYeJSLb5eEem4eTcP/WlLdQGdZm87lUZdLjlMZe+U4Xlw53p+XLPaNydsYpGQ15vfOV/8DwFnl8wRZ2LHsXHY/48IbfHfiYvY51sfKd4P6nQjQ/geAXMVNmstc5/m59P6cU9zlQWbmXs2lbqCS3NtCFB2jXSYJ1fhvGADY7XbY7cVdj2X5jVyzZg1GjhxJXuvRowe+/vrr8298WdEqOcePH9cAaKtXryavP/nkk1qHDh08vmfcuHEaivYsl3/yT/7JP/l3ifw7cODAxfjZqXA8/YaNGzfO47Fl+Y202WzanDlzyGvTpk3TatSoUS7tLwuVfmWlLIwePZrMGjMyMpCQkIDk5GRERkZWYMsCm6ysLMTHx+Po0aOIiCgf8e+lhvRRyZB+KhnSTyUjMzMTderUQXR0dEU35aLAf8MAeFxVuZSo9JOVatWqwWKxIDU1lbyempqK2NhYj+/xtlwWGRkpXwglICIiQvrJD9JHJUP6qWRIP5UMs/mSkGH6xdtvmCfK8hsZGxtbquMvBpX+zgYFBaFdu3ZYvtwIgXS73Vi+fDk6d+5cgS0TBEEQhIqlLL+RnTt3JscDwLJlyyr0N7XSr6wAwMiRIzFgwAC0b98eHTp0wJQpU5CTk4OBAwdWdNMEQRAEoULx9xt5//33o1atWhg/fjwAYPjw4bj66qvxxhtv4JZbbsHcuXOxceNGzJw5s8I+wyUxWenTpw/S0tIwduxYpKSkoHXr1li6dCliYmJK9H673Y5x48Zd8j6/80X6yT/SRyVD+qlkSD+VDOkn3/j7jUxOTiYutC5dumDOnDl47rnn8Mwzz6BBgwb4+uuv0bx584r6CDBp2mUS6yUIgiAIQqWk0mtWBEEQBEG4tJHJiiAIgiAIAY1MVgRBEARBCGhksiIIgiAIQkBz2U9Wpk2bhsTERAQHB6Njx45Yv359RTepQhk/fjyuuOIKhIeHo0aNGujduzf27NlDjsnLy8OQIUNQtWpVhIWF4c477yyWQOhyYsKECTCZTBgxYoT+mvSRwfHjx3HfffehatWqCAkJQYsWLbBx40a9XtM0jB07FjVr1kRISAi6d++Offv2VWCLLz4ulwtjxoxBUlISQkJCUK9ePbz00ktkr5vLsZ9+/fVX3HbbbYiLi4PJZCq2N01J+iQ9PR39+vVDREQEoqKi8OCDD+Ls2bMX8VMI5UKFJfoPAObOnasFBQVpH374ofbnn39qgwcP1qKiorTU1NSKblqF0aNHD23WrFnajh07tK1bt2o9e/bU6tSpo509e1Y/5uGHH9bi4+O15cuXaxs3btQ6deqkdenSpQJbXXGsX79eS0xM1Fq2bKkNHz5cf136qIj09HQtISFBe+CBB7R169ZpBw8e1H744Qdt//79+jETJkzQIiMjta+//lrbtm2bdvvtt2tJSUnauXPnKrDlF5dXXnlFq1q1qrZ48WLt0KFD2pdffqmFhYVpb731ln7M5dhP3333nfbss89qCxYs0ABoCxcuJPUl6ZObbrpJa9WqlbZ27Vrtt99+0+rXr6/17dv3In8S4Xy5rCcrHTp00IYMGaLbLpdLi4uL08aPH1+BrQosTp06pQHQVq5cqWmapmVkZGg2m0378ssv9WN27dqlAdDWrFlTUc2sELKzs7UGDRpoy5Yt066++mp9siJ9ZPD0009r3bp181rvdru12NhY7bXXXtNfy8jI0Ox2u/b5559fjCYGBLfccos2aNAg8tq//vUvrV+/fpqmST9pmlZsslKSPtm5c6cGQNuwYYN+zPfff6+ZTCbt+PHjF63twvlz2bqB/tk2u3v37vpr/rbNvhzJzMwEAH2DsE2bNsHpdJJ+a9y4MerUqXPZ9duQIUNwyy23kL4ApI9UFi1ahPbt2+P//u//UKNGDbRp0wbvv/++Xn/o0CGkpKSQvoqMjETHjh0vq77q0qULli9fjr179wIAtm3bhlWrVuHmm28GIP3kiZL0yZo1axAVFYX27dvrx3Tv3h1msxnr1q276G0Wys4lkcG2LJw+fRoul6tYltuYmBjs3r27gloVWLjdbowYMQJdu3bVMxempKQgKCgIUVFR5NiYmBikpKRUQCsrhrlz52Lz5s3YsGFDsTrpI4ODBw9i+vTpGDlyJJ555hls2LABw4YNQ1BQEAYMGKD3h6fn8HLqq1GjRiErKwuNGzeGxWKBy+XCK6+8gn79+gGA9JMHStInKSkpqFGjBqm3Wq2Ijo6+bPutsnLZTlYE/wwZMgQ7duzAqlWrKropAcXRo0cxfPhwLFu2DMHBwRXdnIDG7Xajffv2ePXVVwEAbdq0wY4dOzBjxgwMGDCgglsXOHzxxRf47LPPMGfOHDRr1gxbt27FiBEjEBcXJ/0kCLiMo4HKsm325cTQoUOxePFi/PLLL6hdu7b+emxsLAoKCpCRkUGOv5z6bdOmTTh16hTatm0Lq9UKq9WKlStXYurUqbBarYiJibns++gfatasiaZNm5LXmjRpguTkZADQ++Nyfw6ffPJJjBo1Cvfccw9atGiB/v374/HHH9c3lpN+Kk5J+iQ2NhanTp0i9YWFhUhPT79s+62yctlOVsqybfblgKZpGDp0KBYuXIiff/4ZSUlJpL5du3aw2Wyk3/bs2YPk5OTLpt+uv/56bN++HVu3btX/tW/fHv369dPLl3sf/UPXrl2Lhb7v3bsXCQkJAICkpCTExsaSvsrKysK6desuq77Kzc0lG8kBgMVigdvtBiD95ImS9Ennzp2RkZGBTZs26cf8/PPPcLvd6Nix40Vvs3AeVLTCtyKZO3euZrfbtdmzZ2s7d+7UHnroIS0qKkpLSUmp6KZVGI888ogWGRmprVixQjt58qT+Lzc3Vz/m4Ycf1urUqaP9/PPP2saNG7XOnTtrnTt3rsBWVzxqNJCmSR/9w/r16zWr1aq98sor2r59+7TPPvtMczgc2qeffqofM2HCBC0qKkr75ptvtD/++EPr1avXJR+SyxkwYIBWq1YtPXR5wYIFWrVq1bSnnnpKP+Zy7Kfs7Gxty5Yt2pYtWzQA2uTJk7UtW7ZoR44c0TStZH1y0003aW3atNHWrVunrVq1SmvQoIGELldCLuvJiqZp2ttvv63VqVNHCwoK0jp06KCtXbu2optUoQDw+G/WrFn6MefOndMeffRRrUqVKprD4dDuuOMO7eTJkxXX6ACAT1akjwy+/fZbrXnz5prdbtcaN26szZw5k9S73W5tzJgxWkxMjGa327Xrr79e27NnTwW1tmLIysrShg8frtWpU0cLDg7W6tatqz377LNafn6+fszl2E+//PKLx++jAQMGaJpWsj45c+aM1rdvXy0sLEyLiIjQBg4cqGVnZ1fApxHOB5OmKSkSBUEQBEEQAozLVrMiCIIgCELlQCYrgiAIgiAENDJZEQRBEAQhoJHJiiAIgiAIAY1MVgRBEARBCGhksiIIgiAIQkAjkxVBEARBEAIamawIQiXlmmuuwYgRI8r03tmzZ8NkMsFkMpX5HOXBihUr9Hb07t27wtohCEJgI5MVQaikLFiwAC+99FKZ3x8REYGTJ0+Sc1xzzTUwmUyYMGFCseNvueUWmEwmPP/882W+JqdLly44efIk7r777nI7pyAIlx4yWRGESkp0dDTCw8PL/H6TyYTY2Nhi54iPj8fs2bPJa8ePH8fy5ctRs2bNMl/PE0FBQYiNjUVISEi5nlcQhEsLmawIwnmQlpaG2NhYvPrqq/prq1evRlBQENkNlrNhwwbccMMNqFatGiIjI3H11Vdj8+bNev2KFSsQFBSE3377TX9t0qRJqFGjBlJTUwEUdwO9++67aNCgAYKDgxETE4O77rqrTJ/p1ltvxenTp/H777/rr3300Ue48cYbUaNGDXJsYmIiXnrpJfTt2xehoaGoVasWpk2bRo7JyMjAf/7zH8TExCA4OBjNmzfH4sWLy9Q2QRAuT2SyIgjnQfXq1fHhhx/i+eefx8aNG5GdnY3+/ftj6NChuP76672+Lzs7GwMGDMCqVauwdu1aNGjQAD179kR2djYAYyLSv39/ZGZmYsuWLRgzZgz+97//ISYmptj5Nm7ciGHDhuHFF1/Enj17sHTpUlx11VVl+kxBQUHo168fZs2apb82e/ZsDBo0yOPxr732Glq1aoUtW7Zg1KhRGD58OJYtWwYAcLvduPnmm/H777/j008/xc6dOzFhwgRYLJYytU0QhMsTa0U3QBAqOz179sTgwYPRr18/tG/fHqGhoRg/frzP91x33XXEnjlzJqKiorBy5UrceuutAICXX34Zy5Ytw0MPPYQdO3ZgwIABuP322z2eLzk5GaGhobj11lsRHh6OhIQEtGnTpsyfadCgQbjyyivx1ltvYdOmTcjMzMStt97qUa/StWtXjBo1CgDQsGFD/P7773jzzTdxww034KeffsL69euxa9cuNGzYEABQt27dMrdLEITLE1lZEYRy4PXXX0dhYSG+/PJLfPbZZ7Db7QCKJhFhYWH6v3/cRampqRg8eDAaNGiAyMhIRERE4OzZs0hOTtbPGRQUhM8++wzz589HXl4e3nzzTa/Xv+GGG5CQkIC6deuif//++Oyzz5Cbm1vmz9OqVSs0aNAAX331FT788EP0798fVqvnv206d+5czN61axcAYOvWrahdu7Y+UREEQSgLsrIiCOXAgQMHcOLECbjdbhw+fBgtWrQAAMTFxWHr1q36cdHR0QCAAQMG4MyZM3jrrbeQkJAAu92Ozp07o6CggJx39erVAID09HSkp6cjNDTU4/XDw8OxefNmrFixAj/++CPGjh2L559/Hhs2bEBUVFSZPtOgQYMwbdo07Ny5E+vXry/TOUQ4KwhCeSArK4JwnhQUFOC+++5Dnz598NJLL+Hf//43Tp06BQCwWq2oX7++/u+fycrvv/+OYcOGoWfPnmjWrBnsdjtOnz5NznvgwAE8/vjjeP/999GxY0cMGDAAbrfbazusViu6d++OSZMm4Y8//sDhw4fx888/l/lz3Xvvvdi+fTuaN2+Opk2bej1u7dq1xewmTZoAAFq2bIljx45h7969ZW6HIAiCrKwIwnny7LPPIjMzE1OnTkVYWBi+++47DBo0yGfES4MGDfDJJ5+gffv2yMrKwpNPPklWIVwuF+677z706NEDAwcOxE033YQWLVrgjTfewJNPPlnsfIsXL8bBgwdx1VVXoUqVKvjuu+/gdrvRqFGjMn+uKlWq4OTJk7DZbD6P+/333zFp0iT07t0by5Ytw5dffoklS5YAAK6++mpcddVVuPPOOzF58mTUr18fu3fvhslkwk033VTmtgmCcHkhKyuCcB6sWLECU6ZMwSeffIKIiAiYzWZ88skn+O233zB9+nSv7/vggw/w119/oW3btujfvz+GDRtGwoJfeeUVHDlyBO+99x4AoGbNmpg5cyaee+45bNu2rdj5oqKisGDBAlx33XVo0qQJZsyYgc8//xzNmjU7r88XFRXl1fX0D0888QQ2btyINm3a4OWXX8bkyZPRo0cPvX7+/Pm44oor0LdvXzRt2hRPPfUUXC7XebVLEITLC5OmaVpFN0IQhIvL7NmzMWLECGRkZJzXeRITEzFixIjzTtn/wAMPICMjA19//fV5nUcQhEsTWVkRhMuUzMxMhIWF4emnn66wNvz2228ICwvDZ599VmFtEAQh8BHNiiBchtx5553o1q0bAJQ5Wqg8aN++vR4tFRYWVmHtEAQhsBE3kCAIgiAIAY24gQRBEARBCGhksiIIgiAIQkAjkxVBEARBEAIamawIgiAIghDQyGRFEARBEISARiYrgiAIgiAENDJZEQRBEAQhoJHJiiAIgiAIAY1MVgRBEARBCGj+H6fTsnzBwaxNAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "brightness_temp = p21c.brightness_temperature(\n",
-    "    ionized_box = ionized_box,\n",
-    "    perturbed_field = perturbed_field,\n",
-    "    spin_temp = spin_temp\n",
-    ")"
+    "plotting.coeval_sliceplot(ionized_field, \"xH_box\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now lets plot our brightness temperature, which has been evolved from high redshift with spin temperature fluctuations:"
+    "### Brightness Temperature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use what we have to get the brightness temperature:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 82,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:13:05.570514Z",
-     "start_time": "2020-02-29T22:13:05.307722Z"
+     "end_time": "2020-02-29T22:10:45.153348Z",
+     "start_time": "2020-02-29T22:10:45.122062Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plotting.coeval_sliceplot(brightness_temp);"
+    "brightness_temp = p21c.brightness_temperature(\n",
+    "    ionized_box=ionized_field, \n",
+    "    perturbed_field=perturbed_field\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also check what the result would have been if we had limited the maximum redshift of heating. Note that this *recalculates* all previous spin temperature and ionized boxes, because they depend on both ``z_heat_max`` and ``zprime_step_factor``."
+    "This has only a single field, ``brightness_temp``:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 83,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-29T22:13:52.943512Z",
-     "start_time": "2020-02-29T22:13:08.799693Z"
+     "end_time": "2020-02-29T22:10:45.355868Z",
+     "start_time": "2020-02-29T22:10:45.155043Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-02-29 15:13:08,824 | INFO | Existing init_boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:08,840 | INFO | Existing z=19.62816486020931 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:11,438 | INFO | Existing z=18.645871295437438 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:11,447 | INFO | Existing z=19.62816486020931 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:14,041 | INFO | Existing z=17.71035361470232 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:14,050 | INFO | Existing z=18.645871295437438 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:16,667 | INFO | Existing z=16.81938439495459 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:16,675 | INFO | Existing z=17.71035361470232 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:19,213 | INFO | Existing z=15.970842280909132 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:19,222 | INFO | Existing z=16.81938439495459 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:21,756 | INFO | Existing z=15.162706934199171 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:21,764 | INFO | Existing z=15.970842280909132 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:24,409 | INFO | Existing z=14.393054223046828 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:24,417 | INFO | Existing z=15.162706934199171 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:26,938 | INFO | Existing z=13.66005164099698 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:26,947 | INFO | Existing z=14.393054223046828 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:29,504 | INFO | Existing z=12.961953943806646 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:29,517 | INFO | Existing z=13.66005164099698 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:32,163 | INFO | Existing z=12.297098994101567 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:32,171 | INFO | Existing z=12.961953943806646 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:34,704 | INFO | Existing z=11.663903803906255 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:34,712 | INFO | Existing z=12.297098994101567 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:37,257 | INFO | Existing z=11.060860765625003 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:37,266 | INFO | Existing z=11.663903803906255 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:39,809 | INFO | Existing z=10.486534062500002 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:39,817 | INFO | Existing z=11.060860765625003 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:42,378 | INFO | Existing z=9.939556250000003 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:42,387 | INFO | Existing z=10.486534062500002 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:44,941 | INFO | Existing z=9.418625000000002 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:44,950 | INFO | Existing z=9.939556250000003 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:47,518 | INFO | Existing z=8.922500000000001 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:47,528 | INFO | Existing z=9.418625000000002 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:50,077 | INFO | Existing z=8.450000000000001 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:50,086 | INFO | Existing z=8.922500000000001 spin_temp boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:52,626 | INFO | Existing z=8.0 perturb_field boxes found and read in (seed=521414794440).\n",
-      "2020-02-29 15:13:52,762 | INFO | Existing brightness_temp box found and read in (seed=521414794440).\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "ionized_box = p21c.ionize_box(\n",
-    "    spin_temp = spin_temp,\n",
-    "    zprime_step_factor=1.05,\n",
-    "    z_heat_max = 20.0\n",
-    ")\n",
-    "\n",
-    "brightness_temp = p21c.brightness_temperature(\n",
-    "    ionized_box = ionized_box,\n",
-    "    perturbed_field = perturbed_field,\n",
-    "    spin_temp = spin_temp\n",
-    ")\n",
-    "\n",
     "plotting.coeval_sliceplot(brightness_temp);"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, it's very similar!"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:21cmfast]",
+   "display_name": ".venv",
    "language": "python",
-   "name": "conda-env-21cmfast-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1546,7 +1364,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.11.9"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/docs/tutorials/lightcones.ipynb b/docs/tutorials/lightcones.ipynb
index 5fddd9b0e..adc9e6b5b 100644
--- a/docs/tutorials/lightcones.ipynb
+++ b/docs/tutorials/lightcones.ipynb
@@ -18,29 +18,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The main entry point into creating lightcones in 21cmFAST is the ``run_lightcone`` function. The function takes a few different arguments, most of which will be familiar to you if you've gone through the coeval tutorial. All simulation parameters can be passed (i.e. ``user_params``, ``cosmo_params``, ``flag_options`` and ``astro_params``). As an alternative to the first two, an ``InitialConditions`` and/or ``PerturbField`` box can be passed. \n",
+    "The main entry point into creating lightcones in 21cmFAST is the ``run_lightcone`` function. The function takes a few different arguments, most of which will be familiar to you if you've gone through the coeval tutorial.\n",
     "\n",
-    "Furthermore, the evolution can be managed with the ``zprime_step_factor`` and ``z_heat_max`` arguments. \n",
+    "The main concept to grasp here is that a lightcone is made simply by running a number\n",
+    "of Coeval simulations at different redshifts, and interpolating them together.\n",
+    "The redshifts that are computed are simply set by the `InputParameters.node_redshifts`\n",
+    "parameter.\n",
     "\n",
     "There are in principle multiple ways to set up a lightcone: there is a choice of which line-of-sight slices are desired (eg. regular in frequency, comoving distance or redshift?), and also whether the box itself should be regular in transverse comoving distance (like the coeval boxes) or exist on an angular lattice. In 21cmFAST < 3.3.0, all lightcones were output in regular, comoving, rectilinear coordinates such that each \"cell\" was cubic and had the size of the coeval cubes used to make it. In 21cmFAST v3.3+, there is much more flexibility. You use a `Lightconer` subclass to tell the code what your lightcone should look like, and you can specify your own sub-class if the builtin ones don't do what you require. The default lightconer is the same regular rectilinear grid as was used in 21cmFAST<3.3."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "Note\n",
-    "\n",
-    "Until v4, you will be able to call `run_lightcone` by directly passing a `redshift` and `max_redshift` (which was the only way to do it pre-3.3). However, since this API is now deprecated, we will exclusively work directly with the new interface, using `Lightconer` subclasses, in this tutorial.\n",
-    "    \n",
-    "</div>"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-03-16T17:09:54.467808Z",
@@ -52,7 +42,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "21cmFAST version is 3.2.1.dev196+g12e3ef7\n"
+      "21cmFAST version is 3.4.1.dev400+gf3997fed\n"
      ]
     }
    ],
@@ -66,11 +56,22 @@
     "import numpy as np\n",
     "from scipy.spatial.transform import Rotation\n",
     "from astropy import units as un\n",
+    "from tempfile import mkdtemp\n",
+    "\n",
     "%matplotlib inline\n",
     "\n",
     "print(f\"21cmFAST version is {p21c.__version__}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cache=p21c.OutputCache(mkdtemp())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -84,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,9 +96,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
    "source": [
     "lcn = p21c.RectilinearLightconer.with_equal_cdist_slices(\n",
     "    min_redshift=7.0,\n",
@@ -115,9 +128,20 @@
     "Notice the `index_offset` argument above. When creating the lightcone, the slice of the coeval box that makes the first slice of the lightcone is arbitrary. By setting `index_offset` to zero, we set the very back of the lightcone to be equal to the last slice of the coeval. The default is to have the *first* slice of the lightcone correspond to the *first* slice of the coeval."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When running the lightcone itself, like the `run_coeval` function, the `run_lightcone`\n",
+    "function is a generator -- at every node redshift, it yields the updated lightcone, filled\n",
+    "to the currently computed redshift. This allows you to do whatever you like with the lightcone\n",
+    "at each iteration. However, if you just want the final lightcone, you can call the\n",
+    "convenience method `.exhaust_lightcone()` instead of `run_lightcone`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-29T22:25:11.665270Z",
@@ -130,35 +154,32 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/inputs.py:510: UserWarning: The USE_INTERPOLATION_TABLES setting has changed in v3.1.2 to be default True. You can likely ignore this warning, but if you relied onhaving USE_INTERPOLATION_TABLES=False by *default*, please set it explicitly. To silence this warning, set it explicitly to True. Thiswarning will be removed in v4.\n",
-      "  warnings.warn(\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vx\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vy\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vz\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vx_2LPT\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vy_2LPT\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vz_2LPT\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n"
+      "/home/sgm/work/21cmfast/21cmfast/src/py21cmfast/wrapper/inputs.py:1168: UserWarning: Resolution is likely too low for accurate evolved density fields\n",
+      " It Is recommendedthat you either increase the resolution (DIM/BOX_LEN) orset the EVOLVE_DENSITY_LINEARLY flag to 1\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
    "source": [
-    "lightcone = p21c.run_lightcone(\n",
+    "idx, z, coeval, lightcone = p21c.exhaust_lightcone(\n",
     "    lightconer=lcn,\n",
     "    global_quantities=(\"brightness_temp\", 'density', 'xH_box'),\n",
-    "    direc='_cache',\n",
-    "    user_params=user_params\n",
+    "    inputs=p21c.InputParameters(\n",
+    "        user_params=user_params, \n",
+    "        node_redshifts=p21c.wrapper.inputs.get_logspaced_redshifts(\n",
+    "            min_redshift=7.0,\n",
+    "            z_step_factor=1.1,\n",
+    "            max_redshift=12.0,\n",
+    "        ),\n",
+    "        random_seed=1234\n",
+    "    ),\n",
+    "    cache=cache\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-29T22:25:12.028481Z",
@@ -168,7 +189,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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AIMOcOXP0f//3f3r66af16KOP6phjjpEkfeYzn2nlkmF99fvf/16XXXZZs+7j8ccf13XXXaePf/zj+sQnPqGtt95ap556arjeNddco27duumxxx5r1vJJ0imnnMIXGgEAaCLe7AIAABUzZ84cnXjiiZo/f746dOigkSNH6jvf+Y523HHH1i5a1djQ3uxqq5577rn6DzgMHjxYHTp0aNb9vfjii1q2bJkkacCAARX7OioAANWIh10AAAAAAACoGoQxAgAAAAAAoGrwsAsAAAAAAABVg4ddAAAAAAAAqBo87AIAAAAAAEDV4GEXAAAAAAAAqgYPuwAAAAAAAFA1eNgFAAAAAACAqsHDLgAAAAAAAFQNHnYBAAAAAACgavCwCwAAAAAAAFWDh10AAAAAAACoGv8fZlrVCHLRrX8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -184,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-29T22:25:13.893642Z",
@@ -194,7 +215,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 2 Axes>"
       ]
@@ -240,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -371,7 +392,7 @@
        "True"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,16 +410,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sgm/work/21cmfast/21cmfast/src/py21cmfast/wrapper/inputs.py:1168: UserWarning: Resolution is likely too low for accurate evolved density fields\n",
+      " It Is recommendedthat you either increase the resolution (DIM/BOX_LEN) orset the EVOLVE_DENSITY_LINEARLY flag to 1\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs_ang = lightcone.inputs.evolve_input_structs(APPLY_RSDS=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sgm/work/21cmfast/21cmfast/.venv/lib/python3.11/site-packages/cosmotile/__init__.py:409: UserWarning: Install numba for a speedup of cloud_in_cell\n",
+      "  fine_field = cloud_in_cell_los(fine_field, fine_rsd)\n"
+     ]
+    }
+   ],
    "source": [
-    "ang_lightcone = p21c.run_lightcone(\n",
+    "idx, z, coeval, ang_lightcone = p21c.exhaust_lightcone(\n",
     "    lightconer=ang_lcn,\n",
     "    global_quantities=(\"brightness_temp\", 'density', 'xH_box'),\n",
-    "    direc='_cache',\n",
-    "    user_params=user_params, \n",
-    "    flag_options={\"APPLY_RSDS\": False}\n",
+    "    inputs=inputs_ang,\n",
+    "    cache=cache\n",
     ")"
    ]
   },
@@ -411,16 +459,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "py21cmfast.outputs.AngularLightcone"
+       "py21cmfast.drivers.lightcone.AngularLightcone"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -438,11 +486,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "bt = ang_lightcone.brightness_temp.reshape((user_params.HII_DIM , user_params.HII_DIM, ang_lightcone.brightness_temp.shape[-1]))"
+    "bt = ang_lightcone.lightcones['brightness_temp'].reshape(\n",
+    "    (user_params.HII_DIM , user_params.HII_DIM, len(ang_lightcone.lightcone_distances))\n",
+    ")"
    ]
   },
   {
@@ -454,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -463,13 +513,13 @@
        "Text(0, 0.5, 'Transverse (y) dimension [cMpc]')"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x1600 with 3 Axes>"
       ]
@@ -480,13 +530,24 @@
    ],
    "source": [
     "fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12,16))\n",
-    "ax[0].imshow(lightcone.brightness_temp[:, :, 0], origin='lower', extent=(0, user_params.BOX_LEN, 0, user_params.BOX_LEN), cmap='EoR', vmin=-150, vmax=30)\n",
+    "kw = {\n",
+    "    \"origin\": 'lower', \n",
+    "    \"extent\": (0, user_params.BOX_LEN, 0, user_params.BOX_LEN), \n",
+    "    \"cmap\": 'EoR', \n",
+    "    \"vmin\": -150, \n",
+    "    \"vmax\": 30\n",
+    "}\n",
+    "\n",
+    "ax[0].imshow(\n",
+    "    lightcone.lightcones['brightness_temp'][:, :, 0], \n",
+    "    **kw\n",
+    ")\n",
     "ax[0].set_title(\"Rectilinear Lightcone Face\")\n",
-    "ax[1].imshow(bt[:, :, 0], origin='lower',extent=(0, user_params.BOX_LEN, 0, user_params.BOX_LEN), cmap='EoR', vmin=-150, vmax=30)\n",
+    "ax[1].imshow(bt[:, :, 0], **kw)\n",
     "ax[1].set_title(\"Angular Lightcone Face\")\n",
     "\n",
-    "btrsd = ang_lightcone.lightcones['brightness_temp_with_rsds'].reshape((user_params.HII_DIM , user_params.HII_DIM, ang_lightcone.brightness_temp.shape[-1]))\n",
-    "ax[2].imshow(btrsd[:, :, 0], origin='lower',extent=(0, user_params.BOX_LEN, 0, user_params.BOX_LEN), cmap='EoR', vmin=-150, vmax=30)\n",
+    "btrsd = ang_lightcone.lightcones['brightness_temp_with_rsds'].reshape(bt.shape)\n",
+    "ax[2].imshow(btrsd[:, :, 0], **kw)\n",
     "ax[2].set_title(\"Angular Lightcone Face (RSDs)\")\n",
     "\n",
     "ax[0].set_xlabel(\"Transverse (x) dimension [cMpc]\")\n",
@@ -503,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -512,13 +573,13 @@
        "Text(0, 0.5, 'Transverse (y) dimension [cMpc]')"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x1600 with 3 Axes>"
       ]
@@ -529,12 +590,12 @@
    ],
    "source": [
     "fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12,16))\n",
-    "ax[0].imshow(lightcone.brightness_temp[:, :, -1], origin='lower', extent=(0, user_params.BOX_LEN, 0, user_params.BOX_LEN), cmap='EoR', vmin=-150, vmax=30)\n",
+    "ax[0].imshow(lightcone.lightcones['brightness_temp'][:, :, -1], **kw)\n",
     "ax[0].set_title(\"Rectilinear Lightcone Face\")\n",
-    "ax[1].imshow(bt[:, :, -1], origin='lower',extent=(0, user_params.BOX_LEN, 0, user_params.BOX_LEN), cmap='EoR', vmin=-150, vmax=30)\n",
+    "ax[1].imshow(bt[:, :, -1], **kw)\n",
     "ax[1].set_title(\"Angular Lightcone Face\")\n",
     "\n",
-    "ax[2].imshow(btrsd[:, :, -1], origin='lower',extent=(0, user_params.BOX_LEN, 0, user_params.BOX_LEN), cmap='EoR', vmin=-150, vmax=30)\n",
+    "ax[2].imshow(btrsd[:, :, -1], **kw)\n",
     "ax[2].set_title(\"Angular Lightcone Face (RSDs)\")\n",
     "\n",
     "ax[0].set_xlabel(\"Transverse (x) dimension [cMpc]\")\n",
@@ -572,47 +633,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/inputs.py:510: UserWarning: The USE_INTERPOLATION_TABLES setting has changed in v3.1.2 to be default True. You can likely ignore this warning, but if you relied onhaving USE_INTERPOLATION_TABLES=False by *default*, please set it explicitly. To silence this warning, set it explicitly to True. Thiswarning will be removed in v4.\n",
-      "  warnings.warn(\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vx\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vy\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vz\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vx_2LPT\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vy_2LPT\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n",
-      "/home/smurray/miniconda3/envs/emu/lib/python3.10/site-packages/py21cmfast/_utils.py:821: UserWarning: Trying to remove array that isn't yet created: hires_vz_2LPT\n",
-      "  warnings.warn(f\"Trying to remove array that isn't yet created: {k}\")\n"
+     "ename": "NameError",
+     "evalue": "name 'p21c' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241m*\u001b[39m_, lightcone_norsd \u001b[38;5;241m=\u001b[39m \u001b[43mp21c\u001b[49m\u001b[38;5;241m.\u001b[39mexhaust_lightcone(\n\u001b[1;32m      2\u001b[0m     lightconer\u001b[38;5;241m=\u001b[39mlcn,\n\u001b[1;32m      3\u001b[0m     global_quantities\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrightness_temp\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdensity\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxH_box\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      4\u001b[0m     cache\u001b[38;5;241m=\u001b[39mcache,\n\u001b[1;32m      5\u001b[0m     inputs \u001b[38;5;241m=\u001b[39m inputs_ang\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      8\u001b[0m lightcone_withsubcell \u001b[38;5;241m=\u001b[39m p21c\u001b[38;5;241m.\u001b[39mrun_lightcone(\n\u001b[1;32m      9\u001b[0m     lightconer\u001b[38;5;241m=\u001b[39mlcn,\n\u001b[1;32m     10\u001b[0m     global_quantities\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrightness_temp\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdensity\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxH_box\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m     11\u001b[0m     cache\u001b[38;5;241m=\u001b[39mcache,\n\u001b[1;32m     12\u001b[0m     inputs\u001b[38;5;241m=\u001b[39minputs_ang\u001b[38;5;241m.\u001b[39mevolve_input_structs(SUBCELL_RSD\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m     13\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'p21c' is not defined"
      ]
     }
    ],
    "source": [
-    "lightcone_norsd = p21c.run_lightcone(\n",
+    "*_, lightcone_norsd = p21c.exhaust_lightcone(\n",
     "    lightconer=lcn,\n",
     "    global_quantities=(\"brightness_temp\", 'density', 'xH_box'),\n",
-    "    direc='_cache',\n",
-    "    user_params=user_params,\n",
-    "    flag_options={\"APPLY_RSDS\": False}\n",
+    "    cache=cache,\n",
+    "    inputs = inputs_ang\n",
     ")\n",
     "\n",
-    "lightcone_withsubcell = p21c.run_lightcone(\n",
+    "*_, lightcone_withsubcell = p21c.exhaust_lightcone(\n",
     "    lightconer=lcn,\n",
     "    global_quantities=(\"brightness_temp\", 'density', 'xH_box'),\n",
-    "    direc='_cache',\n",
-    "    user_params=user_params,\n",
-    "    flag_options={\"SUBCELL_RSD\": True}\n",
+    "    cache=cache,\n",
+    "    inputs=inputs_ang.evolve_input_structs(SUBCELL_RSD=True),\n",
     ")"
    ]
   },
@@ -670,7 +720,7 @@
     "los_displacement_pix = topix(ang_lightcone.lightcones['los_velocity'])\n",
     "cax =ax[0].imshow(los_displacement_pix[...,0], origin='lower', vmin=-1, vmax=1)\n",
     "\n",
-    "los_displacement_rec_pix = topix(lightcone.velocity_z)\n",
+    "los_displacement_rec_pix = topix(lightcone.lightcones['velocity_z'])\n",
     "cax = ax[1].imshow(-los_displacement_rec_pix[..., 0], origin='lower', vmin=-1, vmax=1)\n",
     "plt.colorbar(cax, ax=ax[1])\n",
     "\n"
@@ -746,14 +796,14 @@
     "ax[0, 2].set_title(r\"$T_{\\rm norsd} - T_{\\rm fullrsd}$\")\n",
     "\n",
     "\n",
-    "rct_norsd_lin = lightcone_norsd.brightness_temp[:, :, 0] - lightcone.brightness_temp[:, :, 0]\n",
+    "rct_norsd_lin = lightcone_norsd.lightcones['brightness_temp'][:, :, 0] - lightcone.lightcones['brightness_temp'][:, :, 0]\n",
     "cax= ax[1, 0].imshow(rct_norsd_lin, origin='lower',extent=extent, vmin=-3, vmax=3)\n",
     "ax[1, 0].set_ylabel(\"RECT\")\n",
     "\n",
-    "rct_norsd_full = (lightcone_norsd.brightness_temp[:, :, 0] - lightcone_withsubcell.brightness_temp[:, :, 0])\n",
+    "rct_norsd_full = (lightcone_norsd.lightcones['brightness_temp'][:, :, 0] - lightcone_withsubcell.lightcones['brightness_temp'][:, :, 0])\n",
     "cax= ax[1, 2].imshow(rct_norsd_full, origin='lower',extent=extent, vmin=-3, vmax=3)\n",
     "\n",
-    "rct_lin_full = (lightcone.brightness_temp[:, :, 0] - lightcone_withsubcell.brightness_temp[:, :, 0])\n",
+    "rct_lin_full = (lightcone.lightcones['brightness_temp'][:, :, 0] - lightcone_withsubcell.lightcones['brightness_temp'][:, :, 0])\n",
     "cax= ax[1, 1].imshow(rct_lin_full, origin='lower',extent=extent, vmin=-3, vmax=3)\n",
     "\n",
     "ax[2, 0].set_ylabel(\"DIFF\")\n",
@@ -811,7 +861,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(2, 1, sharex=True)\n",
-    "ax[0].imshow(lightcone.brightness_temp[:, 0])\n",
+    "ax[0].imshow(lightcone.lightcones['brightness_temp'][:, 0])\n",
     "ax[1].imshow(bt[:, 0])"
    ]
   },
@@ -1372,9 +1422,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (21cmfast)",
+   "display_name": ".venv",
    "language": "python",
-   "name": "21cmfast"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1386,7 +1436,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.9"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/src/py21cmfast/__init__.py b/src/py21cmfast/__init__.py
index 38da93b02..74a231f57 100644
--- a/src/py21cmfast/__init__.py
+++ b/src/py21cmfast/__init__.py
@@ -14,28 +14,24 @@
         # package is not installed
         __version__ = "unknown"
 
-# This just ensures that the default directory for boxes is created.
-from os import mkdir as _mkdir
-from os import path
-
-from . import cache_tools, lightcones, plotting, wrapper
+from . import lightcones, plotting, wrapper
 from ._cfg import config
+from ._data import DATA_PATH
 from ._logging import configure_logging
-from .cache_tools import query_cache
-from .drivers.coeval import Coeval, run_coeval
-from .drivers.lightcone import LightCone, exhaust_lightcone, run_lightcone
-from .drivers.param_config import InputParameters, get_logspaced_redshifts
+from .drivers.coeval import Coeval, generate_coeval, run_coeval
+from .drivers.lightcone import LightCone, generate_lightcone, run_lightcone
 from .drivers.single_field import (
     brightness_temperature,
     compute_halo_grid,
     compute_initial_conditions,
     compute_ionization_field,
+    compute_spin_temperature,
     compute_xray_source_field,
     determine_halo_list,
     perturb_field,
     perturb_halo_list,
-    spin_temperature,
 )
+from .io.caching import CacheConfig, OutputCache
 from .lightcones import AngularLightconer, RectilinearLightconer
 from .run_templates import create_params_from_template
 from .utils import get_all_fieldnames
@@ -48,7 +44,9 @@
     AstroParams,
     CosmoParams,
     FlagOptions,
+    InputParameters,
     UserParams,
+    get_logspaced_redshifts,
     global_params,
 )
 from .wrapper.outputs import (
@@ -64,8 +62,3 @@
 )
 
 configure_logging()
-
-try:
-    _mkdir(path.expanduser(config["direc"]))
-except FileExistsError:
-    pass
diff --git a/src/py21cmfast/_cfg.py b/src/py21cmfast/_cfg.py
index c65a5155f..b2afc5c54 100644
--- a/src/py21cmfast/_cfg.py
+++ b/src/py21cmfast/_cfg.py
@@ -21,8 +21,6 @@ class Config(dict):
 
     _defaults = {
         "direc": "~/21cmFAST-cache",
-        "regenerate": False,
-        "write": True,
         "cache_param_sigfigs": 6,
         "cache_redshift_sigfigs": 4,
         "ignore_R_BUBBLE_MAX_error": False,
diff --git a/src/py21cmfast/cache_tools.py b/src/py21cmfast/cache_tools.py
deleted file mode 100644
index 2e4c277e6..000000000
--- a/src/py21cmfast/cache_tools.py
+++ /dev/null
@@ -1,249 +0,0 @@
-"""A set of tools for reading/writing/querying the in-built cache."""
-
-import glob
-import logging
-import os
-import re
-import warnings
-from collections import defaultdict
-from os import path
-from typing import Tuple, Union
-
-from ._cfg import config
-from .wrapper import outputs
-
-logger = logging.getLogger(__name__)
-
-
-def readbox(
-    *,
-    direc=None,
-    fname=None,
-    hsh=None,
-    kind=None,
-    seed=None,
-    redshift=None,
-    load_data=True,
-):
-    """
-    Read in a data set and return an appropriate object for it.
-
-    Parameters
-    ----------
-    direc : str, optional
-        The directory in which to search for the boxes. By default, this is the
-        centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
-    fname: str, optional
-        The filename (without directory) of the data set. If given, this will be
-        preferentially used, and must exist.
-    hsh: str, optional
-        The md5 hsh of the object desired to be read. Required if `fname` not given.
-    kind: str, optional
-        The kind of dataset, eg. "InitialConditions". Will be the name of a class
-        defined in :mod:`~wrapper`. Required if `fname` not given.
-    seed: str or int, optional
-        The random seed of the data set to be read. If not given, and filename not
-        given, then a box will be read if it matches the kind and hsh, with an
-        arbitrary seed.
-    load_data: bool, optional
-        Whether to read in the data in the data set. Otherwise, only its defining
-        parameters are read.
-
-    Returns
-    -------
-    dataset :
-        An output object, whose type depends on the kind of data set being read.
-
-    Raises
-    ------
-    IOError :
-        If no files exist of the given kind and hsh.
-    ValueError :
-        If either ``fname`` is not supplied, or both ``kind`` and ``hsh`` are not supplied.
-    """
-    direc = path.expanduser(direc or config["direc"])
-
-    if not (fname or (hsh and kind)):
-        raise ValueError("Either fname must be supplied, or kind and hsh")
-
-    zstr = f"z{redshift:.4f}_" if redshift is not None else ""
-    if not fname:
-        if not seed:
-            fname = kind + "_" + zstr + hsh + "_r*.h5"
-            files = glob.glob(path.join(direc, fname))
-            if files:
-                fname = files[0]
-            else:
-                raise OSError("No files exist with that kind and hsh.")
-        else:
-            fname = kind + "_" + zstr + hsh + "_r" + str(seed) + ".h5"
-
-    kind = _parse_fname(fname)["kind"]
-    cls = getattr(outputs, kind)
-
-    if hasattr(cls, "from_file"):
-        inst = cls.from_file(
-            fname, direc=direc, load_data=load_data
-        )  # for OutputStruct
-    else:
-        inst = cls.read(fname, direc=direc)  # for HighlevelOutputStruct
-
-    return inst
-
-
-def _parse_fname(fname):
-    patterns = (
-        r"(?P<kind>\w+)_(?P<hash>\w{32})_r(?P<seed>\d+).h5$",
-        r"(?P<kind>\w+)_z(?P<redshift>\d+.\d{1,4})_(?P<hash>\w{32})_r(?P<seed>\d+).h5$",
-    )
-    for pattern in patterns:
-        match = re.match(pattern, os.path.basename(fname))
-        if match:
-            break
-
-    if not match:
-        raise ValueError(
-            "filename {} does not have correct format for a cached output.".format(
-                fname
-            )
-        )
-
-    return match.groupdict()
-
-
-def list_datasets(*, direc=None, kind=None, hsh=None, seed=None, redshift=None):
-    """Yield all datasets which match a given set of filters.
-
-    Can be used to determine parameters of all cached datasets, in conjunction with :func:`readbox`.
-
-    Parameters
-    ----------
-    direc : str, optional
-        The directory in which to search for the boxes. By default, this is the centrally-managed
-        directory, given by the ``config.yml`` in ``.21cmfast``.
-    kind: str, optional
-        Filter by this kind (one of {"InitialConditions", "PerturbedField", "IonizedBox",
-        "TsBox", "BrightnessTemp"}
-    hsh: str, optional
-        Filter by this hsh.
-    seed: str, optional
-        Filter by this seed.
-
-    Yields
-    ------
-    fname: str
-        The filename of the dataset (without directory).
-    parts: tuple of strings
-        The (kind, hsh, seed) of the data set.
-    """
-    direc = path.expanduser(direc or config["direc"])
-
-    fname = "{}{}_{}_r{}.h5".format(
-        kind or r"(?P<kind>[a-zA-Z]+)",
-        f"_z{redshift:.4f}" if redshift is not None else "(.*)",
-        hsh or r"(?P<hash>\w{32})",
-        seed or r"(?P<seed>\d+)",
-    )
-
-    for fl in os.listdir(direc):
-        if re.match(fname, fl):
-            yield fl
-
-
-def query_cache(
-    *, direc=None, kind=None, hsh=None, seed=None, redshift=None, show=True
-):
-    """Get or print datasets in the cache.
-
-    Walks through the cache, with given filters, and return all un-initialised dataset
-    objects, optionally printing their representation to screen.
-    Useful for querying which kinds of datasets are available within the cache, and
-    choosing one to read and use.
-
-    Parameters
-    ----------
-    direc : str, optional
-        The directory in which to search for the boxes. By default, this is the
-        centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast``.
-    kind: str, optional
-        Filter by this kind. Must be one of "InitialConditions", "PerturbedField",
-        "IonizedBox", "TsBox" or "BrightnessTemp".
-    hsh: str, optional
-        Filter by this hsh.
-    seed: str, optional
-        Filter by this seed.
-    show: bool, optional
-        Whether to print out a repr of each object that exists.
-
-    Yields
-    ------
-    obj:
-       Output objects, un-initialized.
-    """
-    for file in list_datasets(
-        direc=direc, kind=kind, hsh=hsh, seed=seed, redshift=redshift
-    ):
-        try:
-            kls = readbox(direc=direc, fname=file, load_data=False)
-        except OSError as e:
-            warnings.warn(f"Failed to read {file}: {e}")
-            continue
-
-        if show:
-            print(f"{file}: {str(kls)}")  # noqa: T201
-
-        yield file, kls
-
-
-def get_boxes_at_redshift(
-    redshift: Union[float, Tuple[float, float]], seed=None, direc=None, **params
-):
-    """Retrieve objects for each file in cache within given redshift bounds."""
-    if not hasattr(redshift, "__len__"):
-        redshift = (redshift / 1.001, redshift * 1.001)
-
-    out = defaultdict(list)
-    for file in list_datasets(direc=direc, seed=seed):
-        try:
-            obj = readbox(direc=direc, fname=file, load_data=False)
-        except OSError:
-            warnings.warn(f"Failed to read {file}")
-
-        if not hasattr(obj, "redshift"):
-            logger.debug(f"{file} has no redshift")
-            continue
-        if not (redshift[0] <= obj.redshift < redshift[1]):
-            logger.debug(f"{file} redshift out of range: {obj.redshift}, {redshift}")
-            continue
-        for paramtype, paramobj in params.items():
-            if hasattr(obj, paramtype) and paramobj != getattr(obj, paramtype):
-                logger.debug(
-                    f"{file} {paramtype} don't match: {getattr(obj, paramtype)} vs. {paramobj}"
-                )
-                break
-        else:
-            out[obj.__class__.__name__].append(obj)
-
-    return out
-
-
-def clear_cache(**kwargs):
-    """Delete datasets in the cache.
-
-    Walks through the cache, with given filters, and deletes all un-initialised dataset
-    objects, optionally printing their representation to screen.
-
-    Parameters
-    ----------
-    kwargs :
-        All options passed through to :func:`query_cache`.
-    """
-    direc = kwargs.get("direc", path.expanduser(config["direc"]))
-    number = 0
-    for fname in list_datasets(**kwargs):
-        if kwargs.get("show", True):
-            logger.info(f"Removing {fname}")
-        os.remove(path.join(direc, fname))
-        number += 1
-
-    logger.info(f"Removed {number} files from cache.")
diff --git a/src/py21cmfast/cli.py b/src/py21cmfast/cli.py
index 11fa75688..004d29860 100644
--- a/src/py21cmfast/cli.py
+++ b/src/py21cmfast/cli.py
@@ -3,6 +3,7 @@
 import attrs
 import builtins
 import click
+import contextlib
 import inspect
 import logging
 import matplotlib.pyplot as plt
@@ -13,19 +14,26 @@
 from os import path, remove
 from pathlib import Path
 
-from . import _cfg, cache_tools, global_params, plotting
+from . import _cfg, global_params, plotting
 from .drivers.coeval import run_coeval
-from .drivers.lightcone import exhaust_lightcone, run_lightcone
-from .drivers.param_config import InputParameters, get_logspaced_redshifts
+from .drivers.lightcone import run_lightcone
 from .drivers.single_field import (
     compute_initial_conditions,
     compute_ionization_field,
+    compute_spin_temperature,
     perturb_field,
-    spin_temperature,
 )
+from .io.caching import OutputCache
 from .lightcones import RectilinearLightconer
 from .wrapper._utils import camel_to_snake
-from .wrapper.inputs import AstroParams, CosmoParams, FlagOptions, UserParams
+from .wrapper.inputs import (
+    AstroParams,
+    CosmoParams,
+    FlagOptions,
+    InputParameters,
+    UserParams,
+    get_logspaced_redshifts,
+)
 from .wrapper.outputs import (
     HaloBox,
     InitialConditions,
@@ -70,16 +78,14 @@ def _update(obj, ctx):
         # noinspection PyProtectedMember
         if hasattr(obj, k):
             try:
-                val = getattr(obj, "_" + k)
-                setattr(obj, "_" + k, type(val)(ctx[k]))
+                val = getattr(obj, f"_{k}")
+                setattr(obj, f"_{k}", type(val)(ctx[k]))
                 ctx.pop(k)
             except (AttributeError, TypeError):
-                try:
+                with contextlib.suppress(AttributeError):
                     val = getattr(obj, k)
                     setattr(obj, k, type(val)(ctx[k]))
                     ctx.pop(k)
-                except AttributeError:
-                    pass
 
 
 def _get_params_from_ctx(ctx, cfg):
@@ -95,7 +101,7 @@ def _get_params_from_ctx(ctx, cfg):
         # get paramters from context
         ctx_params = {k: v for k, v in ctx.items() if k in fieldnames}
         # remove used params from context
-        [ctx.pop(k) for k in ctx_params.keys()]
+        [ctx.pop(k) for k in ctx_params]
 
         # assign to a parameter dict, prioritising arguments > config > defaults
         params[klsname] = {**cfg.get(klsname, {}), **ctx_params}
@@ -108,7 +114,7 @@ def _get_params_from_ctx(ctx, cfg):
     # Also update globals, always.
     _update(global_params, ctx)
     if ctx:
-        warnings.warn("The following arguments were not able to be set: %s" % ctx)
+        warnings.warn(f"The following arguments were not able to be set: {ctx}")
 
     return user_params, cosmo_params, astro_params, flag_options
 
@@ -182,8 +188,7 @@ def init(ctx, config, regen, direc, seed):
         inputs=inputs,
         regenerate=regen,
         write=True,
-        direc=direc,
-        random_seed=seed,
+        cache=OutputCache(direc),
     )
 
 
@@ -343,7 +348,7 @@ def spin(ctx, redshift, prev_z, config, regen, direc, seed):
     xrs = XraySourceBox(inputs=inputs)
     prev_ts = TsBox(inputs=inputs.clone(redshift=prev_z))
 
-    spin_temperature(
+    compute_spin_temperature(
         redshift=redshift,
         inputs=inputs,
         initial_conditions=ics.read(direc),
@@ -487,7 +492,7 @@ def ionize(ctx, redshift, prev_z, config, regen, direc, seed):
     help="Whether to force regeneration of init/perturb files if they already exist.",
 )
 @click.option(
-    "--direc",
+    "--cache-dir",
     type=click.Path(exists=True, dir_okay=True),
     default=None,
     help="cache directory",
@@ -499,7 +504,7 @@ def ionize(ctx, redshift, prev_z, config, regen, direc, seed):
     help="specify a random seed for the initial conditions",
 )
 @click.pass_context
-def coeval(ctx, redshift, config, out, regen, direc, seed):
+def coeval(ctx, redshift, config, out, regen, cache_dir, seed):
     """Efficiently generate coeval cubes at a given redshift.
 
     Parameters
@@ -549,8 +554,7 @@ def coeval(ctx, redshift, config, out, regen, direc, seed):
         inputs=inputs,
         regenerate=regen,
         write=True,
-        direc=direc,
-        random_seed=seed,
+        cache=OutputCache(cache_dir) if cache_dir else None,
     )
 
     if out:
@@ -673,13 +677,12 @@ def lightcone(ctx, redshift, config, out, regen, direc, max_z, seed, lq):
         quantities=lq,
     )
 
-    lc = exhaust_lightcone(
+    lc = run_lightcone(
         lightconer=lcn,
         inputs=inputs,
         regenerate=regen,
         write=True,
-        direc=direc,
-        random_seed=seed,
+        cache=OutputCache(direc),
     )
 
     if out:
@@ -689,85 +692,6 @@ def lightcone(ctx, redshift, config, out, regen, direc, max_z, seed, lq):
         print(f"Saved Lightcone to {fname}.")
 
 
-def _query(
-    direc=None,
-    kind=None,
-    md5=None,
-    seed=None,
-    clear=False,
-    sort_by=("kind", "redshift"),
-    show=None,
-):
-    objects = list(
-        cache_tools.query_cache(direc=direc, kind=kind, hsh=md5, seed=seed, show=False)
-    )
-
-    if not clear:
-        print(f"{len(objects)} Data Sets Found:")
-        print("------------------")
-    else:
-        print(f"Removing {len(objects)} data sets...")
-
-    for sorter in sort_by[::-1]:
-        if sorter == "kind":
-            objects = sorted(objects, key=lambda x: x[1].__class__.__name__)
-        elif sorter == "filename":
-            objects = sorted(objects, key=lambda x: x[0])
-        else:
-            objects = sorted(objects, key=lambda x: getattr(x[1], sorter, 0.0))
-
-    for file, c in objects:
-        if not clear:
-            print(f"  @ {file}:")
-            if not show:
-                print(f"  {c}")
-            else:
-                for s in show:
-                    print(f"  {s}: {getattr(c, s, None)}")
-            print()
-
-        else:
-            direc = direc or path.expanduser(_cfg.config["direc"])
-            remove(path.join(direc, file))
-
-
-@main.command()
-@click.option(
-    "-d",
-    "--direc",
-    type=click.Path(exists=True, dir_okay=True),
-    default=None,
-    help="directory to write data and plots to -- must exist.",
-)
-@click.option("-k", "--kind", type=str, default=None, help="filter by kind of data.")
-@click.option("-m", "--md5", type=str, default=None, help="filter by md5 hsh")
-@click.option("-s", "--seed", type=str, default=None, help="filter by random seed")
-@click.option(
-    "--clear/--no-clear",
-    default=False,
-    help="remove all data sets returned by this query.",
-)
-@click.option("--fields", type=str, multiple=True, default=None)
-@click.option("--sort", type=str, multiple=True, default=["kind", "redshift"])
-def query(direc, kind, md5, seed, clear, fields, sort):
-    """Query the cache database.
-
-    Parameters
-    ----------
-    direc : str
-        Directory in which to search for cache items
-    kind : str
-        Filter output by kind of box (eg. InitialConditions)
-    md5 : str
-        Filter output by hsh
-    seed : float
-        Filter output by random seed.
-    clear : bool
-        Remove all data sets returned by the query.
-    """
-    _query(direc, kind, md5, seed, clear, show=fields, sort_by=tuple(sort))
-
-
 @main.command()
 @click.argument("param", type=str)
 @click.argument("value", type=str)
diff --git a/src/py21cmfast/drivers/__init__.py b/src/py21cmfast/drivers/__init__.py
index 52f9d3171..5539cd0bb 100644
--- a/src/py21cmfast/drivers/__init__.py
+++ b/src/py21cmfast/drivers/__init__.py
@@ -5,3 +5,19 @@
 initial conditions, perturb fields, spin temperature, etc.) and for high-level outputs
 such as coeval boxes and lightcones.
 """
+
+from collections import deque
+from typing import Generator
+
+from .coeval import Coeval, run_coeval
+from .single_field import (
+    compute_initial_conditions,
+    compute_ionization_field,
+    compute_spin_temperature,
+    perturb_field,
+)
+
+
+def exhaust(generator: Generator):
+    """Exhaust a generator without keeping more than one return value in memory."""
+    return deque(generator, maxlen=1)[0]
diff --git a/src/py21cmfast/drivers/_param_config.py b/src/py21cmfast/drivers/_param_config.py
new file mode 100644
index 000000000..35f3e339c
--- /dev/null
+++ b/src/py21cmfast/drivers/_param_config.py
@@ -0,0 +1,463 @@
+"""Functions for setting up and configuring inputs to driver functions."""
+
+from __future__ import annotations
+
+import attrs
+import contextlib
+import inspect
+import logging
+import numpy as np
+from typing import Any, Sequence, get_args
+
+from ..io import h5
+from ..io.caching import OutputCache
+from ..wrapper.cfuncs import construct_fftw_wisdoms
+from ..wrapper.inputs import InputParameters
+from ..wrapper.outputs import OutputStruct, OutputStructZ, _HashType
+
+logger = logging.getLogger(__name__)
+
+
+def check_redshift_consistency(
+    redshift: float, output_structs: list[OutputStruct], funcname: str = "unknown"
+) -> None:
+    """
+    Check if all given :class:`OutputStruct` objects have the same redshift.
+
+    This function iterates over a list of OutputStruct objects and verifies that all
+    the redshifts are consistent with the provided redshift value. If any inconsistency
+    is found, a ValueError is raised.
+
+    Parameters
+    ----------
+    redshift
+        The reference redshift value to compare with the redshifts of the OutputStruct
+        objects.
+    output_structs
+        A list of :class:`OutputStruct` objects to check for redshift consistency.
+    funcname (str, optional)
+        The name of the function or method where this check is being performed. Used
+        in the error raised. Default is "unknown".
+
+    Raises
+    ------
+    ValueError
+        If any :class:`OutputStruct` object in the list has a redshift different from
+        the provided redshift value.
+    """
+    for struct in output_structs:
+        if struct is not None and struct.redshift != redshift:
+            raise ValueError(
+                f"Incompatible redshifts with inputs and {struct.__class__.__name__} in"
+                f" {funcname}: {redshift} != {struct.redshift}"
+            )
+
+
+def _get_incompatible_params(
+    inputs1: InputParameters, inputs2: InputParameters
+) -> dict[str, Any]:
+    """Return a dict of parameters that differ between two InputParameters objects."""
+    incompatible = {}
+    d1 = attrs.asdict(inputs1)  # recursive
+    d2 = attrs.asdict(inputs2)  # recursive
+
+    for name, struct in d1.items():
+        struct2 = d2[name]
+
+        if isinstance(struct, dict):
+            incompatible[name] = {}
+
+            for key, val in struct.items():
+                val2 = struct2[key]
+                if val2 != val:
+                    incompatible[name][key] = (val, val2)
+        elif struct != struct2:
+            incompatible[name] = (struct, struct2)
+
+    # Remove empty sub-dicts (i.e. InputStructs that totally match)
+    return {
+        k: v for k, v in incompatible.items() if not isinstance(v, dict) or len(v) > 0
+    }
+
+
+def _get_incompatible_param_diffstring(
+    inputs1: InputParameters, inputs2: InputParameters
+) -> str:
+    incompatible_params = _get_incompatible_params(inputs1, inputs2)
+
+    return "".join(
+        (
+            f"{name}:\n"
+            + "\n".join(
+                f"  {key}:\n    {v1:>12}\n    {v2:>12}" for key, (v1, v2) in val.items()
+            )
+            if isinstance(val, dict)
+            else f"{name}:\n  {val[0]}\n  {val[1]}"
+        )
+        for name, val in incompatible_params.items()
+    )
+
+
+def check_output_consistency(outputs: dict[str, OutputStruct]) -> None:
+    """Ensure all OutputStruct objects have consistent InputParameters.
+
+    This function compares each given OutputStruct with a reference element, and ensures
+    that each is compatible. Recall that two :class:`InputParameters` can be compatible
+    even if they differ, as long as they agree on the input components to which each
+    of the OutputStructs are dependent.
+
+    Raises
+    ------
+    ValueError
+        If any of the OutputStructs are not compatible.
+    """
+    outputs = {n: output for n, output in outputs.items() if output is not None}
+
+    if len(outputs) < 2:
+        return
+
+    o0 = list(outputs.values())[0]
+    n0 = list(outputs.keys())[0]
+
+    for name, output in outputs.items():
+        if not output._inputs_compatible_with(o0):
+            diff = _get_incompatible_param_diffstring(output.inputs, o0.inputs)
+            raise ValueError(
+                f"InputParameters in {name} do not match those in {n0}. Got:\n\n{diff}"
+            )
+
+
+def check_consistency_of_outputs_with_inputs(
+    inputs: InputParameters, outputs: Sequence[OutputStruct]
+):
+    """Check that all structs in `outputs` are compatible with the `inputs`.
+
+    See Also
+    --------
+    :func:`check_output_consistency`
+        Similar function that checks consistency between several outputs.
+    """
+    for output in outputs:
+        if not output._inputs_compatible_with(inputs):
+            diff = _get_incompatible_param_diffstring(output.inputs, inputs)
+            raise ValueError(
+                f"InputParameters in {output.__class__.__name__} do not match those in "
+                f"the provided InputParameters. Got:\n\n{diff}"
+            )
+
+
+class _OutputStructComputationInspect:
+    """A class that does introspection on a single-field computation function.
+
+    This class implements methods for inspecting the arguments of a single-field
+    computation function (e.g. :func:`compute_initial_conditions`) and doing validation,
+    cache-checking and other quality-of-life improvements.
+
+    It is an internal toolset, not meant to be used by users directly.
+    """
+
+    def __init__(self, _func: callable):
+        self._func = _func
+        self._signature = inspect.signature(_func)
+        self._kls = self._signature.return_annotation
+
+        if not issubclass(self._signature.return_annotation, OutputStruct):
+            raise TypeError(
+                f"{_func.__name__} must return an instance of OutputStruct (and be annotated as such)."
+            )
+
+    @staticmethod
+    def _get_all_output_struct_inputs(
+        kwargs, recurse: bool = False
+    ) -> dict[str, OutputStruct]:
+        """Return all the arguments that are OutputStructs.
+
+        If recurse is True, also add all OutputStructs that are part of iterables.
+        """
+        d = {k: v for k, v in kwargs.items() if isinstance(v, OutputStruct)}
+
+        if recurse:
+            for k, v in kwargs.items():
+                if hasattr(v, "__len__") and isinstance(v[0], OutputStruct):
+                    d |= {f"{k}_{i}": vv for i, vv in enumerate(v)}
+
+        return d
+
+    @staticmethod
+    def _get_inputs(kwargs: dict[str, Any]) -> InputParameters:
+        """Return the most detailed input parameters available.
+
+        For a given set of parameters to a single-field function, find the "inputs"
+        that should be used for instantiating the OutputStruct that the function will
+        return.
+
+        If the parameter "inputs" is given directly, just return that. If not, the
+        inputs must be determined from given OutputStruct parameters that are
+        dependencies of the desired OutputStruct. In this case, we can run into the
+        situation that different dependent OutputStruct's have different inputs. Even
+        though all must be compatible with each other, more basic OutputStructs (like
+        InitialConditions) might not have the same zgrid as the PerturbedField (for
+        example) and this is fine. So, here we return the inputs of the "most advanced"
+        OutputStruct that is given.
+        """
+        inputs = kwargs.get("inputs")
+        if inputs is not None:
+            return inputs
+
+        outputs = _OutputStructComputationInspect._get_all_output_struct_inputs(
+            kwargs, recurse=True
+        )
+
+        minreq = _HashType(0)
+        for output in outputs.values():
+            if output._compat_hash.value >= minreq.value:
+                inputs = output.inputs
+                minreq = output._compat_hash
+
+        if inputs is None:
+            raise ValueError(
+                "No parameter 'inputs' given, and no dependent OutputStruct found!"
+            )
+
+        return inputs
+
+    @staticmethod
+    def check_consistency(kwargs: dict[str, Any], outputs: dict[str, OutputStruct]):
+        """Check consistency of input parameters amongst output struct inputs."""
+        check_output_consistency(outputs)
+        given_inputs = kwargs.get("inputs")
+        if given_inputs is not None:
+            check_consistency_of_outputs_with_inputs(given_inputs, outputs.values())
+
+    def _make_wisdoms(self, inputs: InputParameters):
+        """Decorator for single-field functions that needs FFTW wisdom."""
+        construct_fftw_wisdoms(
+            user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
+        )
+
+    def check_output_struct_types(self, outputs: dict[str, OutputStruct]):
+        """Check given OutputStruct parameters.
+
+        This method checks each OutputStruct given to the compuation function to ensure
+        that it is of the correct type (i.e. `InitialConditions` when requested). The
+        types must be specified as standard Python types (so this is essentially just
+        automated run-time type-checking). It only checks OutputStructs, and allows for
+        optional values.
+        """
+        for name, param in self._signature.parameters.items():
+            val = outputs.get(name)
+            tp = param.annotation
+            if np.issubclass_(tp, OutputStruct) and not isinstance(val, tp):
+                raise TypeError(
+                    f"{name} should be of type {param.annotation.__name__}, got {type(val)}"
+                )
+            elif potential_types := get_args(tp):
+                if type(None) in potential_types and val is None:
+                    continue
+                kls = tuple(
+                    kls for kls in potential_types if np.issubclass_(kls, OutputStruct)
+                )
+                if not kls:
+                    # This is not an OutputStruct kind of parameter, ignore.
+                    continue
+                elif len(kls) > 1:
+                    raise TypeError(
+                        f"{name} parameter has a badly defined type in the signature. Please report this on Github."
+                    )
+                else:
+                    if type(None) not in potential_types:
+                        # This is supposed to be a list/sequence of OutputStruct,
+                        # but this is hard to check because these values are already
+                        # stripped out of the `outputs` variable that is passed here.
+                        # So for now, just ignore it.
+                        continue
+
+                    kls = kls[0]
+
+                    if not isinstance(val, kls):
+                        raise TypeError(
+                            f"{name} should be of type {kls.__name__}, got {type(val)}"
+                        )
+
+    def _get_current_redshift(
+        self, outputs: dict[str, OutputStructZ], kwargs: dict[str, Any]
+    ) -> float | None:
+        """Get the current redshift of evolution from the given parameters.
+
+        If redshift is given directly, return that. Otherwise, return a redshift from
+        any given OutputStruct whose name doesn't start with "previous" or "descendant".
+        We can return the first such redshift we find, because another method will check
+        that all redshifts are the same.
+        """
+        redshift = kwargs.get("redshift")
+        if redshift is None:
+            if current_outputs := [
+                v
+                for k, v in outputs.items()
+                if not k.startswith("previous_") and not k.startswith("descendant_")
+            ]:
+                redshift = current_outputs[0].redshift
+
+        return redshift
+
+    def ensure_redshift_consistency(
+        self, current_redshift: float, outputs: dict[str, OutputStructZ]
+    ):
+        """Ensure that each OutputStruct has the same redshift, if it exists.
+
+        Checks all given OutputStruct objects that have redshifts to check if their
+        redshifts are the same. It does this in three groups: previous, descendant and
+        current-redshift boxes.
+        """
+        if not outputs:
+            return
+
+        if current_outputs := [
+            v
+            for k, v in outputs.items()
+            if not k.startswith("previous_") and not k.startswith("descendant_")
+        ]:
+            check_redshift_consistency(
+                current_redshift, current_outputs, funcname=self._func.__name__
+            )
+
+        inputs = list(outputs.values())[0].inputs
+        if inputs.node_redshifts is not None:
+            previous_outputs = [
+                v for k, v in outputs.items() if k.startswith("previous_")
+            ]
+            previous_z = [z for z in inputs.node_redshifts if z > current_redshift]
+            if previous_outputs and previous_z:
+                previous_z = previous_z[-1]
+                check_redshift_consistency(
+                    previous_z,
+                    previous_outputs,
+                    funcname=f"{self._func.__name__} (previous z)",
+                )
+
+            descendant_outputs = [
+                v for k, v in outputs.items() if k.startswith("descendant_")
+            ]
+            descendant_z = [z for z in inputs.node_redshifts if z < current_redshift]
+
+            if descendant_outputs and descendant_z:
+                descendant_z = descendant_z[0]
+                check_redshift_consistency(
+                    descendant_z,
+                    descendant_outputs,
+                    funcname=f"{self._func.__name__} (descendant z)",
+                )
+
+    def _handle_read_from_cache(
+        self,
+        inputs: InputParameters,
+        current_redshfit: float | None,
+        cache: OutputCache | None,
+        kwargs: dict[str, Any],
+    ) -> OutputStruct | None:
+        """Handle potential reading from cache.
+
+        Checks the given input parameters for cache-related keywords and manages reading
+        an OutputStruct from cache if possible and desired.
+        """
+        if kwargs.pop("regenerate", True):
+            return
+
+        # First check whether the boxes already exist.
+        if issubclass(self._, OutputStructZ):
+            obj = self._kls.new(inputs=inputs, redshift=current_redshfit)
+        else:
+            obj = self._kls.new(inputs=inputs)
+
+        path = cache.find_existing(obj)
+        if path is not None:
+            with contextlib.suppress(OSError):
+                this = h5.read_output_struct(path)
+                if hasattr(this, "redshift"):
+                    logger.info(
+                        f"Existing {obj.__name__} found at z={this.redshift} and read in (seed={this.random_seed})."
+                    )
+                else:
+                    logger.info(
+                        f"Existing {obj.__name__} found and read in (seed={this.random_seed})."
+                    )
+                return this
+
+    def _handle_write_to_cache(
+        self, cache: OutputCache | None, write, obj: OutputStruct
+    ):
+        """Handle writing a box to cache."""
+        if write and not cache:
+            raise ValueError("Cannot write to cache without a cache object.")
+
+        if write:
+            cache.write(obj)
+
+
+class single_field_func(_OutputStructComputationInspect):  # noqa: N801
+    """A decorator for functions that compute single fields.
+
+    This decorator is meant for internal use only.
+    """
+
+    def __call__(self, **kwargs) -> OutputStruct:
+        """Call the single field function."""
+        inputs = self._get_inputs(kwargs)
+        outputs = self._get_all_output_struct_inputs(kwargs)
+        outputs_rec = self._get_all_output_struct_inputs(kwargs, recurse=True)
+        outputsz = {k: v for k, v in outputs.items() if isinstance(v, OutputStructZ)}
+
+        # Get current redshift (could be None)
+        current_redshift = self._get_current_redshift(outputsz, kwargs)
+
+        self.check_consistency(kwargs, outputs_rec)
+        self.check_output_struct_types(outputs)
+        # The following checks both current and previous redshifts, if applicable
+        self.ensure_redshift_consistency(current_redshift, outputsz)
+
+        cache = kwargs.pop("cache", None)
+        regen = kwargs.pop("regenerate", True)
+        write = kwargs.pop("write", False)
+        out = None
+        if cache is not None and not regen:
+            out = self._handle_read_from_cache(inputs, current_redshift, cache, kwargs)
+
+        if "inputs" in self._signature.parameters:
+            # Here we set the inputs (if accepted by the function signature)
+            # to the most advanced ones. This is the explicitly-passed inputs if
+            # they exist, but otherwise the inputs derived from the dependency
+            # that is the most advanced in the computation.
+            kwargs["inputs"] = inputs
+
+        if out is None:
+            self._make_wisdoms(inputs)
+            out = self._func(**kwargs)
+            self._handle_write_to_cache(cache, write, out)
+
+        return out
+
+
+class high_level_func(_OutputStructComputationInspect):  # noqa: N801
+    """A decorator for high-level functions like ``run_coeval``."""
+
+    # def __init__(self, _func: callable):
+    #     self._func = _func
+    def __init__(self, _func: callable):
+        self._func = _func
+        self._signature = inspect.signature(_func)
+        self._kls = self._signature.return_annotation
+
+    def __call__(self, **kwargs):
+        """Call the function."""
+        outputs = self._get_all_output_struct_inputs(kwargs, recurse=True)
+        inputs = self._get_inputs(kwargs)
+        if "inputs" in self._signature.parameters:
+            # Here we set the inputs (if accepted by the function signature)
+            # to the most advanced ones. This is the explicitly-passed inputs if
+            # they exist, but otherwise the inputs derived from the dependency
+            # that is the most advanced in the computation.
+            kwargs["inputs"] = inputs
+
+        self.check_consistency(kwargs, outputs)
+
+        yield from self._func(**kwargs)
diff --git a/src/py21cmfast/drivers/coeval.py b/src/py21cmfast/drivers/coeval.py
index 214f70e0c..a5ba7c818 100644
--- a/src/py21cmfast/drivers/coeval.py
+++ b/src/py21cmfast/drivers/coeval.py
@@ -1,5 +1,6 @@
 """Compute simulations that evolve over redshift."""
 
+import attrs
 import contextlib
 import h5py
 import logging
@@ -8,760 +9,585 @@
 import warnings
 from hashlib import md5
 from pathlib import Path
-from typing import Any, Sequence
+from typing import Any, Self, Sequence, get_args
 
 from .. import __version__
-from .._cfg import config
 from ..c_21cmfast import lib
-from ..wrapper._utils import camel_to_snake
+from ..io import h5
+from ..io.caching import CacheConfig, OutputCache, RunCache
+from ..wrapper.arrays import Array
 from ..wrapper.globals import global_params
-from ..wrapper.inputs import AstroParams, CosmoParams, FlagOptions, UserParams
+from ..wrapper.inputs import InputParameters
 from ..wrapper.outputs import (
     BrightnessTemp,
     HaloBox,
     InitialConditions,
     IonizedBox,
+    OutputStruct,
     PerturbedField,
+    PerturbHaloField,
     TsBox,
-    _OutputStruct,
 )
 from ..wrapper.photoncons import _get_photon_nonconservation_data, setup_photon_cons
 from . import single_field as sf
-from .param_config import (
-    InputParameters,
-    _get_config_options,
-    check_redshift_consistency,
-    get_logspaced_redshifts,
-)
-from .single_field import set_globals
+from ._param_config import high_level_func
 
 logger = logging.getLogger(__name__)
 
 
-class _HighLevelOutput:
-    def get_cached_data(
-        self, kind: str, redshift: float, load_data: bool = False
-    ) -> _OutputStruct:
-        """
-        Return an OutputStruct object which was cached in creating this Coeval box.
-
-        Parameters
-        ----------
-        kind
-            The kind of object: "init", "perturb", "spin_temp", "ionize" or "brightness"
-        redshift
-            The (approximate) redshift of the object to return.
-        load_data
-            Whether to actually read the field data of the object in (call ``obj.read()``
-            after this function to do this manually)
-
-        Returns
-        -------
-        output
-            The output struct object.
-        """
-        if self.cache_files is None:
-            raise AttributeError(
-                "No cache files were associated with this Coeval object."
-            )
-
-        # TODO: also check this file, because it may have been "gather"d.
-
-        if kind not in self.cache_files:
-            raise ValueError(
-                f"{kind} is not a valid kind for the cache. Valid options: "
-                f"{self.cache_files.keys()}"
-            )
-
-        files = self.cache_files.get(kind, {})
-        # files is a list of tuples of (redshift, filename)
-
-        redshifts = np.array([f[0] for f in files])
-
-        indx = np.argmin(np.abs(redshifts - redshift))
-        fname = files[indx][1]
-
-        if not os.path.exists(fname):
-            raise OSError(
-                "The cached file you requested does not exist (maybe it was removed?)."
-            )
-
-        kinds = {
-            "init": InitialConditions,
-            "perturb_field": PerturbedField,
-            "halobox": HaloBox,
-            "ionized_box": IonizedBox,
-            "spin_temp": TsBox,
-            "brightness_temp": BrightnessTemp,
-        }
-        cls = kinds[kind]
-
-        return cls.from_file(fname, load_data=load_data)
-
-    def gather(
-        self,
-        fname: str | None | Path = None,
-        kinds: Sequence | None = None,
-        clean: bool | dict = False,
-        direc: str | Path | None = None,
-    ) -> Path:
-        """Gather the cached data associated with this object into its file."""
-        kinds = kinds or [
-            "init",
-            "perturb_field",
-            "halobox",
-            "ionized_box",
-            "spin_temp",
-            "brightness_temp",
-        ]
-
-        clean = kinds if clean and not hasattr(clean, "__len__") else clean or []
-        if any(c not in kinds for c in clean):
-            raise ValueError(
-                "You are trying to clean cached items that you will not be gathering."
-            )
-
-        direc = Path(direc or config["direc"]).expanduser().absolute()
-        fname = Path(fname or self.get_unique_filename()).expanduser()
-
-        if not fname.exists():
-            fname = direc / fname
-
-        for kind in kinds:
-            redshifts = (f[0] for f in self.cache_files[kind])
-            for i, z in enumerate(redshifts):
-                cache_fname = self.cache_files[kind][i][1]
-
-                obj = self.get_cached_data(kind, redshift=z, load_data=True)
-                with h5py.File(fname, "a") as fl:
-                    cache = (
-                        fl.create_group("cache") if "cache" not in fl else fl["cache"]
-                    )
-                    kind_group = (
-                        cache.create_group(kind) if kind not in cache else cache[kind]
-                    )
-
-                    zstr = f"z{z:.2f}"
-                    if zstr not in kind_group:
-                        z_group = kind_group.create_group(zstr)
-                    else:
-                        z_group = kind_group[zstr]
-
-                    obj.write_data_to_hdf5_group(z_group)
-
-                    if kind in clean:
-                        os.remove(cache_fname)
-        return fname
-
-    def _get_prefix(self):
-        pass
-
-    def _input_rep(self):
-        return "".join(
-            repr(getattr(self, inp))
-            for inp in [
-                "user_params",
-                "cosmo_params",
-                "astro_params",
-                "flag_options",
-                "global_params",
-            ]
-        )
-
-    def get_unique_filename(self):
-        """Generate a unique hash filename for this instance."""
-        return self._get_prefix().format(
-            hash=md5((self._input_rep() + self._particular_rep()).encode()).hexdigest()
-        )
+@attrs.define
+class Coeval:
+    """A full coeval box with all associated data."""
 
-    def _write(self, direc=None, fname=None, clobber=False):
+    initial_conditions: InitialConditions = attrs.field(
+        validator=attrs.validators.instance_of(InitialConditions)
+    )
+    perturbed_field: PerturbedField = attrs.field(
+        validator=attrs.validators.instance_of(PerturbedField)
+    )
+    ionized_box: IonizedBox = attrs.field(
+        validator=attrs.validators.instance_of(IonizedBox)
+    )
+    brightness_temperature: BrightnessTemp = attrs.field(
+        validator=attrs.validators.instance_of(BrightnessTemp)
+    )
+    ts_box: TsBox = attrs.field(
+        default=None,
+        validator=attrs.validators.optional(attrs.validators.instance_of(TsBox)),
+    )
+    halobox: HaloBox = attrs.field(
+        default=None,
+        validator=attrs.validators.optional(attrs.validators.instance_of(HaloBox)),
+    )
+    photon_nonconservation_data: dict = attrs.field(factory=dict)
+
+    def __getattr__(self, name):
         """
-        Write the high level output to file in standard HDF5 format.
+        Custom attribute getter for the Coeval class.
 
-        This method is primarily meant for the automatic caching. Its default
-        filename is a hash generated based on the input data, and the directory is
-        the configured caching directory.
+        This method allows accessing arrays from OutputStruct objects within the Coeval instance
+        as if they were direct attributes of the Coeval object.
 
         Parameters
         ----------
-        direc : str, optional
-            The directory into which to write the file. Default is the configuration
-            directory.
-        fname : str, optional
-            The filename to write, default a unique name produced by the inputs.
-        clobber : bool, optional
-            Whether to overwrite existing file.
+        name : str
+            The name of the attribute being accessed.
 
         Returns
         -------
-        fname : str
-            The absolute path to which the file was written.
-        """
-        direc = os.path.expanduser(direc or config["direc"])
+        Any
+            The value of the requested array from the appropriate OutputStruct object.
 
-        if fname is None:
-            fname = self.get_unique_filename()
-
-        if not os.path.isabs(fname):
-            fname = os.path.abspath(os.path.join(direc, fname))
-
-        if not clobber and os.path.exists(fname):
-            raise FileExistsError(
-                f"The file {fname} already exists. If you want to overwrite, set clobber=True."
-            )
-
-        with h5py.File(fname, "w") as f:
-            # Save input parameters as attributes
-            for k in [
-                "user_params",
-                "cosmo_params",
-                "flag_options",
-                "astro_params",
-                "global_params",
-            ]:
-                q = getattr(self, k)
-                kfile = "_globals" if k == "global_params" else k
-                grp = f.create_group(kfile)
-
-                try:
-                    dct = q.asdict()
-                except AttributeError:
-                    dct = q
-
-                for kk, v in dct.items():
-                    if v is None:
-                        continue
-                    with contextlib.suppress(TypeError):
-                        grp.attrs[kk] = v
-            if self.photon_nonconservation_data is not None:
-                photon_data = f.create_group("photon_nonconservation_data")
-                for k, val in self.photon_nonconservation_data.items():
-                    photon_data[k] = val
-
-            f.attrs["random_seed"] = self.random_seed
-            f.attrs["version"] = __version__
-
-        self._write_particulars(fname)
-
-        return fname
-
-    def _write_particulars(self, fname):
-        pass
-
-    def save(self, fname=None, direc=".", clobber: bool = False):
-        """Save to disk.
-
-        This function has defaults that make it easy to save a unique box to
-        the current directory.
-
-        Parameters
-        ----------
-        fname : str, optional
-            The filename to write, default a unique name produced by the inputs.
-        direc : str, optional
-            The directory into which to write the file. Default is the current directory.
-
-        Returns
-        -------
-        str :
-            The filename to which the box was written.
+        Raises
+        ------
+        AttributeError
+            If the requested attribute is not found in any of the OutputStruct objects.
         """
-        return self._write(direc=direc, fname=fname, clobber=clobber)
+        # We only want to expose fields that are part of the Coeval object
+        for box in attrs.asdict(self, recurse=False).values():
+            if isinstance(box, OutputStruct) and name in box.arrays:
+                return box.get(name)
+        raise AttributeError(f"Coeval has no attribute '{name}'")
 
-    @classmethod
-    def _read_inputs(cls, fname, safe=True):
-        kwargs = {}
-        with h5py.File(fname, "r") as fl:
-            global_req_keys = [
-                k for k, v in global_params.items() if "path" not in k and v is not None
-            ]
-            glbls = dict(fl["_globals"].attrs)
-            if set(glbls.keys()) != set(global_req_keys):
-                missing_items = [
-                    (k, v)
-                    for k, v in global_params.items()
-                    if k not in glbls.keys() and k in global_req_keys
-                ]
-                extra_items = [
-                    (k, v) for k, v in glbls.items() if k not in global_params.keys()
-                ]
-                message = (
-                    f"There are extra or missing global params in the file to be read.\n"
-                    f"EXTRAS: {extra_items}\n"
-                    f"MISSING: {missing_items}\n"
-                )
-                # we don't save None values (we probably should) or paths so ignore these
-                # We also only print the warning for these fields if "safe" is turned off
-                if safe:
-                    raise ValueError(
-                        message
-                        + "set `safe=False` to load structures from previous versions"
-                    )
-                else:
-                    warnings.warn(
-                        message
-                        + "\nExtras are ignored and missing are set to default (shown) values"
-                    )
-
-            if "photon_nonconservation_data" in fl.keys():
-                d = fl["photon_nonconservation_data"]
-                kwargs["photon_nonconservation_data"] = {k: d[k][...] for k in d.keys()}
-
-        return kwargs, glbls
-
-    @classmethod
-    def read(cls, fname, direc=".", safe=True):
-        """Read the HighLevelOutput file from disk, creating a LightCone or Coeval object.
+    @property
+    def output_structs(self) -> dict[str, OutputStruct]:
+        """
+        Get a dictionary of OutputStruct objects contained in this Coeval instance.
 
-        Parameters
-        ----------
-        fname : str
-            The filename path. Can be absolute or relative.
-        direc : str
-            If fname, is relative, the directory in which to find the file. By default,
-            both the current directory and default cache and the  will be searched, in
-            that order.
-        safe : bool
-            If safe is true, we throw an error if the parameter structures in the file do not
-            match the structures in the `inputs.py` module. If false, we allow extra and missing
-            items, setting the missing items to the default values and ignoring extra items.
+        This property method returns a dictionary containing all the OutputStruct
+        objects that are attributes of the Coeval instance. It filters out any
+        non-OutputStruct attributes.
 
         Returns
         -------
-        LightCone :
-            A :class:`LightCone` instance created from the file's data.
+        dict[str, OutputStruct]
+            A dictionary where the keys are attribute names and the values are
+            the corresponding OutputStruct objects.
         """
-        if not os.path.isabs(fname):
-            fname = os.path.abspath(os.path.join(direc, fname))
-
-        if not os.path.exists(fname):
-            raise FileExistsError(f"The file {fname} does not exist!")
-
-        park, glbls = cls._read_inputs(fname, safe=safe)
-        boxk = cls._read_particular(fname, safe=safe)
-
-        with global_params.use(**glbls):
-            out = cls(**park, **boxk)
-
-        return out
-
-    def _read_particular(self, fname, safe=True):
-        pass
-
-
-class Coeval(_HighLevelOutput):
-    """A full coeval box with all associated data."""
-
-    def __init__(
-        self,
-        redshift: float,
-        initial_conditions: InitialConditions,
-        perturbed_field: PerturbedField,
-        ionized_box: IonizedBox,
-        brightness_temp: BrightnessTemp,
-        ts_box: TsBox | None = None,
-        halobox: HaloBox | None = None,
-        cache_files: dict | None = None,
-        photon_nonconservation_data=None,
-        _globals=None,
-    ):
-
-        # Check that all the fields have the same redshift.
-        check_redshift_consistency(
-            redshift,
-            (
-                perturbed_field,
-                halobox,
-                ionized_box,
-                brightness_temp,
-                ts_box,
-            ),
-        )
+        return {
+            k: v
+            for k, v in attrs.asdict(self, recurse=False).items()
+            if isinstance(v, OutputStruct)
+        }
 
-        self.redshift = redshift
-        self.init_struct = initial_conditions
-        self.perturb_struct = perturbed_field
-        self.ionization_struct = ionized_box
-        self.brightness_temp_struct = brightness_temp
-        self.halobox_struct = halobox
-        self.spin_temp_struct = ts_box
-
-        self.cache_files = cache_files
-
-        self.photon_nonconservation_data = photon_nonconservation_data
-        # A *copy* of the current global parameters.
-        self.global_params = _globals or dict(global_params.items())
-
-        # Expose all the fields of the structs to the surface of the Coeval object
-        for box in [
-            initial_conditions,
-            perturbed_field,
-            halobox,
-            ionized_box,
-            brightness_temp,
-            ts_box,
-        ]:
-            if box is None:
+    @classmethod
+    def get_fields(cls, ignore_structs: tuple[str] = ()) -> list[str]:
+        """Obtain a list of name of simulation boxes saved in the Coeval object."""
+        output_structs = []
+        for fld in attrs.fields(cls):
+            if fld.name in ignore_structs:
                 continue
-            for field in box._get_box_structures():
-                setattr(self, field, getattr(box, field))
 
-        # For backwards compatibility
-        if hasattr(self, "velocity_z"):
-            self.velocity = self.velocity_z
+            if np.issubclass_(fld.type, OutputStruct):
+                output_structs.append(fld.type)
+            else:
+                args = get_args(fld.type)
+                for k in args:
+
+                    if np.issubclass_(k, OutputStruct):
+                        output_structs.append(k)
+                        break
 
-    @classmethod
-    def get_fields(cls, spin_temp: bool = True, hbox: bool = True) -> list[str]:
-        """Obtain a list of name of simulation boxes saved in the Coeval object."""
         pointer_fields = []
-        for cls in [InitialConditions, PerturbedField, IonizedBox, BrightnessTemp]:
-            pointer_fields += cls.get_pointer_fields()
+        for struct in output_structs:
+            pointer_fields += [
+                k for k, v in attrs.fields_dict(struct).items() if v.type == Array
+            ]
 
-        if spin_temp:
-            pointer_fields += TsBox.get_pointer_fields()
+        return pointer_fields
 
-        if hbox:
-            pointer_fields += HaloBox.get_pointer_fields()
+    @property
+    def redshift(self) -> float:
+        """The redshift of the coeval box."""
+        return self.perturbed_field.redshift
 
-        return pointer_fields
+    @property
+    def inputs(self) -> InputParameters:
+        """An InputParameters object associated with the coeval box."""
+        return self.brightness_temperature.inputs
 
     @property
     def user_params(self):
         """User params shared by all datasets."""
-        return self.brightness_temp_struct.user_params
+        return self.inputs.user_params
 
     @property
     def cosmo_params(self):
         """Cosmo params shared by all datasets."""
-        return self.brightness_temp_struct.cosmo_params
+        return self.inputs.cosmo_params
 
     @property
     def flag_options(self):
         """Flag Options shared by all datasets."""
-        return self.brightness_temp_struct.flag_options
+        return self.inputs.flag_options
 
     @property
     def astro_params(self):
         """Astro params shared by all datasets."""
-        return self.brightness_temp_struct.astro_params
+        return self.inputs.astro_params
 
     @property
     def random_seed(self):
         """Random seed shared by all datasets."""
-        return self.brightness_temp_struct.random_seed
+        return self.inputs.random_seed
 
-    def _get_prefix(self):
-        return "{name}_z{redshift:.4}_{{hash}}_r{seed}.h5".format(
-            name=self.__class__.__name__,
-            redshift=float(self.redshift),
-            seed=self.random_seed,
-        )
+    def save(self, path: str | Path):
+        """Save the Coeval object to disk."""
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+
+        output_structs = self.output_structs
+        for struct in output_structs.values():
+            h5.write_output_to_hdf5(struct, path, mode="a")
+
+        with h5py.File(path, "a") as fl:
+            fl.attrs["coeval"] = True  # marker identifying this as a coeval box
+            fl.attrs["__version__"] = __version__
 
-    def _particular_rep(self):
-        return ""
-
-    def _write_particulars(self, fname):
-        for name in ["init", "perturb", "ionization", "brightness_temp", "spin_temp"]:
-            struct = getattr(self, f"{name}_struct")
-            if struct is not None:
-                struct.write(fname=fname, write_inputs=False)
-
-                # Also write any other inputs to any of the constituent boxes
-                # to the overarching attrs.
-                with h5py.File(fname, "a") as fl:
-                    for inp in struct._inputs:
-                        if inp not in fl.attrs and inp not in [
-                            "user_params",
-                            "cosmo_params",
-                            "flag_options",
-                            "astro_params",
-                            "global_params",
-                        ]:
-                            fl.attrs[inp] = getattr(struct, inp)
+            grp = fl.create_group("photon_nonconservation_data")
+            for k, v in self.photon_nonconservation_data.items():
+                grp[k] = v
 
     @classmethod
-    def _read_particular(cls, fname, safe=True):
-        kwargs = {}
+    def from_file(cls, path: str | Path, safe: bool = True) -> Self:
+        """Read the Coeval object from disk and return it."""
+        path = Path(path)
+        if not path.exists():
+            raise FileExistsError(f"The file {path} does not exist!")
 
-        with h5py.File(fname, "r") as fl:
-            kwargs["redshift"] = float(fl.attrs["redshift"])
-            for output_class in _OutputStruct._implementations():
-                if output_class.__name__ in fl:
-                    kwargs[camel_to_snake(output_class.__name__)] = (
-                        output_class.from_file(fname, safe=safe)
-                    )
+        selfdict = attrs.fields_dict(cls)
+        type_to_name = {v.type.__name__: k for k, v in selfdict.items()}
 
-        return kwargs
+        with h5py.File(path, "r") as fl:
+            if not fl.attrs.get("coeval", False):
+                raise ValueError(f"The file {path} is not a Coeval file!")
+
+            keys = set(fl.keys())
+
+            grp = fl["photon_nonconservation_data"]
+            photoncons = {k: v[...] for k, v in grp.items()}
+            keys.remove("photon_nonconservation_data")
+
+            kwargs = {
+                type_to_name[k]: h5.read_output_struct(path, struct=k, safe=safe)
+                for k in keys
+            }
+            return cls(photon_nonconservation_data=photoncons, **kwargs)
 
     def __eq__(self, other):
         """Determine if this is equal to another object."""
         return (
             isinstance(other, self.__class__)
-            and other.random_seed == self.random_seed
-            and other.redshift == self.redshift
-            and self.user_params == other.user_params
-            and self.cosmo_params == other.cosmo_params
-            and self.flag_options == other.flag_options
-            and self.astro_params == other.astro_params
+            and other.inputs == self.inputs
+            and self.redshift == other.redshift
         )
 
 
-@set_globals
-def run_coeval(
-    *,
+def evolve_perturb_halos(
     inputs: InputParameters,
-    out_redshifts: float | np.ndarray | None = None,
+    all_redshifts: list[float],
+    write: CacheConfig,
+    initial_conditions: InitialConditions,
+    cache: OutputCache,
+    regenerate: bool,
+    always_purge: bool = False,
+):
+    """
+    Evolve and perturb halo fields across multiple redshifts.
+
+    This function computes and evolves halo fields for a given set of redshifts,
+    applying perturbations to each halo list. It processes redshifts in reverse order
+    to account for descendant halos.
+
+    Parameters
+    ----------
+    inputs : InputParameters
+        Input parameters for the simulation.
+    all_redshifts : list[float]
+        List of redshifts to process, in descending order.
+    write : CacheConfig
+        Configuration for writing output to cache.
+    initial_conditions : InitialConditions
+        Initial conditions for the simulation.
+    cache : OutputCache
+        Cache object for storing and retrieving computed results.
+    regenerate : bool
+        Flag to indicate whether to regenerate results or use cached values.
+    always_purge : bool, optional
+        If True, always purge temporary data. Defaults to False.
+
+    Returns
+    -------
+    list
+        A list of perturbed halo fields for each redshift, in ascending redshift order.
+        Returns an empty list if halo fields are not used or fixed grids are enabled.
+    """
+    # get the halos (reverse redshift order)
+    if not inputs.flag_options.USE_HALO_FIELD or inputs.flag_options.FIXED_HALO_GRIDS:
+        return []
+
+    pt_halos = []
+    kw = {
+        "initial_conditions": initial_conditions,
+        "cache": cache,
+        "regenerate": regenerate,
+    }
+    halos_desc = None
+    for i, z in enumerate(all_redshifts[::-1]):
+        halos = sf.determine_halo_list(
+            redshift=z,
+            inputs=inputs,
+            descendant_halos=halos_desc,
+            write=write.halo_field,
+            **kw,
+        )
+
+        pt_halos.append(
+            sf.perturb_halo_list(
+                halo_field=halos, write=write.perturbed_halo_field, **kw
+            )
+        )
+
+        # we never want to store every halofield
+        with contextlib.suppress(OSError):
+            pt_halos[i].purge(force=always_purge)
+
+        if z in inputs.node_redshifts:
+            # Only evolve on the node_redshifts, not any redshifts in-between
+            # that the user might care about.
+            halos_desc = halos
+
+    # reverse to get the right redshift order
+    return pt_halos[::-1]
+
+
+@high_level_func
+def generate_coeval(
+    *,
+    inputs: InputParameters | None = None,
+    out_redshifts: float | tuple[float] = (),
     regenerate: bool | None = None,
-    write: bool | None = None,
-    direc: str | Path | None = None,
+    write: CacheConfig = CacheConfig(),
+    cache: OutputCache = OutputCache("."),
     initial_conditions: InitialConditions | None = None,
-    perturbed_field: PerturbedField | None = None,
     cleanup: bool = True,
-    hooks: dict[callable, dict[str, Any]] | None = None,
     always_purge: bool = False,
-    **global_kwargs,
 ):
     r"""
-    Evaluate a coeval ionized box at a given redshift, or multiple redshifts.
+    Perform a full coeval simulation of all fields at given redshifts.
 
     This is generally the easiest and most efficient way to generate a set of coeval cubes at a
-    given set of redshift. It self-consistently deals with situations in which the field needs to be
+    given set of redshifts. It self-consistently deals with situations in which the field needs to be
     evolved, and does this with the highest memory-efficiency, only returning the desired redshift.
     All other calculations are by default stored in the on-disk cache so they can be re-used at a
     later time.
 
-    .. note:: User-supplied redshift are *not* used as previous redshift in any scrolling,
-              so that pristine log-sampling can be maintained.
+    Some calculations of the coeval quantities require redshift evolution, i.e. the
+    calculation of higher-redshift coeval boxes up to some maximum redshift in order
+    to integrate the quantities over cosmic time. The redshifts that define this
+    evolution are set by the ``inputs.node_redshifts`` parameter. However, in some
+    simple cases, this evolution is not required, and this parameter can be empty.
+    Thus there is a distinction between the redshifts required for computing the physics
+    (i.e. ``inputs.node_redshifts``) and the redshifts at which the user wants to
+    obtain the resulting coeval cubes. The latter is controlled by ``out_redshifts``.
+    If not set, ``out_redshifts`` will be set to ``inputs.node_redshifts``, so that
+    all computed redshifts are returned as coeval boxes.
+
+    .. note:: User-supplied ``out_redshifts`` are *not* used in the redshift evolution,
+              so that the results depend precisely on the ``node_redshifts`` defined
+              in the input parameters.
 
     Parameters
     ----------
     inputs: :class:`~InputParameters`
         This object specifies the input parameters for the run, including the random seed
     out_redshifts: array_like, optional
-        A single redshift, or multiple redshift, at which to return results. The minimum of these
-        will define the log-scrolling behaviour (if necessary).
+        A single redshift, or multiple redshifts, at which to return results. By default,
+        use all the ``inputs.node_redshifts``. If neither is specified, an error will be
+        raised.
+    regenerate : bool
+        If True, regenerate all fields, even if they are in the cache.
+    write : :class:`~py21cmfast.cache.CacheConfig`, optional
+        Either a bool specifying whether to write _all_ the boxes to cache (or none of
+        them), or a :class:`~py21cmfast.cache.CacheConfig` object specifying which boxes
+        to write.
+    cache : :class:`~py21cmfast.cache.OutputCache`, optional
+        The cache object to use for reading and writing data from the cache. This should
+        be an instance of :class:`~py21cmfast.cache.OutputCache`, which depends solely
+        on specifying a directory to host the cache.
     initial_conditions : :class:`~InitialConditions`, optional
-        If given, the user and cosmo params will be set from this object, and it will not
-        be re-calculated.
-    perturbed_field : list of :class:`~PerturbedField`, optional
-        If given, must be compatible with initial_conditions. It will merely negate the necessity
-        of re-calculating the perturb fields.
+        If given, use these intial conditions as a basis for computing the other
+        fields, instead of re-computing the ICs. If this is defined, the ``inputs`` do
+        not need to be defined (but can be, in order to overwrite the ``node_redshifts``).
     cleanup : bool, optional
         A flag to specify whether the C routine cleans up its memory before returning.
         Typically, if `spin_temperature` is called directly, you will want this to be
-        true, as if the next box to be calculate has different shape, errors will occur
+        true, as if the next box to be calculated has different shape, errors will occur
         if memory is not cleaned. Note that internally, this is set to False until the
         last iteration.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
+    always_purge : bool, optional
+        If True, always purge temporary data from memory, even if the boxes are not
+        being cached. Defaults to False.
 
     Returns
     -------
-    coevals : :class:`~py21cmfast.outputs.Coeval`
+    coevals : list of :class:`~py21cmfast.drivers.coeval.Coeval`
         The full data for the Coeval class, with init boxes, perturbed fields, ionized boxes,
-        brightness temperature, and potential data from the conservation of photons. If a
-        single redshift was specified, it will return such a class. If multiple redshifts
-        were passed, it will return a list of such classes.
-
-    Other Parameters
-    ----------------
-    regenerate, write, direc, random_seed :
-        See docs of :func:`initial_conditions` for more information.
+        brightness temperature, and potential data from the conservation of photons. A
+        list of such objects, one for each redshift in ``out_redshifts``.
     """
-    if out_redshifts is None and perturbed_field is None:
-        raise ValueError("Either out_redshifts or perturb must be given")
+    if isinstance(write, bool):
+        write = CacheConfig() if write else CacheConfig.off()
 
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
+    if not out_redshifts:
+        out_redshifts = inputs.node_redshifts
 
-    singleton = False
-    # Ensure perturb is a list of boxes, not just one.
-    if perturbed_field is None:
-        perturbed_field = ()
-    elif not hasattr(perturbed_field, "__len__"):
-        perturbed_field = (perturbed_field,)
-        singleton = True
+    if not out_redshifts and not inputs.node_redshifts:
+        raise ValueError(
+            "Either out_redshifts or perturb must be given if inputs has no node redshifts"
+        )
 
-    # ensure inputs are compatible with ICs/Perturbedfields
-    inputs.check_output_compatibility((initial_conditions,) + perturbed_field)
+    iokw = {"regenerate": regenerate, "cache": cache}
 
-    iokw = {"regenerate": regenerate, "hooks": hooks, "direc": direc}
+    if not hasattr(out_redshifts, "__len__"):
+        out_redshifts = [out_redshifts]
 
-    if initial_conditions is None:
-        initial_conditions = sf.compute_initial_conditions(
+    if isinstance(out_redshifts, np.ndarray):
+        out_redshifts = out_redshifts.tolist()
+
+    # Get the list of redshifts we need to scroll through.
+    all_redshifts = _get_required_redshifts_coeval(inputs, out_redshifts)
+
+    (initial_conditions, perturbed_field, pt_halos, photon_nonconservation_data) = (
+        _setup_ics_and_pfs_for_scrolling(
+            all_redshifts=all_redshifts,
             inputs=inputs,
+            initial_conditions=initial_conditions,
+            write=write,
+            always_purge=always_purge,
             **iokw,
         )
+    )
+
+    idx, coeval = _obtain_starting_point_for_scrolling(
+        inputs=inputs,
+        initial_conditions=initial_conditions,
+        photon_nonconservation_data=photon_nonconservation_data,
+        cache=cache,
+    )
+
+    for coeval in _redshift_loop_generator(
+        inputs=inputs,
+        all_redshifts=all_redshifts,
+        initial_conditions=initial_conditions,
+        photon_nonconservation_data=photon_nonconservation_data,
+        perturbed_field=perturbed_field,
+        pt_halos=pt_halos,
+        write=write,
+        cleanup=cleanup,
+        always_purge=always_purge,
+        iokw=iokw,
+        init_coeval=coeval,
+        start_idx=idx + 1,
+    ):
+        yield coeval, coeval.redshift in out_redshifts
 
-    # We can go ahead and purge some of the stuff in the initial_conditions, but only if
-    # it is cached -- otherwise we could be losing information.
-    with contextlib.suppress(OSError):
-        initial_conditions.prepare_for_perturb(
-            flag_options=inputs.flag_options, force=always_purge
-        )
+    if lib.photon_cons_allocated:
+        lib.FreePhotonConsMemory()
 
-    if out_redshifts is not None and not hasattr(out_redshifts, "__len__"):
-        singleton = True
-        out_redshifts = [out_redshifts]
 
-    if isinstance(out_redshifts, np.ndarray):
-        out_redshifts = out_redshifts.tolist()
-    if perturbed_field:
-        if out_redshifts is not None and any(
-            p.redshift != z for p, z in zip(perturbed_field, out_redshifts)
-        ):
-            raise ValueError(
-                f"Input redshifts {out_redshifts} do not match "
-                + f"perturb field redshifts {[p.redshift for p in perturbed_field]}"
-            )
-        else:
-            out_redshifts = [p.redshift for p in perturbed_field]
+def run_coeval(**kwargs) -> list[Coeval]:  # noqa: D103
+    return [coeval for coeval, in_nodes in generate_coeval(**kwargs) if in_nodes]
 
-    kw = {
-        **{
-            "inputs": inputs,
-            "initial_conditions": initial_conditions,
-        },
-        **iokw,
-    }
-    photon_nonconservation_data = None
-    if inputs.flag_options.PHOTON_CONS_TYPE != "no-photoncons":
-        photon_nonconservation_data = setup_photon_cons(**kw)
 
-    # Get the list of redshift we need to scroll through.
-    all_redshifts = _get_required_redshifts_coeval(inputs, out_redshifts)
+run_coeval.__doc__ = generate_coeval.__doc__
 
-    # Get all the perturb boxes early. We need to get the perturb at every
-    # redshift.
-    pz = [p.redshift for p in perturbed_field]
-    perturb_ = []
-    for z in all_redshifts:
-        p = (
-            sf.perturb_field(
-                redshift=z, inputs=inputs, initial_conditions=initial_conditions, **iokw
-            )
-            if z not in pz
-            else perturbed_field[pz.index(z)]
-        )
 
-        if inputs.user_params.MINIMIZE_MEMORY:
-            with contextlib.suppress(OSError):
-                p.purge(force=always_purge)
-        perturb_.append(p)
+def _obtain_starting_point_for_scrolling(
+    inputs: InputParameters,
+    initial_conditions: InitialConditions,
+    photon_nonconservation_data: dict,
+    cache: OutputCache,
+    minimum_node: int | None = None,
+):
+    outputs = None
 
-    perturbed_field = perturb_
+    if minimum_node is None:
+        # By default, check for completeness at all nodes, starting at
+        # the last one.
+        minimum_node = len(inputs.node_redshifts) - 1
 
-    # get the halos (reverse redshift order)
-    pt_halos = []
-    if inputs.flag_options.USE_HALO_FIELD and not inputs.flag_options.FIXED_HALO_GRIDS:
-        halos_desc = None
-        for i, z in enumerate(all_redshifts[::-1]):
-            halos = sf.determine_halo_list(
-                redshift=z, descendant_halos=halos_desc, **kw
-            )
-            pt_halos += [sf.perturb_halo_list(halo_field=halos, **kw)]
+    if minimum_node < 0 or inputs.flag_options.USE_HALO_FIELD:
+        return (
+            -1,
+            None,
+        )  # something
 
-            # we never want to store every halofield
-            with contextlib.suppress(OSError):
-                pt_halos[i].purge(force=always_purge)
-            halos_desc = halos
+    logger.info(f"Determining pre-cached boxes for the run in {cache}")
+    rc = RunCache.from_inputs(inputs, cache)
 
-        # reverse to get the right redshift order
-        pt_halos = pt_halos[::-1]
+    for idx in range(minimum_node, -1, -1):
+        if not rc.is_complete_at(index=idx):
+            continue
 
-    # Now we can purge initial_conditions further.
-    with contextlib.suppress(OSError):
-        initial_conditions.prepare_for_spin_temp(
-            flag_options=inputs.flag_options, force=always_purge
-        )
-    if (
-        inputs.flag_options.PHOTON_CONS_TYPE == "z-photoncons"
-        and np.amin(all_redshifts) < global_params.PhotonConsEndCalibz
-    ):
-        raise ValueError(
-            f"You have passed a redshift (z = {np.amin(all_redshifts)}) that is lower than"
-            "the endpoint of the photon non-conservation correction"
-            f"(global_params.PhotonConsEndCalibz = {global_params.PhotonConsEndCalibz})."
-            "If this behaviour is desired then set global_params.PhotonConsEndCalibz"
-            f"to a value lower than z = {np.amin(all_redshifts)}."
-        )
+        _z = inputs.node_redshifts[idx]
+        outputs = rc.get_all_boxes_at_z(z=_z)
+        break
 
-    ib_tracker = [0] * len(out_redshifts)
-    bt = [0] * len(out_redshifts)
-    # At first we don't have any "previous" st or ib.
-    st, ib, pf, hb = None, None, None, None
-    # optional fields which remain None if their flags are off
-    hb2, ph2, st2, xrs = None, None, None, None
+    # Create a Coeval from the outputs
+    if outputs is not None:
+        return idx, Coeval(
+            initial_conditions=initial_conditions,
+            perturbed_field=outputs["PerturbField"],
+            ionized_box=outputs["IonizedBox"],
+            brightness_temperature=outputs["BrightnessTemp"],
+            ts_box=outputs.get("TsBox", None),
+            halobox=outputs.get("Halobox", None),
+            photon_nonconservation_data=photon_nonconservation_data,
+        )
+    else:
+        return -1, None
 
-    hb_tracker = [None] * len(out_redshifts)
-    st_tracker = [None] * len(out_redshifts)
 
-    spin_temp_files = []
-    hbox_files = []
-    perturb_files = []
-    ionize_files = []
-    brightness_files = []
-    phf_files = []
+def _redshift_loop_generator(
+    inputs: InputParameters,
+    initial_conditions: InitialConditions,
+    all_redshifts: Sequence[float],
+    perturbed_field: list[PerturbedField],
+    pt_halos: list[PerturbHaloField],
+    write: CacheConfig,
+    iokw: dict,
+    cleanup: bool,
+    always_purge: bool,
+    photon_nonconservation_data: dict,
+    start_idx: int = 0,
+    init_coeval: Coeval | None = None,
+):
+    if isinstance(write, bool):
+        write = CacheConfig()
 
     # Iterate through redshift from top to bottom
     hbox_arr = []
+
+    prev_coeval = init_coeval
+    this_coeval = None
+
+    this_halobox = None
+    this_spin_temp = None
+    this_pthalo = None
+
+    kw = {
+        **iokw,
+        "initial_conditions": initial_conditions,
+    }
+
     for iz, z in enumerate(all_redshifts):
+        if iz < start_idx:
+            continue
+
         logger.info(
             f"Computing Redshift {z} ({iz + 1}/{len(all_redshifts)}) iterations."
         )
-        pf2 = perturbed_field[iz]
-        pf2.load_all()
+        this_perturbed_field = perturbed_field[iz]
+        this_perturbed_field.load_all()
 
         if inputs.flag_options.USE_HALO_FIELD:
             if not inputs.flag_options.FIXED_HALO_GRIDS:
-                ph2 = pt_halos[iz]
-
-            hb2 = sf.compute_halo_grid(
-                perturbed_halo_list=ph2,
-                perturbed_field=pf2,
-                previous_ionize_box=ib,
-                previous_spin_temp=st,
+                this_pthalo = pt_halos[iz]
+
+            this_halobox = sf.compute_halo_grid(
+                perturbed_halo_list=this_pthalo,
+                perturbed_field=this_perturbed_field,
+                previous_ionize_box=getattr(prev_coeval, "ionized_box", None),
+                previous_spin_temp=getattr(prev_coeval, "ts_box", None),
+                write=write.halobox,
                 **kw,
             )
 
         if inputs.flag_options.USE_TS_FLUCT:
             # append the halo redshift array so we have all halo boxes [z,zmax]
-            hbox_arr += [hb2]
+            hbox_arr += [this_halobox]
             if inputs.flag_options.USE_HALO_FIELD:
                 xrs = sf.compute_xray_source_field(
                     hboxes=hbox_arr,
+                    write=write.xray_source_box,
                     **kw,
                 )
+            else:
+                xrs = None
 
-            st2 = sf.spin_temperature(
-                previous_spin_temp=st,
-                perturbed_field=pf2,
+            this_spin_temp = sf.compute_spin_temperature(
+                previous_spin_temp=getattr(prev_coeval, "ts_box", None),
+                perturbed_field=this_perturbed_field,
                 xray_source_box=xrs,
+                write=write.spin_temp,
                 **kw,
                 cleanup=(cleanup and z == all_redshifts[-1]),
             )
 
-        ib2 = sf.compute_ionization_field(
-            previous_ionized_box=ib,
-            perturbed_field=pf2,
+        this_ionized_box = sf.compute_ionization_field(
+            previous_ionized_box=getattr(prev_coeval, "ionized_box", None),
+            perturbed_field=this_perturbed_field,
             # perturb field *not* interpolated here.
-            previous_perturbed_field=pf,
-            halobox=hb2,
-            spin_temp=st2,
+            previous_perturbed_field=getattr(prev_coeval, "perturbed_field", None),
+            halobox=this_halobox,
+            spin_temp=this_spin_temp,
+            write=write.ionized_box,
             **kw,
         )
 
-        if pf is not None:
+        if prev_coeval is not None:
             with contextlib.suppress(OSError):
-                pf.purge(force=always_purge)
-        if ph2 is not None:
+                prev_coeval.perturbed_field.purge(force=always_purge)
+
+        if this_pthalo is not None:
             with contextlib.suppress(OSError):
-                ph2.purge(force=always_purge)
+                this_pthalo.purge(force=always_purge)
+
         # we only need the SFR fields at previous redshifts for XraySourceBox
-        if hb is not None:
+        if this_halobox is not None:
             with contextlib.suppress(OSError):
-                hb.prepare(
+                this_halobox.prepare(
                     keep=[
                         "halo_sfr",
                         "halo_sfr_mini",
@@ -770,78 +596,103 @@ def run_coeval(
                     ],
                     force=always_purge,
                 )
-        if z in out_redshifts:
-            logger.debug(f"PID={os.getpid()} doing brightness temp for z={z}")
-            ib_tracker[out_redshifts.index(z)] = ib2
-            st_tracker[out_redshifts.index(z)] = st2
-            hb_tracker[out_redshifts.index(z)] = hb2
-
-            _bt = sf.brightness_temperature(
-                inputs=inputs,
-                ionized_box=ib2,
-                perturbed_field=pf2,
-                spin_temp=st2,
-                **iokw,
-            )
-
-            bt[out_redshifts.index(z)] = _bt
-        else:
-            ib = ib2
-            pf = pf2
-            _bt = None
-            hb = hb2
-            st = st2
-
-        perturb_files.append((z, os.path.join(direc, pf2.filename)))
-        if inputs.flag_options.USE_HALO_FIELD:
-            hbox_files.append((z, os.path.join(direc, hb2.filename)))
-            if not inputs.flag_options.FIXED_HALO_GRIDS:
-                phf_files.append((z, os.path.join(direc, ph2.filename)))
-        if inputs.flag_options.USE_TS_FLUCT:
-            spin_temp_files.append((z, os.path.join(direc, st2.filename)))
-        ionize_files.append((z, os.path.join(direc, ib2.filename)))
 
-        if _bt is not None:
-            brightness_files.append((z, os.path.join(direc, _bt.filename)))
+        logger.debug(f"PID={os.getpid()} doing brightness temp for z={z}")
 
-    if inputs.flag_options.PHOTON_CONS_TYPE == "z-photoncons":
-        photon_nonconservation_data = _get_photon_nonconservation_data()
+        _bt = sf.brightness_temperature(
+            ionized_box=this_ionized_box,
+            perturbed_field=this_perturbed_field,
+            spin_temp=this_spin_temp,
+            write=write.brightness_temp,
+            **iokw,
+        )
 
-    if lib.photon_cons_allocated:
-        lib.FreePhotonConsMemory()
+        if inputs.flag_options.PHOTON_CONS_TYPE == "z-photoncons":
+            # Updated info at each z.
+            photon_nonconservation_data = _get_photon_nonconservation_data()
 
-    coevals = [
-        Coeval(
-            redshift=z,
+        this_coeval = Coeval(
             initial_conditions=initial_conditions,
-            perturbed_field=perturbed_field[all_redshifts.index(z)],
-            ionized_box=ib,
-            brightness_temp=_bt,
-            ts_box=st,
-            halobox=hb,
+            perturbed_field=this_perturbed_field,
+            ionized_box=this_ionized_box,
+            brightness_temperature=_bt,
+            ts_box=this_spin_temp,
+            halobox=this_halobox,
             photon_nonconservation_data=photon_nonconservation_data,
-            cache_files={
-                "init": [(0, os.path.join(direc, initial_conditions.filename))],
-                "perturb_field": perturb_files,
-                "halobox": hbox_files,
-                "ionized_box": ionize_files,
-                "brightness_temp": brightness_files,
-                "spin_temp": spin_temp_files,
-                "pt_halos": phf_files,
-            },
         )
-        for z, ib, _bt, st, hb in zip(
-            out_redshifts, ib_tracker, bt, st_tracker, hb_tracker
+
+        if z in inputs.node_redshifts:
+            # Only evolve on the node_redshifts, not any redshifts in-between
+            # that the user might care about.
+            prev_coeval = this_coeval
+        yield this_coeval
+
+
+def _setup_ics_and_pfs_for_scrolling(
+    all_redshifts: Sequence[float],
+    initial_conditions: InitialConditions | None,
+    inputs: InputParameters,
+    write: CacheConfig,
+    always_purge: bool,
+    **iokw,
+) -> tuple[InitialConditions, PerturbedField, PerturbHaloField, dict]:
+    if initial_conditions is None:
+        initial_conditions = sf.compute_initial_conditions(
+            inputs=inputs, write=write.initial_conditions, **iokw
         )
-    ]
 
-    # If a single redshift was passed, then pass back singletons.
-    if singleton:
-        coevals = coevals[0]
+    # We can go ahead and purge some of the stuff in the initial_conditions, but only if
+    # it is cached -- otherwise we could be losing information.
+    with contextlib.suppress(OSError):
+        initial_conditions.prepare_for_perturb(
+            flag_options=inputs.flag_options, force=always_purge
+        )
 
-    logger.debug("Returning from Coeval")
+    kw = {
+        "initial_conditions": initial_conditions,
+        **iokw,
+    }
+    photon_nonconservation_data = {}
+    if inputs.flag_options.PHOTON_CONS_TYPE != "no-photoncons":
+        photon_nonconservation_data = setup_photon_cons(**kw)
 
-    return coevals
+    if (
+        inputs.flag_options.PHOTON_CONS_TYPE == "z-photoncons"
+        and np.amin(all_redshifts) < global_params.PhotonConsEndCalibz
+    ):
+        raise ValueError(
+            f"You have passed a redshift (z = {np.amin(all_redshifts)}) that is lower than"
+            "the endpoint of the photon non-conservation correction"
+            f"(global_params.PhotonConsEndCalibz = {global_params.PhotonConsEndCalibz})."
+            "If this behaviour is desired then set global_params.PhotonConsEndCalibz"
+            f"to a value lower than z = {np.amin(all_redshifts)}."
+        )
+
+    # Get all the perturb boxes early. We need to get the perturb at every
+    # redshift.
+    perturbed_field = []
+    for z in all_redshifts:
+        p = sf.perturb_field(redshift=z, write=write.perturbed_field, **kw)
+
+        if inputs.user_params.MINIMIZE_MEMORY:
+            with contextlib.suppress(OSError):
+                p.purge(force=always_purge)
+        perturbed_field.append(p)
+
+    pt_halos = evolve_perturb_halos(
+        inputs=inputs,
+        all_redshifts=all_redshifts,
+        write=write,
+        always_purge=always_purge,
+        **kw,
+    )
+    # Now we can purge initial_conditions further.
+    with contextlib.suppress(OSError):
+        initial_conditions.prepare_for_spin_temp(
+            flag_options=inputs.flag_options, force=always_purge
+        )
+
+    return initial_conditions, perturbed_field, pt_halos, photon_nonconservation_data
 
 
 def _get_required_redshifts_coeval(
@@ -851,42 +702,17 @@ def _get_required_redshifts_coeval(
     # Turn into a set so that exact matching user-set redshift
     # don't double-up with scrolling ones.
     if (
-        inputs.flag_options.USE_TS_FLUCT or inputs.flag_options.INHOMO_RECO
-    ) and inputs.node_redshifts.min() > min(user_redshifts):
+        (inputs.flag_options.USE_TS_FLUCT or inputs.flag_options.INHOMO_RECO)
+        and user_redshifts
+        and min(inputs.node_redshifts) > min(user_redshifts)
+    ):
         warnings.warn(
             f"minimum node redshift {inputs.node_redshifts.min()} is above output redshift {min(user_redshifts)},"
             + "This may result in strange evolution"
         )
 
-    needed_nodes = [z for z in inputs.node_redshifts if z > min(user_redshifts)]
+    zmin_user = min(user_redshifts) if user_redshifts else 0
+    needed_nodes = [z for z in inputs.node_redshifts if z > zmin_user]
     redshifts = np.concatenate((needed_nodes, user_redshifts))
     redshifts = np.sort(np.unique(redshifts))[::-1]
     return redshifts.tolist()
-
-
-def _get_coeval_callbacks(
-    scrollz: list[float], coeval_callback, coeval_callback_redshifts
-) -> list[bool]:
-    compute_coeval_callback = [False] * len(scrollz)
-
-    if coeval_callback is not None:
-        if isinstance(coeval_callback_redshifts, (list, np.ndarray)):
-            for coeval_z in coeval_callback_redshifts:
-                assert isinstance(coeval_z, (int, float, np.number))
-                compute_coeval_callback[
-                    np.argmin(np.abs(np.array(scrollz) - coeval_z))
-                ] = True
-            if sum(compute_coeval_callback) != len(coeval_callback_redshifts):
-                logger.warning(
-                    "some of the coeval_callback_redshifts refer to the same node_redshift"
-                )
-        elif (
-            isinstance(coeval_callback_redshifts, int) and coeval_callback_redshifts > 0
-        ):
-            compute_coeval_callback = [
-                not i % coeval_callback_redshifts for i in range(len(scrollz))
-            ]
-        else:
-            raise ValueError("coeval_callback_redshifts has to be list or integer > 0.")
-
-    return compute_coeval_callback
diff --git a/src/py21cmfast/drivers/lightcone.py b/src/py21cmfast/drivers/lightcone.py
index 89d69d234..210d332ba 100644
--- a/src/py21cmfast/drivers/lightcone.py
+++ b/src/py21cmfast/drivers/lightcone.py
@@ -1,101 +1,86 @@
 """Module containing a driver function for creating lightcones."""
 
+import attrs
 import contextlib
 import h5py
 import logging
 import numpy as np
-import os
 import warnings
 from astropy import units
 from astropy.cosmology import z_at_value
 from collections import deque
 from cosmotile import apply_rsds
 from pathlib import Path
-from typing import Sequence
+from typing import Self, Sequence
 
+from .. import __version__
 from ..c_21cmfast import lib
-from ..cache_tools import get_boxes_at_redshift
+from ..io import h5
+from ..io.caching import CacheConfig, OutputCache, RunCache
 from ..lightcones import Lightconer, RectilinearLightconer
 from ..wrapper.globals import global_params
-from ..wrapper.inputs import AstroParams, CosmoParams, FlagOptions, UserParams
-from ..wrapper.outputs import InitialConditions, PerturbedField
+from ..wrapper.inputs import InputParameters
+from ..wrapper.outputs import InitialConditions, PerturbedField, PerturbHaloField
 from ..wrapper.photoncons import _get_photon_nonconservation_data, setup_photon_cons
+from . import exhaust
 from . import single_field as sf
-from .coeval import Coeval, _get_coeval_callbacks, _HighLevelOutput
-from .param_config import InputParameters, _get_config_options, get_logspaced_redshifts
-from .single_field import set_globals
+from ._param_config import high_level_func
+from .coeval import (
+    _obtain_starting_point_for_scrolling,
+    _redshift_loop_generator,
+    _setup_ics_and_pfs_for_scrolling,
+    evolve_perturb_halos,
+)
 
 logger = logging.getLogger(__name__)
 
 
-class LightCone(_HighLevelOutput):
-    """A full Lightcone with all associated evolved data."""
-
-    def __init__(
-        self,
-        distances,
-        inputs,
-        lightcones,
-        global_quantities=None,
-        photon_nonconservation_data=None,
-        cache_files: dict | None = None,
-        _globals=None,
-        log10_mturnovers=None,
-        log10_mturnovers_mini=None,
-        current_redshift=None,
-        current_index=None,
-    ):
-        self.random_seed = inputs.random_seed
-        self.user_params = inputs.user_params
-        self.cosmo_params = inputs.cosmo_params
-        self.astro_params = inputs.astro_params
-        self.flag_options = inputs.flag_options
-        self.node_redshifts = inputs.node_redshifts
-        self.cache_files = cache_files
-        self.log10_mturnovers = log10_mturnovers
-        self.log10_mturnovers_mini = log10_mturnovers_mini
-        self._current_redshift = current_redshift
-        self.lightcone_distances = distances
-
-        if not hasattr(self.lightcone_distances, "unit"):
-            self.lightcone_distances <<= units.Mpc
-
-        # A *copy* of the current global parameters.
-        self.global_params = _globals or dict(global_params.items())
-
-        if global_quantities:
-            for name, data in global_quantities.items():
-                if name.endswith("_box"):
-                    # Remove the _box because it looks dumb.
-                    setattr(self, f"global_{name[:-4]}", data)
-                else:
-                    setattr(self, f"global_{name}", data)
-
-        self.photon_nonconservation_data = photon_nonconservation_data
-
-        for name, data in lightcones.items():
-            setattr(self, name, data)
-
-        # Hold a reference to the global/lightcones in a dict form for easy reference.
-        self.global_quantities = global_quantities
-        self.lightcones = lightcones
-        self._current_index = current_index or self.shape[-1] - 1
+@attrs.define()
+class LightCone:
+    """A full Lightcone with all associated evolved data.
 
-    @property
-    def global_xHI(self):
-        """Global neutral fraction function."""
-        warnings.warn(
-            "global_xHI is deprecated. From now on, use global_xH. Will be removed in v3.1"
-        )
-        return self.global_xH
+    Attributes
+    ----------
+    lightcone_distances: units.Quantity
+        The comoving distance to each cell in the lightcones.
+    inputs: InputParameters
+        The input parameters corresponding to the lightcones.
+    lightcones: dict[str, np.ndarray]
+        Lightcone arrays, each of shape `(N, N, Nz)`.
+    global_quantities: dict[str, np.ndarray] | None
+        Arrays of length `node_redshifts` containing the mean field across redshift.
+    photon_nonconservation_data: dict
+        Data defining the conservation hack for photons.
+    _last_completed_node: int
+        Since the lightcone is filled up incrementally, this keeps track of the index
+        of the last completed node redshift that has been added to the lightcone.
+    _last_completed_lcidx: int
+        In conjunction with _last_completed_node, this keeps track of the index that
+        has been filled up *in the lightcone* (recalling that the lightcone has
+        multiple redshifts in between each node redshift). While in principle this
+        can be computed from _last_completed_node, it is much more efficient to keep
+        track of it manually.
+    """
+
+    lightcone_distances: units.Quantity = attrs.field()
+    inputs: InputParameters = attrs.field(
+        validator=attrs.validators.instance_of(InputParameters)
+    )
+    lightcones: dict[str, np.ndarray] = attrs.field(
+        validator=attrs.validators.instance_of(dict)
+    )
+    global_quantities: dict[str, np.ndarray] | None = attrs.field(default=None)
+    photon_nonconservation_data: dict = attrs.field(factory=dict)
+    _last_completed_node: int = attrs.field(default=-1)
+    _last_completed_lcidx: int = attrs.field(default=-1)
 
     @property
-    def cell_size(self):
+    def cell_size(self) -> float:
         """Cell size [Mpc] of the lightcone voxels."""
         return self.user_params.BOX_LEN / self.user_params.HII_DIM
 
     @property
-    def lightcone_dimensions(self):
+    def lightcone_dimensions(self) -> tuple[float, float, float]:
         """Lightcone size over each dimension -- tuple of (x,y,z) in Mpc."""
         return (
             self.user_params.BOX_LEN,
@@ -104,22 +89,47 @@ def lightcone_dimensions(self):
         )
 
     @property
-    def shape(self):
+    def shape(self) -> tuple[int, int, int]:
         """Shape of the lightcone as a 3-tuple."""
         return self.lightcones[list(self.lightcones.keys())[0]].shape
 
     @property
-    def n_slices(self):
+    def n_slices(self) -> int:
         """Number of redshift slices in the lightcone."""
         return self.shape[-1]
 
     @property
-    def lightcone_coords(self):
+    def lightcone_coords(self) -> tuple[float, float, float]:
         """Co-ordinates [Mpc] of each slice along the redshift axis."""
         return self.lightcone_distances - self.lightcone_distances[0]
 
     @property
-    def lightcone_redshifts(self):
+    def user_params(self):
+        """User params shared by all datasets."""
+        return self.inputs.user_params
+
+    @property
+    def cosmo_params(self):
+        """Cosmo params shared by all datasets."""
+        return self.inputs.cosmo_params
+
+    @property
+    def flag_options(self):
+        """Flag Options shared by all datasets."""
+        return self.inputs.flag_options
+
+    @property
+    def astro_params(self):
+        """Astro params shared by all datasets."""
+        return self.inputs.astro_params
+
+    @property
+    def random_seed(self):
+        """Random seed shared by all datasets."""
+        return self.inputs.random_seed
+
+    @property
+    def lightcone_redshifts(self) -> np.ndarray:
         """Redshift of each cell along the redshift axis."""
         return np.array(
             [
@@ -128,116 +138,91 @@ def lightcone_redshifts(self):
             ]
         )
 
-    def _get_prefix(self):
-        return "{name}_z{zmin:.4}-{zmax:.4}_{{hash}}_r{seed}.h5".format(
-            name=self.__class__.__name__,
-            zmin=float(self.lightcone_redshifts.min()),
-            zmax=float(self.lightcone_redshifts.max()),
-            seed=self.random_seed,
-        )
+    def save(self, path: str | Path):
+        """Save the lightcone object to disk."""
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        h5._write_inputs_to_group(self.inputs, path)
 
-    def _particular_rep(self):
-        return (
-            str(np.round(self.node_redshifts, 3))
-            + str(self.global_quantities.keys())
-            + str(self.lightcones.keys())
-        )
+        with h5py.File(path, "a") as fl:
+            fl.attrs["lightcone"] = True  # marker identifying this as a lightcone box
 
-    def _write_particulars(self, fname):
-        with h5py.File(fname, "a") as f:
-            # Save the boxes to the file
-            boxes = f.create_group("lightcones")
+            fl.attrs["last_completed_node"] = self._last_completed_node
+            fl.attrs["last_completed_lcidx"] = self._last_completed_lcidx
+
+            fl.attrs["__version__"] = __version__
+
+            grp = fl.create_group("photon_nonconservation_data")
+            for k, v in self.photon_nonconservation_data.items():
+                grp[k] = v
 
-            # Go through all fields in this struct, and save
+            # Save the boxes to the file
+            boxes = fl.create_group("lightcones")
             for k, val in self.lightcones.items():
                 boxes[k] = val
 
-            global_q = f.create_group("global_quantities")
+            global_q = fl.create_group("global_quantities")
             for k, v in self.global_quantities.items():
                 global_q[k] = v
 
-            f["node_redshifts"] = self.node_redshifts
-            f["distances"] = self.lightcone_distances
-            f["log10_mturnovers"] = self.log10_mturnovers
-            f["log10_mturnovers_mini"] = self.log10_mturnovers_mini
+            fl["lightcone_distances"] = self.lightcone_distances.to_value("Mpc")
 
-    def make_checkpoint(self, fname, index: int, redshift: float):
+    def make_checkpoint(self, path: str | Path, lcidx: int, node_index: int):
         """Write updated lightcone data to file."""
-        with h5py.File(fname, "a") as fl:
-            current_index = fl.attrs.get("current_index", 0)
+        with h5py.File(path, "a") as fl:
+            last_completed_lcidx = fl.attrs["last_completed_lcidx"]
 
             for k, v in self.lightcones.items():
-                fl["lightcones"][k][..., -index : v.shape[-1] - current_index] = v[
-                    ..., -index : v.shape[-1] - current_index
-                ]
+                fl["lightcones"][k][
+                    ..., -lcidx : v.shape[-1] - last_completed_lcidx
+                ] = v[..., -lcidx : v.shape[-1] - last_completed_lcidx]
 
             global_q = fl["global_quantities"]
             for k, v in self.global_quantities.items():
-                global_q[k][-index : v.shape[-1] - current_index] = v[
-                    -index : v.shape[-1] - current_index
+                global_q[k][-lcidx : v.shape[-1] - last_completed_lcidx] = v[
+                    -lcidx : v.shape[-1] - last_completed_lcidx
                 ]
 
-            fl.attrs["current_index"] = index
-            fl.attrs["current_redshift"] = redshift
-            self._current_redshift = redshift
-            self._current_index = index
+            fl.attrs["last_completed_lcidx"] = lcidx
+            fl.attrs["last_completed_node"] = node_index
 
-    @classmethod
-    def _read_inputs(cls, fname, safe=True):
-        kwargs = {}
-        parkw = {}
-        with h5py.File(fname, "r") as fl:
-            for k, kls in [
-                ("user_params", UserParams),
-                ("cosmo_params", CosmoParams),
-                ("flag_options", FlagOptions),
-                ("astro_params", AstroParams),
-            ]:
-                dct = dict(fl[k].attrs)
-                parkw[k] = kls.from_subdict(dct, safe=safe)
-
-            parkw["random_seed"] = fl.attrs["random_seed"]
-            parkw["node_redshifts"] = fl["node_redshifts"][...]
-            kwargs["inputs"] = InputParameters(**parkw)
-            kwargs["current_redshift"] = fl.attrs.get("current_redshift", None)
-            kwargs["current_index"] = fl.attrs.get("current_index", None)
-
-        # Get the standard inputs.
-        kw, glbls = _HighLevelOutput._read_inputs(fname, safe=safe)
-
-        return {**kw, **kwargs}, glbls
+            self._last_completed_lcidx = lcidx
+            self._last_completed_node = node_index
 
     @classmethod
-    def _read_particular(cls, fname, safe=True):
+    def from_file(cls, path: str | Path, safe: bool = True) -> Self:
+        """Create a new instance from a saved lightcone on disk."""
         kwargs = {}
-        with h5py.File(fname, "r") as fl:
+        with h5py.File(path, "r") as fl:
+            if not fl.attrs.get("lightcone", False):
+                raise ValueError(f"The file {path} is not a lightcone file!")
+
+            kwargs["inputs"] = h5.read_inputs(fl, safe=safe)
+            kwargs["last_completed_node"] = fl.attrs["last_completed_node"]
+            kwargs["last_completed_lcidx"] = fl.attrs["last_completed_lcidx"]
+
+            grp = fl["photon_nonconservation_data"]
+            kwargs["photon_nonconservation_data"] = {k: v[...] for k, v in grp.items()}
+
             boxes = fl["lightcones"]
             kwargs["lightcones"] = {k: boxes[k][...] for k in boxes.keys()}
 
             glb = fl["global_quantities"]
             kwargs["global_quantities"] = {k: glb[k][...] for k in glb.keys()}
-            kwargs["distances"] = fl["distances"][...]
+            kwargs["lightcone_distances"] = fl["lightcone_distances"][...] * units.Mpc
 
-            kwargs["log10_mturnovers"] = fl["log10_mturnovers"][...]
-            kwargs["log10_mturnovers_mini"] = fl["log10_mturnovers_mini"][...]
-
-        return kwargs
+        return cls(**kwargs)
 
     def __eq__(self, other):
         """Determine if this is equal to another object."""
         return (
             isinstance(other, self.__class__)
-            and other.random_seed == self.random_seed
             and np.all(
                 np.isclose(
                     other.lightcone_redshifts, self.lightcone_redshifts, atol=1e-3
                 )
             )
-            and np.all(np.isclose(other.node_redshifts, self.node_redshifts, atol=1e-3))
-            and self.user_params == other.user_params
-            and self.cosmo_params == other.cosmo_params
-            and self.flag_options == other.flag_options
-            and self.astro_params == other.astro_params
+            and self.inputs == other.inputs
             and self.global_quantities.keys() == other.global_quantities.keys()
             and self.lightcones.keys() == other.lightcones.keys()
         )
@@ -293,7 +278,7 @@ def compute_rsds(self, n_subcells: int = 4, fname: str | Path | None = None):
                 out=dvdx_on_h,
             )
 
-            tb_with_rsds = self.brightness_temp / (1 + dvdx_on_h)
+            tb_with_rsds = self.lightcones["brightness_temp"] / (1 + dvdx_on_h)
         else:
             gradient_component = 1 + dvdx_on_h  # not clipped!
             Tcmb = 2.728
@@ -301,7 +286,7 @@ def compute_rsds(self, n_subcells: int = 4, fname: str | Path | None = None):
             tb_with_rsds = np.where(
                 gradient_component < 1e-7,
                 1000.0 * (self.Ts_box - Trad) / (1.0 + self.lightcone_redshifts),
-                (1.0 - np.exp(self.brightness_temp / gradient_component))
+                (1.0 - np.exp(self.lightcones["brightness_temp"] / gradient_component))
                 * 1000.0
                 * (self.Ts_box - Trad)
                 / (1.0 + self.lightcone_redshifts),
@@ -333,18 +318,17 @@ def setup_lightcone_instance(
     scrollz: Sequence[float],
     inputs: InputParameters,
     global_quantities: Sequence[str],
+    photon_nonconservation_data: dict,
     lightcone_filename: Path | None = None,
-):
+) -> LightCone:
     """Returns a LightCone instance given a lightconer as input."""
     if lightcone_filename and Path(lightcone_filename).exists():
-        lightcone = LightCone.read(lightcone_filename)
-        start_idx = np.sum(
-            scrollz >= lightcone._current_redshift
-        )  # NOTE: assuming descending
-        logger.info(f"Read in LC file at z={lightcone._current_redshift}")
-        if start_idx < len(scrollz):
+        lightcone = LightCone.from_file(lightcone_filename)
+        idx = lightcone._last_completed_node
+        logger.info("Read in LC file")
+        if idx < len(scrollz) - 1:
             logger.info(
-                f"starting at z={scrollz[start_idx]}, step ({start_idx + 1}/{len(scrollz) + 1}"
+                f"starting at z={scrollz[idx + 1]}, step ({idx + 2}/{len(scrollz)}"
             )
     else:
         lcn_cls = (
@@ -367,104 +351,37 @@ def setup_lightcone_instance(
             )
 
         lightcone = lcn_cls(
-            lightconer.lc_distances,
-            inputs,
-            lc,
-            log10_mturnovers=np.zeros_like(scrollz),
-            log10_mturnovers_mini=np.zeros_like(scrollz),
+            lightcone_distances=lightconer.lc_distances,
+            inputs=inputs,
+            lightcones=lc,
             global_quantities={
                 quantity: np.zeros(len(scrollz)) for quantity in global_quantities
             },
-            _globals=dict(global_params.items()),
+            photon_nonconservation_data=photon_nonconservation_data,
         )
-        start_idx = 0
-    return lightcone, start_idx
+    return lightcone
 
 
-@set_globals
 def _run_lightcone_from_perturbed_fields(
     *,
     initial_conditions: InitialConditions,
     perturbed_fields: Sequence[PerturbedField],
     lightconer: Lightconer,
     inputs: InputParameters,
+    photon_nonconservation_data: dict,
+    pt_halos: list[PerturbHaloField],
     regenerate: bool | None = None,
     global_quantities: tuple[str] = ("brightness_temp", "xH_box"),
-    direc: Path | str | None = None,
+    cache: OutputCache = OutputCache("."),
     cleanup: bool = True,
-    hooks: dict | None = None,
+    write: CacheConfig = CacheConfig(),
     always_purge: bool = False,
     lightcone_filename: str | Path = None,
-    **global_kwargs,
 ):
-    r"""
-    Evaluate a full lightcone ending at a given redshift.
-
-    This is generally the easiest and most efficient way to generate a lightcone, though it can
-    be done manually by using the lower-level functions which are called by this function.
-
-    Parameters
-    ----------
-    lightconer : :class:`~Lightconer`
-        This object specifies the dimensions, redshifts, and quantities required by the lightcone run
-    inputs: :class:`~InputParameters`
-        This object specifies the input parameters for the run, including the random seed
-    global_quantities : tuple of str, optional
-        The quantities to save as globally-averaged redshift-dependent functions.
-        These may be any of the quantities that can be used in ``lightcone_quantities``.
-        The mean is taken over the full 3D cube at each redshift, rather than a 2D
-        slice.
-    initial_conditions : :class:`~InitialConditions`, optional
-        If given, the user and cosmo params will be set from this object, and it will not be
-        re-calculated.
-    perturbed_fields : list of :class:`~PerturbedField`, optional
-        If given, must be compatible with initial_conditions. It will merely negate the necessity of
-        re-calculating the
-        perturb fields. It will also be used to set the redshift if given.
-    cleanup : bool, optional
-        A flag to specify whether the C routine cleans up its memory before returning.
-        Typically, if `spin_temperature` is called directly, you will want this to be
-        true, as if the next box to be calculate has different shape, errors will occur
-        if memory is not cleaned. Note that internally, this is set to False until the
-        last iteration.
-    lightcone_filename
-        The filename to which to save the lightcone. The lightcone is returned in
-        memory, and can be saved manually later, but including this filename will
-        save the lightcone on each iteration, which can be helpful for checkpointing.
-    return_at_z
-        If given, evaluation of the lightcone will be stopped at the given redshift,
-        and the partial lightcone object will be returned. Lightcone evaluation can
-        continue if the returned lightcone is saved to file, and this file is passed
-        as `lightcone_filename`.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
-
-    Returns
-    -------
-    lightcone : :class:`~py21cmfast.LightCone`
-        The lightcone object.
-    coeval_callback_output : list
-        Only if coeval_callback in not None.
-
-    Other Parameters
-    ----------------
-    regenerate, write, direc, random_seed
-        See docs of :func:`initial_conditions` for more information.
-    """
-    direc = Path(direc)
-
     lightconer.validate_options(inputs.user_params, inputs.flag_options)
 
     # Get the redshift through which we scroll and evaluate the ionization field.
     scrollz = np.array([pf.redshift for pf in perturbed_fields])
-    if np.any(np.diff(scrollz) >= 0):
-        raise ValueError(
-            "The perturb fields must be ordered by redshift in descending order.\n"
-            + f"redshifts: {scrollz}\n"
-            + f"diffs: {np.diff(scrollz)}"
-        )
 
     lcz = lightconer.lc_redshifts
     if not np.all(scrollz.min() * 0.99 < lcz) and np.all(lcz < scrollz.max() * 1.01):
@@ -476,223 +393,70 @@ def _run_lightcone_from_perturbed_fields(
             f"while the lightcone redshift range is {lcz.min()} to {lcz.max()}."
         )
 
-    if (
-        inputs.flag_options.PHOTON_CONS_TYPE == "z-photoncons"
-        and np.amin(scrollz) < global_params.PhotonConsEndCalibz
-    ):
-        raise ValueError(
-            f"""
-            You have passed a redshift (z = {np.amin(scrollz)}) that is lower than the
-            endpoint of the photon non-conservation correction
-            (global_params.PhotonConsEndCalibz = {global_params.PhotonConsEndCalibz}).
-            If this behaviour is desired then set global_params.PhotonConsEndCalibz to a
-            value lower than z = {np.amin(scrollz)}.
-            """
-        )
-
-    iokw = {"hooks": hooks, "regenerate": regenerate, "direc": direc}
+    iokw = {"regenerate": regenerate, "cache": cache}
 
     # Create the LightCone instance, loading from file if needed
-    lightcone, start_idx = setup_lightcone_instance(
+    lightcone = setup_lightcone_instance(
         lightconer=lightconer,
         inputs=inputs,
         scrollz=scrollz,
         global_quantities=global_quantities,
         lightcone_filename=lightcone_filename,
+        photon_nonconservation_data=photon_nonconservation_data,
     )
-    if start_idx >= len(scrollz):
-        logger.info(
-            f"Lightcone already full at z={lightcone._current_redshift}. Returning."
-        )
-        # effectively adds one more iteration, but since start_idx > len(scrollz) it will be the only one
+    if lightcone._last_completed_node == len(scrollz) - 1:
+        logger.info("Lightcone already full. Returning.")
         yield None, None, None, lightcone
 
-    # Remove anything in initial_conditions not required for spin_temp
-    with contextlib.suppress(OSError):
-        initial_conditions.prepare_for_spin_temp(
-            flag_options=inputs.flag_options, force=always_purge
-        )
-    kw = {
-        **{
-            "initial_conditions": initial_conditions,
-            "inputs": inputs,
-        },
-        **iokw,
-    }
-
-    photon_nonconservation_data = None
-    if inputs.flag_options.PHOTON_CONS_TYPE != "no-photoncons":
-        setup_photon_cons(**kw)
-
-    # At first we don't have any "previous" fields.
-    st, ib, pf, hbox = None, None, None, None
-    # optional fields which remain None if their flags are off
-    hbox2, ph2, st2, xrs = None, None, None, None
-
-    if (
-        lightcone._current_redshift
-        and not np.isclose(scrollz.min(), lightcone._current_redshift)
-        and not inputs.flag_options.USE_HALO_FIELD
-    ):
-        logger.info(
-            f"Finding boxes at z={lightcone._current_redshift} with seed {lightcone.random_seed} and direc={direc}"
-        )
-        cached_boxes = get_boxes_at_redshift(
-            redshift=lightcone._current_redshift,
-            seed=lightcone.random_seed,
-            direc=direc,
-            user_params=inputs.user_params,
-            cosmo_params=inputs.cosmo_params,
-            flag_options=inputs.flag_options,
-            astro_params=inputs.astro_params,
-        )
-        try:
-            st = cached_boxes["TsBox"][0] if inputs.flag_options.USE_TS_FLUCT else None
-            pf = cached_boxes["PerturbedField"][0]
-            ib = cached_boxes["IonizedBox"][0]
-        except (KeyError, IndexError):
-            raise OSError(
-                f"No component boxes found at z={lightcone._current_redshift} with "
-                f"seed {lightcone.random_seed} and direc={direc}. You need to have "
-                "run with write=True to continue from a checkpoint."
-            )
+    idx, prev_coeval = _obtain_starting_point_for_scrolling(
+        inputs=inputs,
+        cache=cache,
+        initial_conditions=initial_conditions,
+        photon_nonconservation_data=photon_nonconservation_data,
+        minimum_node=lightcone._last_completed_node,
+    )
 
-    # we explicitly pass the descendant halos here since we have a redshift list prior
-    #   this will generate the extra fields if STOC_MINIMUM_Z is given
-    pt_halos = []
-    if inputs.flag_options.USE_HALO_FIELD and not inputs.flag_options.FIXED_HALO_GRIDS:
-        halos_desc = None
-        for iz, z in enumerate(scrollz[::-1]):
-            halo_field = sf.determine_halo_list(
-                redshift=z,
-                descendant_halos=halos_desc,
-                **kw,
-            )
-            halos_desc = halo_field
-            pt_halos += [sf.perturb_halo_list(halo_field=halo_field, **kw)]
-
-            # we never want to store every halofield
-            with contextlib.suppress(OSError):
-                pt_halos[iz].purge(force=always_purge)
-        # reverse the halo lists to be in line with the redshift lists
-        pt_halos = pt_halos[::-1]
-
-    # Now that we've got all the perturb fields, we can purge init more.
-    with contextlib.suppress(OSError):
-        initial_conditions.prepare_for_spin_temp(
-            flag_options=inputs.flag_options, force=always_purge
+    if idx < lightcone._last_completed_node:
+        warnings.warn(
+            f"The cache at {cache} only contains complete coeval boxes for {idx + 1} redshift nodes, "
+            f"instead of {lightcone._last_completed_node + 1}, which is the current checkpointing "
+            f"redshift of the lightcone. Repeating the higher-z calculations..."
         )
 
-    # arrays to hold cache filenames
-    perturb_files = []
-    spin_temp_files = []
-    ionize_files = []
-    brightness_files = []
-    hbox_files = []
-    phf_files = []
-
-    # saved global quantities which aren't lightcones
-    log10_mturnovers = np.zeros(len(scrollz))
-    log10_mturnovers_mini = np.zeros(len(scrollz))
-
-    # structs which need to be kept beyond one snapshot
-    hboxes = []
-
-    # coeval objects to interpolate onto the lightcone
-    coeval = None
-    prev_coeval = None
+    lightcone._last_completed_node = idx
+    lightcone._last_completed_lcidx = (
+        np.sum(lightcone.lightcone_redshifts >= scrollz[lightcone._last_completed_node])
+        - 1
+    )
 
     if lightcone_filename and not Path(lightcone_filename).exists():
         lightcone.save(lightcone_filename)
 
-    # Iterate through redshift from top to bottom
-    for iz, z in enumerate(scrollz):
-        if iz < start_idx:
-            continue
-        logger.info(f"Computing Redshift {z} ({iz + 1}/{len(scrollz)}) iterations.")
-
-        # Best to get a perturb for this redshift, to pass to brightness_temperature
-        pf2 = perturbed_fields[iz]
-        # This ensures that all the arrays that are required for spin_temp are there,
-        # in case we dumped them from memory into file.
-        pf2.load_all()
-        if inputs.flag_options.USE_HALO_FIELD:
-            if not inputs.flag_options.FIXED_HALO_GRIDS:
-                ph2 = pt_halos[iz]
-                ph2.load_all()
-
-            hbox2 = sf.compute_halo_grid(
-                perturbed_halo_list=ph2,
-                previous_ionize_box=ib,
-                previous_spin_temp=st,
-                perturbed_field=pf2,
-                **kw,
-            )
-
-            if inputs.flag_options.USE_TS_FLUCT:
-                hboxes.append(hbox2)
-                xrs = sf.compute_xray_source_field(
-                    hboxes=hboxes,
-                    **kw,
-                )
-
-        if inputs.flag_options.USE_TS_FLUCT:
-            st2 = sf.spin_temperature(
-                previous_spin_temp=st,
-                perturbed_field=pf2,
-                xray_source_box=xrs,
-                cleanup=(cleanup and iz == (len(scrollz) - 1)),
-                **kw,
-            )
-
-        ib2 = sf.compute_ionization_field(
-            previous_ionized_box=ib,
-            perturbed_field=pf2,
-            previous_perturbed_field=pf,
-            spin_temp=st2,
-            halobox=hbox2,
-            cleanup=(cleanup and iz == (len(scrollz) - 1)),
-            **kw,
-        )
-        log10_mturnovers[iz] = ib2.log10_Mturnover_ave
-        log10_mturnovers_mini[iz] = ib2.log10_Mturnover_MINI_ave
-
-        bt2 = sf.brightness_temperature(
+    for iz, coeval in enumerate(
+        _redshift_loop_generator(
             inputs=inputs,
-            ionized_box=ib2,
-            perturbed_field=pf2,
-            spin_temp=st2,
-            **iokw,
-        )
-
-        coeval = Coeval(
-            redshift=z,
             initial_conditions=initial_conditions,
-            perturbed_field=pf2,
-            ionized_box=ib2,
-            brightness_temp=bt2,
-            ts_box=st2,
-            halobox=hbox2,
+            all_redshifts=scrollz,
+            perturbed_field=perturbed_fields,
+            pt_halos=pt_halos,
+            write=write,
+            cleanup=cleanup,
+            always_purge=always_purge,
             photon_nonconservation_data=photon_nonconservation_data,
-            _globals=None,
+            start_idx=lightcone._last_completed_node + 1,
+            init_coeval=prev_coeval,
+            iokw=iokw,
         )
-
-        perturb_files.append((z, direc / pf2.filename))
-        if inputs.flag_options.USE_HALO_FIELD:
-            hbox_files.append((z, direc / hbox2.filename))
-            if not inputs.flag_options.FIXED_HALO_GRIDS:
-                phf_files.append((z, direc / ph2.filename))
-        if inputs.flag_options.USE_TS_FLUCT:
-            spin_temp_files.append((z, direc / st2.filename))
-        ionize_files.append((z, direc / ib2.filename))
-        brightness_files.append((z, direc / bt2.filename))
-
+    ):
         # Save mean/global quantities
         for quantity in global_quantities:
             lightcone.global_quantities[quantity][iz] = np.mean(
                 getattr(coeval, quantity)
             )
 
+        # Update photon conservation data in-place
+        lightcone.photon_nonconservation_data |= coeval.photon_nonconservation_data
+
         # Get lightcone slices
         lc_index = None
         if prev_coeval is not None:
@@ -705,83 +469,36 @@ def _run_lightcone_from_perturbed_fields(
 
             # only checkpoint if we have slices
             if lightcone_filename and lc_index is not None:
-                lightcone.make_checkpoint(
-                    lightcone_filename, redshift=z, index=lc_index
-                )
+                lightcone.make_checkpoint(lightcone_filename, lcidx=idx, node_index=iz)
 
-        # purge arrays we don't need
-        if pf is not None:
-            with contextlib.suppress(OSError):
-                pf.purge(force=always_purge)
-        if ph2 is not None:
-            with contextlib.suppress(OSError):
-                ph2.purge(force=always_purge)
-        # we only need the SFR fields at previous redshifts for XraySourceBox
-        if hbox is not None:
-            with contextlib.suppress(OSError):
-                hbox.prepare(
-                    keep=[
-                        "halo_sfr",
-                        "halo_sfr_mini",
-                        "halo_xray",
-                        "log10_Mcrit_MCG_ave",
-                    ],
-                    force=always_purge,
-                )
-
-        # Save current ones as old ones.
-        pf = pf2
-        hbox = hbox2
-        st = st2
-        ib = ib2
         prev_coeval = coeval
 
         # last redshift things
         if iz == len(scrollz) - 1:
-            if inputs.flag_options.PHOTON_CONS_TYPE == "z-photoncons":
-                photon_nonconservation_data = _get_photon_nonconservation_data()
-
             if lib.photon_cons_allocated:
                 lib.FreePhotonConsMemory()
 
-            lightcone.photon_nonconservation_data = photon_nonconservation_data
             if isinstance(lightcone, AngularLightcone) and lightconer.get_los_velocity:
                 lightcone.compute_rsds(
                     fname=lightcone_filename, n_subcells=inputs.astro_params.N_RSD_STEPS
                 )
 
-        # Append some info to the lightcone before we return
-        lightcone.cache_files = {
-            "init": [(0, direc / initial_conditions.filename)],
-            "perturb_field": perturb_files,
-            "ionized_box": ionize_files,
-            "brightness_temp": brightness_files,
-            "spin_temp": spin_temp_files,
-            "halobox": hbox_files,
-            "pt_halos": phf_files,
-        }
-
-        lightcone.log10_mturnovers = log10_mturnovers
-        lightcone.log10_mturnovers_mini = log10_mturnovers_mini
+        yield iz, coeval.redshift, coeval, lightcone
 
-        yield iz, z, coeval, lightcone
 
-
-def run_lightcone(
+@high_level_func
+def generate_lightcone(
     *,
     lightconer: Lightconer,
     inputs: InputParameters,
     global_quantities=("brightness_temp", "xH_box"),
     initial_conditions: InitialConditions | None = None,
-    perturbed_fields: Sequence[PerturbedField | None] = (None,),
-    cleanup=True,
-    write=None,
-    direc=None,
-    hooks=None,
-    regenerate=None,
+    cleanup: bool = True,
+    write: CacheConfig = CacheConfig(),
+    cache: OutputCache = OutputCache("."),
+    regenerate: bool = True,
     always_purge: bool = False,
     lightcone_filename: str | Path = None,
-    **global_kwargs,
 ):
     r"""
     Create a generator function for a lightcone run.
@@ -803,10 +520,6 @@ def run_lightcone(
     initial_conditions : :class:`~InitialConditions`, optional
         If given, the user and cosmo params will be set from this object, and it will not be
         re-calculated.
-    perturbed_fields : list of :class:`~PerturbedField`, optional
-        If given, must be compatible with initial_conditions. It will merely negate the necessity of
-        re-calculating the
-        perturb fields. It will also be used to set the node redshifts if given.
     cleanup : bool, optional
         A flag to specify whether the C routine cleans up its memory before returning.
         Typically, if `spin_temperature` is called directly, you will want this to be
@@ -817,10 +530,6 @@ def run_lightcone(
         The filename to which to save the lightcone. The lightcone is returned in
         memory, and can be saved manually later, but including this filename will
         save the lightcone on each iteration, which can be helpful for checkpointing.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Returns
     -------
@@ -834,21 +543,13 @@ def run_lightcone(
     regenerate, write, direc, hooks
         See docs of :func:`initial_conditions` for more information.
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
-    pf_given = any(perturbed_fields)
-    if pf_given and initial_conditions is None:
-        raise ValueError(
-            "If perturbed_fields are provided, initial_conditions must be provided"
-        )
-
-    # TODO: make sure cosmo_params is consistent with lightconer.cosmo and cell sizes as well
-    inputs.check_output_compatibility((initial_conditions,) + perturbed_fields)
+    if isinstance(write, bool):
+        write = CacheConfig() if write else CacheConfig.off()
 
     if len(inputs.node_redshifts) == 0:
         raise ValueError(
-            "You are attempting to run a lightcone with no node_redshifts.\n"
-            + "This can only be done with coevals without evolution"
+            "You are attempting to run a lightcone with no node_redshifts."
+            "Please set node_redshifts on the `inputs` parameter."
         )
 
     # while we still use the full list for caching etc, we don't need to run below the lightconer instance
@@ -859,11 +560,6 @@ def run_lightcone(
         final_node = np.argmax(below_lc_z)
         scrollz = scrollz[: final_node + 1]  # inclusive
 
-    if pf_given and scrollz != [pf.redshift for pf in perturbed_fields]:
-        raise ValueError(
-            f"given PerturbField redshifts {[pf.redshift for pf in perturbed_fields]}"
-            + f"do not match selected InputParameters.node_redshifts {scrollz}"
-        )
     lcz = lightconer.lc_redshifts
 
     if not np.all(min(scrollz) * 0.99 < lcz) and np.all(lcz < max(scrollz) * 1.01):
@@ -875,35 +571,21 @@ def run_lightcone(
             f"while the lightcone redshift range is {lcz.min()} to {lcz.max()}."
         )
 
-    iokw = {"hooks": hooks, "regenerate": regenerate, "direc": direc}
-
-    if initial_conditions is None:  # no need to get cosmo, user params out of it.
-        initial_conditions = sf.compute_initial_conditions(
-            inputs=inputs,
-            **iokw,
-        )
+    iokw = {"cache": cache, "regenerate": regenerate}
 
-    # We can go ahead and purge some of the stuff in the initial_conditions, but only if
-    # it is cached -- otherwise we could be losing information.
-    try:
-        # TODO: should really check that the file at path actually contains a fully
-        # working copy of the initial_conditions.
-        initial_conditions.prepare_for_perturb(
-            flag_options=inputs.flag_options, force=always_purge
-        )
-    except OSError:
-        pass
-
-    if not pf_given:
-        perturbed_fields = []
-        for z in scrollz:
-            p = sf.perturb_field(
-                redshift=z, inputs=inputs, initial_conditions=initial_conditions, **iokw
-            )
-            if inputs.user_params.MINIMIZE_MEMORY:
-                with contextlib.suppress(OSError):
-                    p.purge(force=always_purge)
-            perturbed_fields.append(p)
+    (
+        initial_conditions,
+        perturbed_fields,
+        pt_halos,
+        photon_nonconservation_data,
+    ) = _setup_ics_and_pfs_for_scrolling(
+        all_redshifts=scrollz,
+        initial_conditions=initial_conditions,
+        inputs=inputs,
+        write=write,
+        always_purge=always_purge,
+        **iokw,
+    )
 
     yield from _run_lightcone_from_perturbed_fields(
         initial_conditions=initial_conditions,
@@ -911,23 +593,19 @@ def run_lightcone(
         lightconer=lightconer,
         inputs=inputs,
         regenerate=regenerate,
+        pt_halos=pt_halos,
+        photon_nonconservation_data=photon_nonconservation_data,
         global_quantities=global_quantities,
-        direc=direc,
+        cache=cache,
+        write=write,
         cleanup=cleanup,
-        hooks=hooks,
         always_purge=always_purge,
         lightcone_filename=lightcone_filename,
-        **global_kwargs,
     )
 
 
-def exhaust_lightcone(**kwargs):
-    """
-    Convenience function to run through an entire lightcone.
+def run_lightcone(**kwargs):  # noqa: D103
+    return exhaust(generate_lightcone(**kwargs))
 
-    keywords passed are identical to run_lightcone.
-    """
-    lc_gen = run_lightcone(**kwargs)
-    [[iz, z, coev, lc]] = deque(lc_gen, maxlen=1)
 
-    return iz, z, coev, lc
+run_lightcone.__doc__ = generate_lightcone.__doc__
diff --git a/src/py21cmfast/drivers/param_config.py b/src/py21cmfast/drivers/param_config.py
deleted file mode 100644
index cb4ddc305..000000000
--- a/src/py21cmfast/drivers/param_config.py
+++ /dev/null
@@ -1,316 +0,0 @@
-"""Functions for setting up and configuring inputs to driver functions."""
-
-from __future__ import annotations
-
-import attrs
-import logging
-import numpy as np
-import os
-import warnings
-from functools import cached_property
-from typing import Any, Sequence
-
-from .._cfg import config
-from ..run_templates import create_params_from_template
-from ..wrapper.globals import global_params
-from ..wrapper.inputs import (
-    AstroParams,
-    CosmoParams,
-    FlagOptions,
-    InputStruct,
-    UserParams,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class InputCrossValidationError(ValueError):
-    """Error when two parameters from different structs aren't consistent."""
-
-    pass
-
-
-def input_param_field(kls: InputStruct):
-    """An attrs field that must be an InputStruct.
-
-    Parameters
-    ----------
-    kls : InputStruct subclass
-        The parameter structure which should be returned as an attrs field
-
-    """
-    return attrs.field(
-        default=kls.new(),
-        converter=kls.new,
-        validator=attrs.validators.instance_of(kls),
-    )
-
-
-def get_logspaced_redshifts(
-    min_redshift: float,
-    z_step_factor: float,
-    max_redshift: float,
-):
-    """Compute a sequence of redshifts to evolve over that are log-spaced."""
-    redshifts = [min_redshift]
-    while redshifts[-1] < max_redshift:
-        redshifts.append((redshifts[-1] + 1.0) * z_step_factor - 1.0)
-
-    return np.array(redshifts)[::-1]
-
-
-def _node_redshifts_converter(value, self):
-    # we assume an array-like is passed
-    if hasattr(value, "__len__"):
-        return np.sort(np.array(value, dtype=float).flatten())[::-1]
-    if isinstance(value, float):
-        return np.array([value])
-    return np.array([])
-
-
-@attrs.define(kw_only=True, frozen=True)
-class InputParameters:
-    """A class defining a collection of InputStruct instances.
-
-    This class simplifies combining different InputStruct instances together, performing
-    validation checks between them, and being able to cross-check compatibility between
-    different sets of instances.
-    """
-
-    random_seed = attrs.field(converter=int)
-    user_params: UserParams = input_param_field(UserParams)
-    cosmo_params: CosmoParams = input_param_field(CosmoParams)
-    flag_options: FlagOptions = input_param_field(FlagOptions)
-    astro_params: AstroParams = input_param_field(AstroParams)
-
-    # passed to the converter, TODO: this can be cleaned up
-    node_redshifts = attrs.field(
-        converter=attrs.Converter(_node_redshifts_converter, takes_self=True)
-    )
-
-    @node_redshifts.default
-    def _node_redshifts_default(self):
-        return (
-            get_logspaced_redshifts(
-                min_redshift=5.5,
-                max_redshift=self.user_params.Z_HEAT_MAX,
-                z_step_factor=self.user_params.ZPRIME_STEP_FACTOR,
-            )
-            if (self.flag_options.INHOMO_RECO or self.flag_options.USE_TS_FLUCT)
-            else None
-        )
-
-    @node_redshifts.validator
-    def _node_redshifts_validator(self, att, val):
-        if (
-            self.flag_options.INHOMO_RECO or self.flag_options.USE_TS_FLUCT
-        ) and val.max() < self.user_params.Z_HEAT_MAX:
-            raise ValueError(
-                "For runs with inhomogeneous recombinations or spin temperature fluctuations,\n"
-                + f"your maximum passed node_redshifts {val.max()} must be above Z_HEAT_MAX {self.user_params.Z_HEAT_MAX}"
-            )
-
-    @flag_options.validator
-    def _flag_options_validator(self, att, val):
-        if self.user_params is not None:
-            if (
-                val.USE_MINI_HALOS
-                and not self.user_params.USE_RELATIVE_VELOCITIES
-                and not val.FIX_VCB_AVG
-            ):
-                warnings.warn(
-                    "USE_MINI_HALOS needs USE_RELATIVE_VELOCITIES to get the right evolution!"
-                )
-
-            if val.HALO_STOCHASTICITY and self.user_params.PERTURB_ON_HIGH_RES:
-                msg = (
-                    "Since the lowres density fields are required for the halo sampler"
-                    "We are currently unable to use PERTURB_ON_HIGH_RES and HALO_STOCHASTICITY"
-                    "Simultaneously."
-                )
-                raise NotImplementedError(msg)
-            if val.USE_HALO_FIELD and "GAMMA-APPROX" in (
-                self.user_params.INTEGRATION_METHOD_MINI,
-                self.user_params.INTEGRATION_METHOD_ATOMIC,
-            ):
-                msg = "the USE_HALO_FIELD mode uses more complex scaling relations which are not compatible with 'GAMMA-APPROX' integration"
-                raise ValueError(msg)
-
-        if val.USE_EXP_FILTER and not val.USE_HALO_FIELD:
-            warnings.warn("USE_EXP_FILTER has no effect unless USE_HALO_FIELD is true")
-
-    @astro_params.validator
-    def _astro_params_validator(self, att, val):
-        if val.R_BUBBLE_MAX > self.user_params.BOX_LEN:
-            raise InputCrossValidationError(
-                f"R_BUBBLE_MAX is larger than BOX_LEN ({val.R_BUBBLE_MAX} > {self.user_params.BOX_LEN}). This is not allowed."
-            )
-
-        if val.R_BUBBLE_MAX != 50 and self.flag_options.INHOMO_RECO:
-            warnings.warn(
-                "You are setting R_BUBBLE_MAX != 50 when INHOMO_RECO=True. "
-                "This is non-standard (but allowed), and usually occurs upon manual "
-                "update of INHOMO_RECO"
-            )
-
-        if val.M_TURN > 8 and self.flag_options.USE_MINI_HALOS:
-            warnings.warn(
-                "You are setting M_TURN > 8 when USE_MINI_HALOS=True. "
-                "This is non-standard (but allowed), and usually occurs upon manual "
-                "update of M_TURN"
-            )
-
-        if (
-            global_params.HII_FILTER == 1
-            and val.R_BUBBLE_MAX > self.user_params.BOX_LEN / 3
-        ):
-            msg = (
-                "Your R_BUBBLE_MAX is > BOX_LEN/3 "
-                f"({val.R_BUBBLE_MAX} > {self.user_params.BOX_LEN / 3})."
-            )
-
-            if config["ignore_R_BUBBLE_MAX_error"]:
-                warnings.warn(msg)
-            else:
-                raise ValueError(msg)
-
-    @user_params.validator
-    def _user_params_validator(self, att, val):
-        # perform a very rudimentary check to see if we are underresolved and not using the linear approx
-        if val.BOX_LEN > val.DIM and not global_params.EVOLVE_DENSITY_LINEARLY:
-            warnings.warn(
-                "Resolution is likely too low for accurate evolved density fields\n It Is recommended"
-                + "that you either increase the resolution (DIM/BOX_LEN) or"
-                + "set the EVOLVE_DENSITY_LINEARLY flag to 1"
-            )
-
-    def __getitem__(self, key):
-        """Get an item from the instance in a dict-like manner."""
-        # Also allow using **input_parameters
-        return getattr(self, key)
-
-    def is_compatible_with(self, other: InputParameters) -> bool:
-        """Check if this object is compatible with another parameter struct.
-
-        Compatibility is slightly different from strict equality. Compatibility requires
-        that if a parameter struct *exists* on the other object, then it must be equal
-        to this one. That is, if astro_params is None on the other InputParameter object,
-        then this one can have astro_params as NOT None, and it will still be
-        compatible. However the inverse is not true -- if this one has astro_params as
-        None, then all others must have it as None as well.
-        """
-        if not isinstance(other, InputParameters):
-            return False
-
-        return not any(
-            other[key] is not None and self[key] is not None and self[key] != other[key]
-            for key in self.merge_keys()
-        )
-
-    def check_output_compatibility(self, output_structs):
-        """Generate a new InputParameters instance given a list of OutputStructs.
-
-        In contrast to other construction methods, we do not accept overwriting of
-        sub-fields here, since it will no longer be compatible with the output structs.
-
-        All required fields not present in the `OutputStruct` objects need to be provided.
-        """
-        # get matching fields in each output struct
-        fieldnames = [field.name for field in attrs.fields(self.__class__) if field.eq]
-        for struct in output_structs:
-            if struct is None:
-                continue
-
-            input_params = {
-                k.lstrip(""): getattr(struct, k, None)
-                for k in struct._inputs
-                if k.lstrip("") in fieldnames
-            }
-
-            # Since self is always complete we can just compare against it
-            for field, struct_val in input_params.items():
-                input_val = getattr(self, field)
-                if struct_val is not None and struct_val != input_val:
-                    raise ValueError(
-                        f"InputParameters not compatible with {struct} {field}: inputs {input_val} != struct {struct_val}"
-                    )
-
-    def evolve_input_structs(self, **kwargs):
-        """Return an altered clone of the `InputParameters` structs.
-
-        Unlike clone(), this function takes fields from the constituent `InputStruct` classes
-        and only overwrites those sub-fields instead of the entire field
-        """
-        struct_args = {}
-        for inp_type in ("cosmo_params", "user_params", "astro_params", "flag_options"):
-            obj = getattr(self, inp_type)
-            struct_args[inp_type] = obj.clone(
-                **{k: v for k, v in kwargs.items() if hasattr(obj, k)}
-            )
-
-        return self.clone(**struct_args)
-
-    @classmethod
-    def from_template(cls, name, **kwargs):
-        """Construct full InputParameters instance from native or TOML file template.
-
-        Takes `InputStruct` fields as keyword arguments which overwrite the template/default fields
-        """
-        return cls(
-            **create_params_from_template(name),
-            **kwargs,
-        )
-
-    def clone(self, **kwargs):
-        """Generate a copy of the InputParameter structure with specified changes."""
-        return attrs.evolve(self, **kwargs)
-
-    def __repr__(self):
-        """
-        String representation of the structure.
-
-        Created by combining repr methods from the InputStructs
-        which make up this object
-        """
-        return (
-            f"cosmo_params: {repr(self.cosmo_params)}\n"
-            + f"user_params: {repr(self.user_params)}\n"
-            + f"astro_params: {repr(self.astro_params)}\n"
-            + f"flag_options: {repr(self.flag_options)}\n"
-        )
-
-    # TODO: methods for equality: w/o redshifts, w/o seed
-
-
-def check_redshift_consistency(redshift, output_structs):
-    """Make sure all given OutputStruct objects exist at the same given redshift."""
-    for struct in output_structs:
-        if struct is not None and struct.redshift != redshift:
-            raise ValueError(
-                f"Incompatible redshifts with inputs and {struct.__class__.__name__}: {redshift} != {struct.redshift}"
-            )
-
-
-def _get_config_options(
-    direc, regenerate, write, hooks
-) -> tuple[str, bool, dict[callable, dict[str, Any]]]:
-    direc = str(os.path.expanduser(config["direc"] if direc is None else direc))
-
-    if hooks is None or len(hooks) > 0:
-        hooks = hooks or {}
-
-        if callable(write) and write not in hooks:
-            hooks[write] = {"direc": direc}
-
-        if not hooks:
-            if write is None:
-                write = config["write"]
-
-            if not callable(write) and write:
-                hooks["write"] = {"direc": direc}
-
-    return (
-        direc,
-        bool(config["regenerate"] if regenerate is None else regenerate),
-        hooks,
-    )
diff --git a/src/py21cmfast/drivers/single_field.py b/src/py21cmfast/drivers/single_field.py
index 3ab4d5b93..8cb3fa83b 100644
--- a/src/py21cmfast/drivers/single_field.py
+++ b/src/py21cmfast/drivers/single_field.py
@@ -5,24 +5,13 @@
 example initial conditions, perturbed fields and ionization fields.
 """
 
-import contextlib
 import logging
 import numpy as np
 import warnings
 from astropy import units as un
 from astropy.cosmology import z_at_value
-from functools import wraps
-from pathlib import Path
-from typing import Any, Callable
-
-from ..wrapper.cfuncs import construct_fftw_wisdoms, get_halo_list_buffer_size
-from ..wrapper.inputs import (
-    AstroParams,
-    CosmoParams,
-    FlagOptions,
-    UserParams,
-    global_params,
-)
+
+from ..wrapper.inputs import InputParameters, global_params
 from ..wrapper.outputs import (
     BrightnessTemp,
     HaloBox,
@@ -34,111 +23,44 @@
     TsBox,
     XraySourceBox,
 )
-from .param_config import (
-    InputParameters,
-    _get_config_options,
+from ._param_config import (
+    check_output_consistency,
     check_redshift_consistency,
+    single_field_func,
 )
 
 logger = logging.getLogger(__name__)
 
 
-def set_globals(func: callable):
-    """Decorator that sets global parameters."""
-
-    @wraps(func)
-    def inner(*args, **kwargs):
-        # Get all kwargs that are actually global params
-        global_kwargs = {k: v for k, v in kwargs.items() if k in global_params.keys()}
-        other_kwargs = {k: v for k, v in kwargs.items() if k not in global_kwargs}
-        with global_params.use(**global_kwargs):
-            return func(*args, **other_kwargs)
-
-    return inner
-
-
-@set_globals
-def compute_initial_conditions(
-    *,
-    inputs: InputParameters,
-    regenerate: bool | None = None,
-    write: bool | None = None,
-    direc: Path | None = None,
-    hooks: dict[Callable, dict[str, Any]] | None = None,
-    **global_kwargs,
-) -> InitialConditions:
+@single_field_func
+def compute_initial_conditions(*, inputs: InputParameters) -> InitialConditions:
     r"""
     Compute initial conditions.
 
     Parameters
     ----------
-    user_params : :class:`~UserParams` instance, optional
-        Defines the overall options and parameters of the run.
-    cosmo_params : :class:`~CosmoParams` instance, optional
-        Defines the cosmological parameters used to compute initial conditions.
-    random_seed : int, optional
-        The random seed used to generate the phases of the initial conditions.
+    inputs
+        The InputParameters instance defining the run.
     regenerate : bool, optional
         Whether to force regeneration of data, even if matching cached data is found.
-        This is applied recursively to any potential sub-calculations. It is ignored in
-        the case of dependent data only if that data is explicitly passed to the function.
-    write : bool, optional
-        Whether to write results to file (i.e. cache). This is recursively applied to
-        any potential sub-calculations.
-    hooks
-        Any extra functions to apply to the output object. This should be a dictionary
-        where the keys are the functions, and the values are themselves dictionaries of
-        parameters to pass to the function. The function signature should be
-        ``(output, **params)``, where the ``output`` is the output object.
-    direc : str, optional
-        The directory in which to search for the boxes and write them. By default, this
-        is the directory given by ``boxdir`` in the configuration file,
-        ``~/.21cmfast/config.yml``. This is recursively applied to any potential
-        sub-calculations.
-
-    Other Parameters
-    ----------------
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
+    cache
+        An OutputCache object defining how to read cached boxes.
 
     Returns
     -------
     :class:`~InitialConditions`
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
     # Initialize memory for the boxes that will be returned.
-    ics = InitialConditions(inputs=inputs)
+    ics = InitialConditions.new(inputs=inputs)
+    return ics.compute()
 
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
-
-    # First check whether the boxes already exist.
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            ics.read(direc)
-            logger.info(
-                f"Existing initial_conditions found and read in (seed={ics.random_seed})."
-            )
-            return ics
-    return ics.compute(hooks=hooks)
 
-
-@set_globals
+@single_field_func
 def perturb_field(
     *,
     redshift: float,
-    inputs: InputParameters,
+    inputs: InputParameters | None = None,
     initial_conditions: InitialConditions,
-    regenerate: bool | None = None,
-    write: bool | None = None,
-    direc: Path | None = None,
-    hooks: dict[Callable, dict[str, Any]] | None = None,
-    **global_kwargs,
 ) -> PerturbedField:
     r"""
     Compute a perturbed field at a given redshift.
@@ -172,47 +94,21 @@ def perturb_field(
     The user and cosmo parameter structures are by default inferred from the
     ``initial_conditions``.
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
-    inputs.check_output_compatibility(
-        [initial_conditions],
-    )
-
     # Initialize perturbed boxes.
-    fields = PerturbedField(redshift=redshift, inputs=inputs)
-
-    # Check whether the boxes already exist
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            fields.read(direc)
-            logger.info(
-                f"Existing z={redshift} perturb_field boxes found and read in "
-                f"(seed={fields.random_seed})."
-            )
-            return fields
-
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
+    fields = PerturbedField.new(redshift=redshift, inputs=inputs)
 
     # Run the C Code
-    return fields.compute(ics=initial_conditions, hooks=hooks)
+    return fields.compute(ics=initial_conditions)
 
 
-@set_globals
+@single_field_func
 def determine_halo_list(
     *,
     redshift: float,
-    inputs: InputParameters,
+    inputs: InputParameters | None = None,
     initial_conditions: InitialConditions,
     descendant_halos: HaloField | None = None,
-    regenerate=None,
-    write=None,
-    direc=None,
-    hooks=None,
-    **global_kwargs,
-):
+) -> HaloField:
     r"""
     Find a halo list, given a redshift.
 
@@ -226,94 +122,40 @@ def determine_halo_list(
         The halos that form the descendants (i.e. lower redshift) of those computed by
         this function. If this is not provided, we generate the initial stochastic halos
         directly in this function (and progenitors can then be determined by these).
-    astro_params: :class:`~AstroParams` instance, optional
-        The astrophysical parameters defining the course of reionization.
-    flag_options: :class:`FlagOptions` instance, optional
-        The flag options enabling/disabling extra modules in the simulation.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Returns
     -------
     :class:`~HaloField`
-
-    Other Parameters
-    ----------------
-    regenerate, write, direc, random_seed:
-        See docs of :func:`initial_conditions` for more information.
-
-    Examples
-    --------
-    Fill this in once finalised
-
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
-    # Configure and check input/output parameters/structs
-    inputs.check_output_compatibility(
-        [initial_conditions, descendant_halos],
-    )
-
     if inputs.user_params.HMF != "ST":
         warnings.warn(
             "DexM Halofinder sses a fit to the Sheth-Tormen mass function."
             "With HMF!=1 the Halos from DexM will not be from the same mass function",
         )
 
-    hbuffer_size = get_halo_list_buffer_size(
-        redshift, inputs.user_params, inputs.cosmo_params
-    )
-
     if descendant_halos is None:
-        descendant_halos = HaloField(
-            redshift=0.0,
-            inputs=inputs,
-            dummy=True,
-        )
+        descendant_halos = HaloField.dummy()
 
     # Initialize halo list boxes.
-    fields = HaloField(
+    fields = HaloField.new(
         redshift=redshift,
         desc_redshift=descendant_halos.redshift,
-        buffer_size=hbuffer_size,
         inputs=inputs,
     )
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
-
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            fields.read(direc)
-            logger.info(
-                f"Existing z={redshift} determine_halo_list boxes found and read in "
-                f"(seed={fields.random_seed})."
-            )
-            return fields
 
     # Run the C Code
     return fields.compute(
         ics=initial_conditions,
-        hooks=hooks,
         descendant_halos=descendant_halos,
     )
 
 
-@set_globals
+@single_field_func
 def perturb_halo_list(
     *,
-    inputs: InputParameters,
     initial_conditions: InitialConditions,
     halo_field: HaloField,
-    regenerate=None,
-    write=None,
-    direc=None,
-    hooks=None,
-    **global_kwargs,
-):
+) -> PerturbHaloField:
     r"""
     Given a halo list, perturb the halos for a given redshift.
 
@@ -343,51 +185,30 @@ def perturb_halo_list(
     Fill this in once finalised
 
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
+    inputs = initial_conditions.inputs
     hbuffer_size = halo_field.n_halos
-
-    # Configure and check input/output parameters/structs
-    inputs.check_output_compatibility(
-        [initial_conditions, halo_field],
-    )
-
     redshift = halo_field.redshift
+
     # Initialize halo list boxes.
-    fields = PerturbHaloField(
+    fields = PerturbHaloField.new(
         redshift=redshift,
         buffer_size=hbuffer_size,
         inputs=inputs,
     )
 
-    # Check whether the boxes already exist
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            fields.read(direc)
-            logger.info(
-                f"Existing z={redshift} perturb_halo_list boxes found and read in "
-                f"(seed={fields.random_seed})."
-            )
-            return fields
-
     # Run the C Code
-    return fields.compute(ics=initial_conditions, halo_field=halo_field, hooks=hooks)
+    return fields.compute(ics=initial_conditions, halo_field=halo_field)
 
 
-@set_globals
+@single_field_func
 def compute_halo_grid(
     *,
     initial_conditions: InitialConditions,
-    inputs: InputParameters,
+    inputs: InputParameters | None = None,
     perturbed_halo_list: PerturbHaloField | None = None,
     perturbed_field: PerturbedField | None = None,
     previous_spin_temp: TsBox | None = None,
     previous_ionize_box: IonizedBox | None = None,
-    write=None,
-    direc=None,
-    regenerate: bool | None = None,
-    hooks=None,
-    **global_kwargs,
 ) -> HaloBox:
     r"""
     Compute grids of halo properties from a catalogue.
@@ -399,7 +220,7 @@ def compute_halo_grid(
     Parameters
     ----------
     initial_conditions : :class:`~InitialConditions`
-        The initial conditions of the run. The user and cosmo params
+        The initial conditions of the run.
     perturbed_halo_list: :class:`~PerturbHaloField` or None, optional
         This contains all the dark matter haloes obtained if using the USE_HALO_FIELD.
         This is a list of halo masses and coords for the dark matter haloes.
@@ -409,24 +230,12 @@ def compute_halo_grid(
         The previous spin temperature box. Used for feedback when USE_MINI_HALOS==True
     previous_ionize_box: :class:`IonizedBox` or None
         An at the last timestep. Used for feedback when USE_MINI_HALOS==True
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Returns
     -------
     :class:`~HaloBox` :
         An object containing the halo box data.
-
-    Other Parameters
-    ----------------
-    regenerate, write, direc :
-        See docs of :func:`initial_conditions` for more information.
-
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
     if perturbed_halo_list:
         redshift = perturbed_halo_list.redshift
     elif perturbed_field:
@@ -436,32 +245,7 @@ def compute_halo_grid(
             "Either perturbed_field or perturbed_halo_list are required (or both)."
         )
 
-    inputs.check_output_compatibility(
-        (
-            initial_conditions,
-            perturbed_halo_list,
-            perturbed_field,
-            previous_spin_temp,
-            previous_ionize_box,
-        ),
-    )
-    check_redshift_consistency(redshift, (perturbed_halo_list, perturbed_field))
-
-    prev_z = previous_ionize_box.redshift if previous_ionize_box else None
-    check_redshift_consistency(prev_z, (previous_ionize_box, previous_spin_temp))
-
-    # Initialize halo list boxes.
-    box = HaloBox(redshift=redshift, inputs=inputs)
-
-    # Check whether the boxes already exist
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            box.read(direc)
-            logger.info(
-                f"Existing z={redshift} halo_box boxes found and read in "
-                f"(seed={box.random_seed})."
-            )
-            return box
+    box = HaloBox.new(redshift=redshift, inputs=inputs)
 
     if perturbed_field is None:
         if inputs.flag_options.FIXED_HALO_GRIDS or inputs.user_params.AVG_BELOW_SAMPLER:
@@ -469,22 +253,15 @@ def compute_halo_grid(
                 "You must provide the perturbed field if FIXED_HALO_GRIDS is True or AVG_BELOW_SAMPLER is True"
             )
         else:
-            perturbed_field = PerturbedField(
-                redshift=0.0,
-                inputs=inputs,
-                dummy=True,
-            )
+            perturbed_field = PerturbedField.dummy()
+
     elif perturbed_halo_list is None:
         if not inputs.flag_options.FIXED_HALO_GRIDS:
             raise ValueError(
                 "You must provide the perturbed halo list if FIXED_HALO_GRIDS is False"
             )
         else:
-            perturbed_halo_list = PerturbHaloField(
-                redshift=0.0,
-                inputs=inputs,
-                dummy=True,
-            )
+            perturbed_halo_list = PerturbHaloField.dummy()
 
     # NOTE: due to the order, we use the previous spin temp here, like spin_temperature,
     #       but UNLIKE ionize_box, which uses the current box
@@ -496,11 +273,7 @@ def compute_halo_grid(
             or not inputs.flag_options.USE_MINI_HALOS
         ):
             # Dummy spin temp is OK since we're above Z_HEAT_MAX
-            previous_spin_temp = TsBox(
-                redshift=0.0,
-                inputs=inputs,
-                dummy=True,
-            )
+            previous_spin_temp = TsBox.dummy()
         else:
             raise ValueError("Below Z_HEAT_MAX you must specify the previous_spin_temp")
 
@@ -510,7 +283,7 @@ def compute_halo_grid(
             or not inputs.flag_options.USE_MINI_HALOS
         ):
             # Dummy ionize box is OK since we're above Z_HEAT_MAX
-            previous_ionize_box = IonizedBox(redshift=0.0, inputs=inputs, dummy=True)
+            previous_ionize_box = IonizedBox.dummy()
         else:
             raise ValueError(
                 "Below Z_HEAT_MAX you must specify the previous_ionize_box"
@@ -522,13 +295,11 @@ def compute_halo_grid(
         perturbed_field=perturbed_field,
         previous_ionize_box=previous_ionize_box,
         previous_spin_temp=previous_spin_temp,
-        hooks=hooks,
     )
 
 
 # TODO: make this more general and probably combine with the lightcone interp function
 def interp_halo_boxes(
-    inputs: InputParameters,
     halo_boxes: list[HaloBox],
     fields: list[str],
     redshift: float,
@@ -555,6 +326,7 @@ def interp_halo_boxes(
     :class:`~HaloBox` :
         An object containing the halo box data
     """
+    inputs = halo_boxes[0].inputs
     z_halos = [box.redshift for box in halo_boxes]
     if not np.all(np.diff(z_halos) > 0):
         raise ValueError("halo_boxes must be in ascending order of redshift")
@@ -576,45 +348,34 @@ def interp_halo_boxes(
 
     # I set the box redshift to be the stored one so it is read properly into the ionize box
     # for the xray source it doesn't matter, also since it is not _compute()'d, it won't be cached
-    inputs.check_output_compatibility(halo_boxes)
-    hbox_out = HaloBox(
-        redshift=redshift,
-        inputs=inputs,
+    check_output_consistency(
+        dict(zip([f"box-{i}" for i in range(len(halo_boxes))], halo_boxes))
     )
+    hbox_out = HaloBox.new(redshift=redshift, inputs=inputs)
 
     # initialise the memory
-    hbox_out()
+    hbox_out._init_arrays()
 
     # interpolate halo boxes in gridded SFR
     hbox_prog = halo_boxes[idx_prog]
     hbox_desc = halo_boxes[idx_desc]
 
     for field in fields:
-        interp_field = (1 - interp_param) * getattr(
-            hbox_desc, field
-        ) + interp_param * getattr(hbox_prog, field)
-        if field in hbox_out._array_state.keys():
-            getattr(hbox_out, field)[...] = interp_field
-        else:
-            setattr(hbox_out, field, interp_field)
+        interp_field = (1 - interp_param) * hbox_desc.get(
+            field
+        ) + interp_param * hbox_prog.get(field)
+        hbox_out.set(field, interp_field)
 
     logger.debug(
         f"interpolated to z={redshift} between [{z_desc},{z_prog}] ({interp_param})"
     )
     logger.debug(
-        f"{fields[0]} averages desc ({idx_desc}): {getattr(hbox_desc, fields[0]).mean()}"
-        + f" interp {getattr(hbox_out, fields[0]).mean()}"
-        + f" prog ({idx_prog}) {getattr(hbox_prog, fields[0]).mean()}"
+        f"{fields[0]} averages desc ({idx_desc}): {hbox_desc.get(fields[0]).mean()}"
+        + f" interp {hbox_out.get(fields[0]).mean()}"
+        + f" prog ({idx_prog}) {hbox_prog.get(fields[0]).mean()}"
     )
 
-    # HACK: this passes the field pointers to the backend,
-    # NOTE: the arrays are initialised in the call above so they shouldn't
-    # be re-initialised causing a memory leak
-    hbox_out._init_cstruct()
-    # HACK: Since we don't compute, we have to mark the struct as computed
-    for k, state in hbox_out._array_state.items():
-        if state.initialized and k in fields:
-            state.computed_in_mem = True
+    hbox_out.sync()
 
     return hbox_out
 
@@ -622,17 +383,11 @@ def interp_halo_boxes(
 # NOTE: the current implementation of this box is very hacky, since I have trouble figuring out a way to _compute()
 #   over multiple redshifts in a nice way using this wrapper.
 # TODO: if we move some code to jax or similar I think this would be one of the first candidates (just filling out some filtered grids)
-@set_globals
+@single_field_func
 def compute_xray_source_field(
     *,
-    inputs: InputParameters,
     initial_conditions: InitialConditions,
     hboxes: list[HaloBox],
-    write=None,
-    direc=None,
-    regenerate=None,
-    hooks=None,
-    **global_kwargs,
 ) -> XraySourceBox:
     r"""
     Compute filtered grid of SFR for use in spin temperature calculation.
@@ -649,45 +404,18 @@ def compute_xray_source_field(
         The initial conditions of the run. The user and cosmo params
     hboxes: Sequence of :class:`~HaloBox` instances
         This contains the list of Halobox instances which are used to create this source field
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Returns
     -------
     :class:`~XraySourceBox` :
         An object containing x ray heating, ionisation, and lyman alpha rates.
-
-    Other Parameters
-    ----------------
-    regenerate, write, direc :
-        See docs of :func:`initial_conditions` for more information.
-
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
     z_halos = [hb.redshift for hb in hboxes]
-    inputs.check_output_compatibility(hboxes + [initial_conditions])
+    inputs = hboxes[0].inputs
     redshift = z_halos[-1]
 
     # Initialize halo list boxes.
-    box = XraySourceBox(redshift=redshift, inputs=inputs)
-
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
-
-    # Check whether the boxes already exist
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            box.read(direc)
-            logger.info(
-                f"Existing z={redshift} xray_source boxes found and read in "
-                f"(seed={box.random_seed})."
-            )
-            return box
+    box = XraySourceBox.new(redshift=redshift, inputs=inputs)
 
     # set minimum R at cell size
     l_factor = (4 * np.pi / 3.0) ** (-1 / 3)
@@ -713,86 +441,127 @@ def compute_xray_source_field(
     zpp_avg = zpp_edges - np.diff(np.insert(zpp_edges, 0, redshift)) / 2
 
     # call the box the initialize the memory, since I give some values before computing
-    box()
-    final_box_computed = False
+    box._init_arrays()
     for i in range(global_params.NUM_FILTER_STEPS_FOR_Ts):
         R_inner = R_range[i - 1].to("Mpc").value if i > 0 else 0
         R_outer = R_range[i].to("Mpc").value
 
         if zpp_avg[i] >= z_max:
-            box.filtered_sfr[i] = 0
-            box.filtered_sfr_mini[i] = 0
-            box.filtered_xray[i] = 0
-            box.mean_log10_Mcrit_LW[i] = inputs.astro_params.M_TURN  # minimum
+            box.filtered_sfr.value[i] = 0
+            box.filtered_sfr_mini.value[i] = 0
+            box.filtered_xray.value[i] = 0
+            box.mean_log10_Mcrit_LW.value[i] = inputs.astro_params.M_TURN  # minimum
             logger.debug(f"ignoring Radius {i} which is above Z_HEAT_MAX")
             continue
 
         hbox_interp = interp_halo_boxes(
-            inputs,
-            hboxes[::-1],
-            ["halo_sfr", "halo_xray", "halo_sfr_mini", "log10_Mcrit_MCG_ave"],
-            zpp_avg[i],
+            halo_boxes=hboxes[::-1],
+            fields=["halo_sfr", "halo_xray", "halo_sfr_mini", "log10_Mcrit_MCG_ave"],
+            redshift=zpp_avg[i],
         )
 
         # if we have no halos we ignore the whole shell
-        if np.all(hbox_interp.halo_sfr + hbox_interp.halo_sfr_mini == 0):
-            box.filtered_sfr[i] = 0
-            box.filtered_sfr_mini[i] = 0
-            box.filtered_xray[i] = 0
-            box.mean_log10_Mcrit_LW[i] = hbox_interp.log10_Mcrit_MCG_ave
+        if np.all(hbox_interp.halo_sfr.value + hbox_interp.halo_sfr_mini.value == 0):
+            box.filtered_sfr.value[i] = 0
+            box.filtered_sfr_mini.value[i] = 0
+            box.filtered_xray.value[i] = 0
+            box.mean_log10_Mcrit_LW.value[i] = hbox_interp.log10_Mcrit_MCG_ave
             logger.debug(f"ignoring Radius {i} due to no stars")
             continue
 
-        # HACK: so that I can compute in the loop multiple times
-        # since the array state is initialized already it shouldn't re-initialise
-        for k, state in box._array_state.items():
-            if state.initialized:
-                state.computed_in_mem = False
-
-        # we only want to call hooks at the end so we set a dummy hook here
-        hooks_in = hooks if i == global_params.NUM_FILTER_STEPS_FOR_Ts - 1 else {}
-
         box = box.compute(
             halobox=hbox_interp,
             R_inner=R_inner,
             R_outer=R_outer,
             R_ct=i,
-            hooks=hooks_in,
+            allow_already_computed=True,
         )
-        if i == global_params.NUM_FILTER_STEPS_FOR_Ts - 1:
-            final_box_computed = True
 
-    # HACK: sometimes we don't compute on the last step
+    # Sometimes we don't compute on the last step
     # (if the first zpp > z_max or there are no halos at max R)
     # in which case the array is not marked as computed
-    if not final_box_computed:
-        # we need to pass the memory to C, mark it as computed and call the hooks
-        box()
-
-        for k, state in box._array_state.items():
-            if state.initialized:
-                state.computed_in_mem = True
+    for name, array in box.arrays.items():
+        setattr(box, name, array.with_value(array.value))
 
-        box._call_hooks(hooks)
+    box.sync()
 
     return box
 
 
-@set_globals
+@single_field_func
+def compute_spin_temperature(
+    *,
+    initial_conditions: InitialConditions,
+    perturbed_field: PerturbedField,
+    inputs: InputParameters | None = None,
+    xray_source_box: XraySourceBox | None = None,
+    previous_spin_temp: TsBox | None = None,
+    cleanup: bool = False,
+) -> TsBox:
+    r"""
+    Compute spin temperature boxes at a given redshift.
+
+    See the notes below for how the spin temperature field is evolved through redshift.
+
+    Parameters
+    ----------
+    initial_conditions : :class:`~InitialConditions`
+        The initial conditions
+    perturbed_field : :class:`~PerturbField`, optional
+        If given, this field will be used, otherwise it will be generated. To be generated,
+        either `initial_conditions` and `redshift` must be given, or `user_params`, `cosmo_params` and
+        `redshift`. By default, this will be generated at the same redshift as the spin temperature
+        box. The redshift of perturb field is allowed to be different than `redshift`. If so, it
+        will be interpolated to the correct redshift, which can provide a speedup compared to
+        actually computing it at the desired redshift.
+    xray_source_box : :class:`XraySourceBox`, optional
+        If USE_HALO_FIELD is True, this box specifies the filtered sfr and xray emissivity at all
+        redshifts/filter radii required by the spin temperature algorithm.
+    previous_spin_temp : :class:`TsBox` or None
+        The previous spin temperature box. Needed when we are beyond the first snapshot
+
+    Returns
+    -------
+    :class:`~TsBox`
+        An object containing the spin temperature box data.
+    """
+    redshift = perturbed_field.redshift
+
+    if redshift >= inputs.user_params.Z_HEAT_MAX:
+        previous_spin_temp = TsBox.new(inputs=inputs, redshift=0.0, dummy=True)
+
+    if xray_source_box is None:
+        if inputs.flag_options.USE_HALO_FIELD:
+            raise ValueError("xray_source_box is required when USE_HALO_FIELD is True")
+        else:
+            xray_source_box = XraySourceBox.dummy()
+
+    # Set up the box without computing anything.
+    box = TsBox.new(
+        redshift=redshift,
+        inputs=inputs,
+    )
+
+    # Run the C Code
+    return box.compute(
+        cleanup=cleanup,
+        perturbed_field=perturbed_field,
+        xray_source_box=xray_source_box,
+        prev_spin_temp=previous_spin_temp,
+        ics=initial_conditions,
+    )
+
+
+@single_field_func
 def compute_ionization_field(
     *,
-    inputs: InputParameters,
     perturbed_field: PerturbedField,
     initial_conditions: InitialConditions,
+    inputs: InputParameters | None = None,
     previous_perturbed_field: PerturbedField | None = None,
     previous_ionized_box: IonizedBox | None = None,
     spin_temp: TsBox | None = None,
     halobox: HaloBox | None = None,
-    regenerate=None,
-    write=None,
-    direc=None,
-    hooks=None,
-    **global_kwargs,
 ) -> IonizedBox:
     r"""
     Compute an ionized box at a given redshift.
@@ -802,8 +571,6 @@ def compute_ionization_field(
 
     Parameters
     ----------
-    initial_conditions : :class:`~InitialConditions`
-        The initial conditions
     perturbed_field : :class:`~PerturbField`
         The perturbed density field.
     previous_perturbed_field : :class:`~PerturbField`, optional
@@ -821,184 +588,53 @@ def compute_ionization_field(
     halobox: :class:`~HaloBox` or None, optional
         If passed, this contains all the dark matter haloes obtained if using the USE_HALO_FIELD.
         These are grids of containing summed halo properties such as ionizing emissivity.
-    astro_params: :class:`~AstroParams` instance, optional
-        The astrophysical parameters defining the course of reionization.
-    flag_options: :class:`FlagOptions` instance, optional
-        The flag options enabling/disabling extra modules in the simulation.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Returns
     -------
     :class:`~IonizedBox` :
         An object containing the ionized box data.
 
-    Other Parameters
-    ----------------
-    regenerate, write, direc, random_seed :
-        See docs of :func:`initial_conditions` for more information.
-
     Notes
     -----
     Typically, the ionization field at any redshift is dependent on the evolution of xHI up until
     that redshift, which necessitates providing a previous ionization field to define the current
-    one. This function provides several options for doing so. First, if neither the spin
-    temperature field, nor inhomogeneous recombinations (specified in flag options) are used, no
-    evolution needs to be done. Otherwise, either (in order of precedence)
-
-    1. a specific previous :class`~IonizedBox` object is provided, which will be used directly,
-    2. a previous redshift is provided, for which a cached field on disk will be sought,
-    3. a step factor is provided which recursively steps through redshift, calculating previous
-       fields up until Z_HEAT_MAX, and returning just the final field at the current redshift, or
-    4. the function is instructed to treat the current field as being an initial "high-redshift"
-       field such that specific sources need not be found and evolved.
-
-    .. note:: If a previous specific redshift is given, but no cached field is found at that
-              redshift, the previous ionization field will be evaluated based on `z_step_factor`.
-
-    Examples
-    --------
-    By default, no spin temperature is used, and neither are inhomogeneous recombinations,
-    so that no evolution is required, thus the following will compute a coeval ionization box:
-
-    >>> xHI = compute_ionization_field(redshift=7.0)
-
-    However, if either of those options are true, then a full evolution will be required:
-
-    >>> xHI = compute_ionization_field(redshift=7.0, flag_options=FlagOptions(INHOMO_RECO=True,USE_TS_FLUCT=True))
-
-    This will by default evolve the field from a redshift of *at least* `Z_HEAT_MAX` (a global
-    parameter), in logarithmic steps of `ZPRIME_STEP_FACTOR`. To change these:
-
-    >>> xHI = compute_ionization_field(redshift=7.0, zprime_step_factor=1.2, z_heat_max=15.0,
-    >>>                  flag_options={"USE_TS_FLUCT":True})
-
-    Alternatively, one can pass an exact previous redshift, which will be sought in the disk
-    cache, or evaluated:
-
-    >>> ts_box = compute_ionization_field(redshift=7.0, previous_ionize_box=8.0, flag_options={
-    >>>                     "USE_TS_FLUCT":True})
-
-    Beware that doing this, if the previous box is not found on disk, will continue to evaluate
-    prior boxes based on `ZPRIME_STEP_FACTOR`. Alternatively, one can pass a previous
-    :class:`~IonizedBox`:
-
-    >>> xHI_0 = compute_ionization_field(redshift=8.0, flag_options={"USE_TS_FLUCT":True})
-    >>> xHI = compute_ionization_field(redshift=7.0, previous_ionize_box=xHI_0)
-
-    Again, the first line here will implicitly use ``ZPRIME_STEP_FACTOR`` to evolve the field from
-    ``Z_HEAT_MAX``. Note that in the second line, all of the input parameters are taken directly from
-    `xHI_0` so that they are consistent, and we need not specify the ``flag_options``.
-
-    As the function recursively evaluates previous redshift, the previous spin temperature fields
-    will also be consistently recursively evaluated. Only the final ionized box will actually be
-    returned and kept in memory, however intervening results will by default be cached on disk.
-    One can also pass an explicit spin temperature object:
-
-    >>> ts = spin_temperature(redshift=7.0)
-    >>> xHI = compute_ionization_field(redshift=7.0, spin_temp=ts)
-
-    If automatic recursion is used, then it is done in such a way that no large boxes are kept
-    around in memory for longer than they need to be (only two at a time are required).
+    one. If neither the spin temperature field, nor inhomogeneous recombinations (specified in
+    flag options) are used, no evolution needs to be done. If the redshift is beyond
+    Z_HEAT_MAX, previous fields are not required either.
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
-    # Configure and check input/output parameters/structs
-    inputs.check_output_compatibility(
-        (
-            initial_conditions,
-            perturbed_field,
-            previous_perturbed_field,
-            previous_ionized_box,
-            spin_temp,
-            halobox,
-        ),
-    )
     redshift = perturbed_field.redshift
-    check_redshift_consistency(redshift, [perturbed_field, spin_temp, halobox])
 
-    # Get the previous redshift
-    if previous_ionized_box is not None:
-        prev_z = previous_ionized_box.redshift
+    if redshift >= inputs.user_params.Z_HEAT_MAX:
+        # Previous boxes must be "initial"
+        previous_ionized_box = IonizedBox.initial()
+        previous_perturbed_field = PerturbedField.initial()
 
-        # Ensure the previous ionized box has a higher redshift than this one.
-        if prev_z <= redshift:
+    if inputs.evolution_required:
+        if previous_ionized_box is None:
             raise ValueError(
-                "Previous ionized box must have a higher redshift than that being evaluated."
-                + f"{prev_z} <= {redshift}"
+                "You need to provide a previous ionized box when redshift < Z_HEAT_MAX."
             )
-    elif (
-        not inputs.flag_options.INHOMO_RECO
-        and not inputs.flag_options.USE_TS_FLUCT
-        or redshift >= inputs.user_params.Z_HEAT_MAX
-    ):
-        prev_z = 0  # signal value for first box
-    else:
-        raise ValueError(
-            "You need to provide a previous ionized box when redshift < Z_HEAT_MAX."
-        )
-
-    check_redshift_consistency(prev_z, [previous_perturbed_field, previous_ionized_box])
-
-    box = IonizedBox(
-        inputs=inputs,
-        redshift=redshift,
-        prev_ionize_redshift=prev_z,
-    )
-
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
-
-    # Check whether the boxes already exist
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            box.read(direc)
-            logger.info(
-                f"Existing z={redshift} ionized boxes found and read in (seed={box.random_seed})."
+        if previous_perturbed_field is None:
+            raise ValueError(
+                "You need to provide a previous perturbed field when redshift < Z_HEAT_MAX."
             )
-            return box
-
-    # EVERYTHING PAST THIS POINT ONLY HAPPENS IF THE BOX DOESN'T ALREADY EXIST
-    # ------------------------------------------------------------------------
-
-    # Get appropriate previous ionization box
-    if previous_ionized_box is None:
-        previous_ionized_box = IonizedBox(redshift=0.0, inputs=inputs, initial=True)
+    else:
+        if previous_ionized_box is None:
+            previous_ionized_box = IonizedBox.initial()
+        if previous_perturbed_field is None:
+            previous_perturbed_field = PerturbedField.initial()
 
-    if not inputs.flag_options.USE_MINI_HALOS:
-        previous_perturbed_field = PerturbedField(
-            redshift=0.0, inputs=inputs, initial=True
-        )
-    elif previous_perturbed_field is None:
-        # If we are beyond Z_HEAT_MAX, just make an empty box
-        if prev_z == 0:
-            previous_perturbed_field = PerturbedField(
-                redshift=0.0, inputs=inputs, initial=True
-            )
-        else:
-            raise ValueError("No previous perturbed field given, but one is required.")
+    box = IonizedBox.new(inputs=inputs, redshift=redshift)
 
     if not inputs.flag_options.USE_HALO_FIELD:
         # Construct an empty halo field to pass in to the function.
-        halobox = HaloBox(
-            redshift=0.0,
-            inputs=inputs,
-            dummy=True,
-        )
+        halobox = HaloBox.dummy()
     elif halobox is None:
         raise ValueError("No halo box given but USE_HALO_FIELD=True")
 
     # Set empty spin temp box if necessary.
     if not inputs.flag_options.USE_TS_FLUCT:
-        spin_temp = TsBox(
-            redshift=0.0,
-            inputs=inputs,
-            dummy=True,
-        )
+        spin_temp = TsBox.dummy()
     elif spin_temp is None:
         raise ValueError("No spin temperature box given but USE_TS_FLUCT=True")
 
@@ -1010,206 +646,15 @@ def compute_ionization_field(
         spin_temp=spin_temp,
         halobox=halobox,
         ics=initial_conditions,
-        hooks=hooks,
     )
 
 
-@set_globals
-def spin_temperature(
-    *,
-    inputs: InputParameters,
-    initial_conditions: InitialConditions,
-    perturbed_field: PerturbedField,
-    xray_source_box: XraySourceBox | None = None,
-    previous_spin_temp: TsBox | None = None,
-    regenerate=None,
-    write=None,
-    direc=None,
-    cleanup=True,
-    hooks=None,
-    **global_kwargs,
-) -> TsBox:
-    r"""
-    Compute spin temperature boxes at a given redshift.
-
-    See the notes below for how the spin temperature field is evolved through redshift.
-
-    Parameters
-    ----------
-    initial_conditions : :class:`~InitialConditions`
-        The initial conditions
-    perturbed_field : :class:`~PerturbField`, optional
-        If given, this field will be used, otherwise it will be generated. To be generated,
-        either `initial_conditions` and `redshift` must be given, or `user_params`, `cosmo_params` and
-        `redshift`. By default, this will be generated at the same redshift as the spin temperature
-        box. The redshift of perturb field is allowed to be different than `redshift`. If so, it
-        will be interpolated to the correct redshift, which can provide a speedup compared to
-        actually computing it at the desired redshift.
-    xray_source_box : :class:`XraySourceBox`, optional
-        If USE_HALO_FIELD is True, this box specifies the filtered sfr and xray emissivity at all
-        redshifts/filter radii required by the spin temperature algorithm.
-    previous_spin_temp : :class:`TsBox` or None
-        The previous spin temperature box. Needed when we are beyond the first snapshot
-    astro_params: :class:`~AstroParams` instance, optional
-        The astrophysical parameters defining the course of reionization.
-    flag_options: :class:`FlagOptions` instance, optional
-        The flag options enabling/disabling extra modules in the simulation.
-    cleanup : bool, optional
-        A flag to specify whether the C routine cleans up its memory before returning.
-        Typically, if `spin_temperature` is called directly, you will want this to be
-        true, as if the next box to be calculate has different shape, errors will occur
-        if memory is not cleaned. However, it can be useful to set it to False if
-        scrolling through parameters for the same box shape.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
-
-    Returns
-    -------
-    :class:`~TsBox`
-        An object containing the spin temperature box data.
-
-    Other Parameters
-    ----------------
-    regenerate, write, direc, random_seed :
-        See docs of :func:`initial_conditions` for more information.
-
-    Notes
-    -----
-    Typically, the spin temperature field at any redshift is dependent on the evolution of spin
-    temperature up until that redshift, which necessitates providing a previous spin temperature
-    field to define the current one. Either a specific previous spin temperature object is provided,
-    or the function is instructed to treat the current field as being an initial "high-redshift"
-    field such that specific sources need not be found and evolved.:
-
-    Examples
-    --------
-    To calculate and return a fully evolved spin temperature field at a given redshift (with
-    default input parameters), simply use:
-
-    >>> ts_box = spin_temperature(redshift=7.0)
-
-    This will by default evolve the field from a redshift of *at least* `Z_HEAT_MAX` (a global
-    parameter), in logarithmic steps of `z_step_factor`. Thus to change these:
-
-    >>> ts_box = spin_temperature(redshift=7.0, zprime_step_factor=1.2, z_heat_max=15.0)
-
-    Alternatively, one can pass an exact previous redshift, which will be sought in the disk
-    cache, or evaluated:
-
-    >>> ts_box = spin_temperature(redshift=7.0, previous_spin_temp=8.0)
-
-    Beware that doing this, if the previous box is not found on disk, will continue to evaluate
-    prior boxes based on the ``z_step_factor``. Alternatively, one can pass a previous spin
-    temperature box:
-
-    >>> ts_box1 = spin_temperature(redshift=8.0)
-    >>> ts_box = spin_temperature(redshift=7.0, previous_spin_temp=ts_box1)
-
-    Again, the first line here will implicitly use ``z_step_factor`` to evolve the field from
-    around ``Z_HEAT_MAX``. Note that in the second line, all of the input parameters are taken
-    directly from `ts_box1` so that they are consistent. Finally, one can force the function to
-    evaluate the current redshift as if it was beyond ``Z_HEAT_MAX`` so that it depends only on
-    itself:
-
-    >>> ts_box = spin_temperature(redshift=7.0, zprime_step_factor=None)
-
-    This is usually a bad idea, and will give a warning, but it is possible.
-    """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
-    # Configure and check input/output parameters/structs
-    inputs.check_output_compatibility(
-        (initial_conditions, perturbed_field, previous_spin_temp, xray_source_box),
-    )
-    redshift = perturbed_field.redshift
-    check_redshift_consistency(redshift, (perturbed_field, xray_source_box))
-
-    # Get the previous redshift
-    if previous_spin_temp is not None:
-        prev_z = previous_spin_temp.redshift
-    elif redshift < inputs.user_params.Z_HEAT_MAX:
-        raise ValueError(
-            "previous_spin_temp is required when the redshift is lower than Z_HEAT_MAX"
-        )
-    else:
-        # Set prev_z to anything, since we don't need it.
-        prev_z = 300  # needs to be castable to float type
-
-    # Ensure the previous spin temperature has a higher redshift than this one.
-    if prev_z <= redshift:
-        raise ValueError(
-            "Previous spin temperature box must have a higher redshift than "
-            "that being evaluated."
-        )
-
-    if xray_source_box is None:
-        if inputs.flag_options.USE_HALO_FIELD:
-            raise ValueError("xray_source_box is required when USE_HALO_FIELD is True")
-        else:
-            xray_source_box = XraySourceBox(
-                redshift=0.0,
-                inputs=inputs,
-                dummy=True,
-            )
-
-    # Set up the box without computing anything.
-    box = TsBox(
-        redshift=redshift,
-        inputs=inputs,
-        prev_spin_redshift=prev_z,
-    )
-
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
-
-    # Check whether the boxes already exist on disk.
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            box.read(direc)
-            logger.info(
-                f"Existing z={redshift} spin_temp boxes found and read in "
-                f"(seed={box.random_seed})."
-            )
-            return box
-
-    # Create appropriate previous_spin_temp. We've already checked that if it is None,
-    # we're above the Z_HEAT_MAX.
-    if previous_spin_temp is None:
-        # We end up never even using this box, just need to define it
-        # unallocated to be able to send into the C code.
-        previous_spin_temp = TsBox(
-            redshift=0.0,
-            inputs=inputs,
-            dummy=True,
-        )
-
-    # Run the C Code
-    return box.compute(
-        cleanup=cleanup,
-        perturbed_field=perturbed_field,
-        xray_source_box=xray_source_box,
-        prev_spin_temp=previous_spin_temp,
-        ics=initial_conditions,
-        hooks=hooks,
-    )
-
-
-@set_globals
+@single_field_func
 def brightness_temperature(
     *,
-    inputs: InputParameters,
     ionized_box: IonizedBox,
     perturbed_field: PerturbedField,
     spin_temp: TsBox | None = None,
-    write=None,
-    regenerate: bool | None = None,
-    direc=None,
-    hooks=None,
-    **global_kwargs,
 ) -> BrightnessTemp:
     r"""
     Compute a coeval brightness temperature box.
@@ -1222,22 +667,13 @@ def brightness_temperature(
         A pre-computed perturbed field at the same redshift as `ionized_box`.
     spin_temp: :class:`TsBox`, optional
         A pre-computed spin temperature, at the same redshift as the other boxes.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Returns
     -------
     :class:`BrightnessTemp` instance.
     """
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, write, hooks)
-
-    inputs.check_output_compatibility(
-        (ionized_box, perturbed_field, spin_temp),
-    )
     redshift = ionized_box.redshift
-    check_redshift_consistency(redshift, (ionized_box, perturbed_field, spin_temp))
+    inputs = ionized_box.inputs
 
     if spin_temp is None:
         if inputs.flag_options.USE_TS_FLUCT:
@@ -1245,34 +681,12 @@ def brightness_temperature(
                 "You have USE_TS_FLUCT=True, but have not provided a spin_temp!"
             )
         else:
-            spin_temp = TsBox(
-                redshift=0.0,
-                inputs=inputs,
-                dummy=True,
-            )
+            spin_temp = TsBox.dummy()
 
-    box = BrightnessTemp(
-        redshift=redshift,
-        inputs=inputs,
-    )
-
-    # Construct FFTW wisdoms. Only if required
-    construct_fftw_wisdoms(
-        user_params=inputs.user_params, cosmo_params=inputs.cosmo_params
-    )
-
-    # Check whether the boxes already exist on disk.
-    if not regenerate:
-        with contextlib.suppress(OSError):
-            box.read(direc)
-            logger.info(
-                f"Existing brightness_temp box found and read in (seed={box.random_seed})."
-            )
-            return box
+    box = BrightnessTemp.new(redshift=redshift, inputs=inputs)
 
     return box.compute(
         spin_temp=spin_temp,
         ionized_box=ionized_box,
         perturbed_field=perturbed_field,
-        hooks=hooks,
     )
diff --git a/src/py21cmfast/io/__init__.py b/src/py21cmfast/io/__init__.py
new file mode 100644
index 000000000..3a07d54af
--- /dev/null
+++ b/src/py21cmfast/io/__init__.py
@@ -0,0 +1 @@
+"""I/O for the 21cmFAST package."""
diff --git a/src/py21cmfast/io/caching.py b/src/py21cmfast/io/caching.py
new file mode 100644
index 000000000..affdb5ff9
--- /dev/null
+++ b/src/py21cmfast/io/caching.py
@@ -0,0 +1,499 @@
+"""Module to deal with the cache.
+
+The module has a manager that essentially establishes a database of cached files,
+and provides methods to handle the caching of output data (i.e. determining the
+filename for a given set of parameters).
+"""
+
+import attrs
+import numpy as np
+import re
+from pathlib import Path
+from typing import Self
+
+from .._cfg import config
+from ..wrapper.inputs import InputParameters
+from ..wrapper.outputs import OutputStruct
+from .h5 import read_inputs, read_output_struct, write_output_to_hdf5
+
+
+@attrs.define(frozen=True)
+class OutputCache:
+    """An object that manages cache files from 21cmFAST simulations.
+
+    This object has a single attribute -- the top-level directory of the cache. This
+    directory can be anywhere on disk. A number of methods exist on the object to
+    interact with the cache, including finding existing cache files for a particular
+    OutputStruct, writing/reading an OutputStruct to/from the cache, and listing
+    existing datasets.
+
+    The cache is meant for single-field OutputStruct objects, not "collections" of
+    outputs in an evolved universe (like Coeval or Lightcone objects).
+    """
+
+    direc: Path = attrs.field(
+        default=Path(config["direc"]).expanduser(), converter=Path
+    )
+
+    _path_structures = {
+        "InitialConditions": "{user_cosmo}/{seed}/InitialConditions.h5",
+        "PerturbedField": "{user_cosmo}/{seed}/{zgrid}/{redshift}/PerturbedField.h5",
+        "other": "{user_cosmo}/{seed}/{zgrid}/{redshift}/{astro_flag}/{cls}.h5",
+    }
+
+    @classmethod
+    def _get_hashes(cls, inputs: InputParameters) -> dict[str, str]:
+        """Return a dict of hashes for different components of the calculation."""
+        return {
+            "user_cosmo": hash((inputs.cosmo_params, inputs.user_params)),
+            "seed": inputs.random_seed,
+            "zgrid": hash(inputs.node_redshifts),
+            "astro_flag": hash((inputs.astro_params, inputs.user_params)),
+        }
+
+    def get_filename(self, obj: OutputStruct) -> str:
+        """
+        Generate a filename for a given OutputStruct object based on its properties.
+
+        This method constructs a unique filename using the object's class name, redshift
+        (if available), and hashes of its input parameters. The filename structure is
+        determined by the _path_structures dictionary.
+
+        Parameters
+        ----------
+        obj : OutputStruct
+            The OutputStruct object for which to generate a filename.
+
+        Returns
+        -------
+        str
+            The generated filename for the given OutputStruct object.
+        """
+        hashes = self._get_hashes(obj.inputs)
+        redshift = getattr(obj, "redshift", None)
+        kls = obj.__class__.__name__
+
+        pth = self._path_structures.get(kls, self._path_structures["other"])
+        return pth.format(redshift=redshift, cls=kls, **hashes)
+
+    def get_path(self, obj: OutputStruct) -> Path:
+        """
+        Get the full path for a given OutputStruct object.
+
+        This method combines the cache directory with the filename generated
+        for the given OutputStruct object to create a complete file path.
+
+        Parameters
+        ----------
+        obj : OutputStruct
+            The OutputStruct object for which to generate the full path.
+
+        Returns
+        -------
+        Path
+            The complete file path for the given OutputStruct object.
+        """
+        return self.direc / self.get_filename(obj)
+
+    def find_existing(self, obj: OutputStruct) -> Path | None:
+        """
+        Try to find existing boxes which match the parameters of this instance.
+
+        Parameters
+        ----------
+        obj : OutputStruct
+            The OutputStruct instance to search for.
+
+        Returns
+        -------
+        Path
+            The path to an existing cached OutputStruct matching this instance, or
+            None if no match is found.
+        """
+        # Try an explicit path
+        f = self.get_path(obj)
+        return f if f.exists() else None
+
+    def write(self, obj: OutputStruct) -> None:
+        """
+        Write an OutputStruct object to the cache.
+
+        This method writes the given OutputStruct object to an HDF5 file in the cache,
+        using the path determined by the object's properties.
+
+        Parameters
+        ----------
+        obj : OutputStruct
+            The OutputStruct object to be written to the cache.
+        """
+        pth = self.get_path(obj)
+        write_output_to_hdf5(obj, path=pth)
+
+    def list_datasets(
+        self,
+        *,
+        kind: str | None = None,
+        inputs: InputParameters | None = None,
+        all_seeds: bool = True,
+        redshift: float | None = None,
+    ) -> list[Path]:
+        """Return all datasets in the cache which match a given set of filters.
+
+        Parameters
+        ----------
+        kind: str, optional
+            Filter by this kind (a class name of an OutputStruct).
+        inputs : InputParameters
+            Filter by these input parameters
+        all_seeds
+            Set to False to only include the seed within `inputs`.
+        redshift
+            The redshift to search for.
+
+        Returns
+        -------
+        files
+            list of paths pointing to files matching the filters.
+        """
+        if inputs is not None:
+            hashes = self._get_hashes(inputs)
+        else:
+            hashes = {
+                "user_cosmo": ".+?",
+                "seed": r"\d+",
+                "zgrid": ".+?",
+                "astro_flag": ".+?",
+            }
+
+        if all_seeds:
+            hashes["seed"] = r"\d+"
+
+        hashes["redshift"] = str(redshift) if redshift is not None else ".+?"
+
+        allfiles = self.direc.glob("**/*")
+        template = self._path_structures.get(kind, self._path_structures["other"])
+        template = template.format(**hashes)
+        matches = []
+        for fl in allfiles:
+            match = re.search(template, fl.name)
+            if match is not None:
+                matches.append(match)
+
+        return matches
+
+    def load(self, obj: OutputStruct) -> OutputStruct:
+        """Load a cache-backed object from disk corresponding to a given object."""
+        existing = self.find_existing(obj)
+
+        if existing is None:
+            raise OSError(f"No cache exists for {obj} yet!")
+
+        return read_output_struct(existing, struct=obj.__class__.__name__)
+
+
+def _pathfield():
+    return attrs.field(
+        default=None,
+        converter=attrs.converters.optional(Path),
+    )
+
+
+def _dict_of_paths_field():
+    def _convert(x: dict | None) -> tuple[Path]:
+        if x is None:
+            return x
+
+        if isinstance(x, dict):
+            return {float(z): Path(d) for z, d in x.items()}
+
+    return attrs.field(
+        default=None,
+        converter=_convert,
+    )
+
+
+@attrs.define
+class RunCache:
+    """An object that specifies all cache files that should/can exist for a full run.
+
+    This object should be instantiated via the `.from_inputs()` class method.
+
+    The instance simply holds references to all possible cache files for a particular
+    total simulation (including all evolution over redshift). Not all of these files
+    might exist: if a file doesn't exist it implies that the simulation has not run
+    for that redshift/field yet. Attributes with values of None are not meant to exist
+    as part of the simulation (e.g. they may be TsBox instances when USE_TS_FLUCT=False).
+    """
+
+    InitialConditions: Path = _pathfield()
+    PerturbedField: tuple[Path] = _dict_of_paths_field()
+    TsBox: tuple[Path] = _dict_of_paths_field()
+    IonizedBox: tuple[Path] = _dict_of_paths_field()
+    BrightnessTemp: tuple[Path] = _dict_of_paths_field()
+    HaloBox: tuple[Path] = _dict_of_paths_field()
+    PerturbHaloField: tuple[Path] = _dict_of_paths_field()
+    XraySourceBox: tuple[Path] = _dict_of_paths_field()
+    HaloBox: tuple[Path] = _dict_of_paths_field()
+    inputs: InputParameters | None = attrs.field(default=None)
+
+    @classmethod
+    def from_inputs(cls, inputs: InputParameters, cache: OutputCache) -> Self:
+        """
+        Create a RunCache instance from input parameters and an OutputCache.
+
+        This method generates file paths for various output structures based on the
+        provided input parameters and cache configuration.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters for the simulation.
+        cache : OutputCache
+            The output cache object containing directory and path structure information.
+
+        Returns
+        -------
+        RunCache
+            A new RunCache instance with file paths for various output structures.
+        """
+        hashes = cache._get_hashes(inputs)
+        ics = cache.direc / cache._path_structures["InitialConditions"].format(**hashes)
+        pfs = {}
+
+        others = {
+            "IonizedBox": {},
+            "BrightnessTemp": {},
+        }
+        if inputs.flag_options.USE_TS_FLUCT:
+            others |= {"TsBox": {}}
+        if inputs.flag_options.USE_HALO_FIELD:
+            others |= {"PerturbHaloField": {}, "XraySourceBox": {}, "HaloBox": {}}
+
+        for z in inputs.node_redshifts:
+            pfs[z] = cache.direc / cache._path_structures["PerturbedField"].format(
+                redshift=z, **hashes
+            )
+
+            for name, val in others.items():
+                val[z] = cache.direc / cache._path_structures["other"].format(
+                    redshift=z, cls=name, **hashes
+                )
+
+        return cls(
+            InitialConditions=ics,
+            PerturbedField=pfs,
+            **others,
+            inputs=inputs,
+        )
+
+    @classmethod
+    def from_example_file(cls, path: Path | str) -> Self:
+        """Create a RunCache object from an example file.
+
+        This method can be used to determine all the cache files that make up a full
+        simulation, given a single example file. Note that this method is somewhat
+        ambiguous when the input file is "high up" in the simulation heirarchy (e.g.
+        InitialConditions or PerturbedField) because the input parameters to these
+        objects may differ from those of the full simulation, in their astro_params
+        and flag_options. For this reason, it is better to supply a cache object like
+        IonizedBox or BrightnessTemp.
+
+        Parameters
+        ----------
+        path : Path | str
+            The path to a particular file in cache. The returned OutputCache object
+            will include this file.
+        """
+        inputs = read_inputs(Path(path))
+        hashes = OutputCache._get_hashes(inputs)
+        hashes["redshift"] = ".+?"
+        hashes["cls"] = ".+?"
+        for template in OutputCache._path_structures.values():
+            template = template.format(**hashes)
+            match = re.search(template, str(path))
+            if match is not None:
+                parent = Path(str(path)[: match.start])
+                break
+        else:
+            raise ValueError(
+                f"The file {path} does not seem to be within a cache structure."
+            )
+
+        return cls.from_inputs(inputs, OutputCache(parent))
+
+    def is_complete_at(
+        self, z: float | None = None, index: float | None = None
+    ) -> bool:
+        """Determine whether the simulation has been completed down to a given redshift."""
+        if index is not None and z is not None:
+            raise ValueError("Cannot specify both z and index")
+        if index is not None:
+            z = self.inputs.node_redshifts[index]
+
+        for kind in attrs.asdict(self, recurse=False).values():
+            if not isinstance(kind, dict):
+                continue
+
+            if not kind[z].exists():
+                return False
+
+    def get_output_struct_at_z(
+        self,
+        kind: type[OutputStruct] | str,
+        z: float | None = None,
+        index: int | None = None,
+        match_z_within: float = 0.01,
+    ):
+        """Return an output struct of a given kind at or close to a given redshift.
+
+        Parameters
+        ----------
+        z : float
+            The redshift at which to return an output struct.
+        index : int
+            The node-redshift index at which to return the output struct.
+        allow_closest : bool
+            Whether to allow the closest redshift available in the cache to be returned.
+
+        Returns
+        -------
+        OutputStruct
+            The output struct corresponding to the kind and redshift.
+        """
+        if not isinstance(kind, str):
+            kind = kind.__name__
+        if kind not in attrs.fields_dict(kind):
+            raise ValueError(f"Unknown output kind: {kind}")
+        if index is not None:
+            if z is not None:
+                raise ValueError("Cannot specify both z and index")
+            z = self.inputs.node_redshifts[index]
+
+        zs_of_kind = list(getattr(self, kind).keys())
+        if z not in zs_of_kind:
+            closest = np.argmin(np.abs(zs_of_kind - z))
+            if abs(closest - z) > match_z_within:
+                raise ValueError(
+                    f"No output struct found for kind '{kind}' at redshift {z} (closest available: {zs_of_kind[closest]} at z={closest})"
+                )
+            z = closest
+
+        fl = getattr(self, kind)[z]
+        return read_output_struct(fl)
+
+    def get_all_boxes_at_z(
+        self,
+        z: float | None,
+        index: int | None,
+        match_z_within: float = 0.01,
+    ) -> dict[str, OutputStruct]:
+        """Return all boxes at or close to a given redshift.
+
+        Parameters
+        ----------
+        z : float
+            The redshift at which to return the boxes.
+        index : int
+            The node-redshift index at which to return the boxes.
+        allow_closest : bool
+            Whether to allow the closest redshift available in the cache to be returned.
+
+        Returns
+        -------
+        dict[str, Box]
+            A dictionary mapping box names to their corresponding Box instances.
+        """
+        kinds = [
+            k
+            for k, v in attrs.asdict(self, recurse=False).items()
+            if isinstance(v, dict)
+        ]
+        return {
+            k: self.get_output_struct_at_z(k, z, index, match_z_within) for k in kinds
+        }
+
+    def is_complete(self) -> bool:
+        """Whether the cache for the full simulation is complete."""
+        if not self.InitialConditions.exists():
+            return False
+
+        for kind in attrs.asdict(self, recurse=False).values():
+            if not isinstance(kind, dict):
+                continue
+
+            for fl in kind.values():
+                if not fl.exists():
+                    return False
+
+    # def get_completed_redshift(self) -> tuple[float, int]:
+    #     """Obtain the redshift down to which the cache is complete."""
+    #     if not self.InitialConditions.exists():
+    #         return None, -1
+
+    #     zgrid_files = {
+    #         k: v
+    #         for k, v in attrs.asdict(self, recurse=False).items()
+    #         if isinstance(v, dict)
+    #     }
+
+    #     for i, z in enumerate(self.inputs.node_redshifts):
+    #         for file_dict in zgrid_files.values():
+    #             if z not in file_dict:
+    #                 return self.inputs.node_redshifts[i - 1], i - 1
+
+    #     return self.inputs.node_redshifts[-1], len(self.inputs.node_redshifts) - 1
+
+    # def is_partial(self):
+    #     """Whether the cache is complete down to some redshift, but not the last z."""
+    #     z, idx = self.get_completed_redshift()
+    #     return idx == len(self.inputs.node_redshifts) - 1
+
+
+@attrs.define
+class CacheConfig:
+    """A configuration object that specifies whether a certain field should be cached."""
+
+    initial_conditions: bool = attrs.field(default=True, converter=bool)
+    perturbed_field: bool = attrs.field(default=True, converter=bool)
+    spin_temp: bool = attrs.field(default=True, converter=bool)
+    ionized_box: bool = attrs.field(default=True, converter=bool)
+    brightness_temp: bool = attrs.field(default=True, converter=bool)
+    halobox: bool = attrs.field(default=True, converter=bool)
+    perturbed_halo_field = attrs.field(default=True, converter=bool)
+    halo_field = attrs.field(default=True, converter=bool)
+    xray_source_box = attrs.field(default=True, converter=bool)
+
+    @classmethod
+    def on(cls) -> Self:
+        """Generate a CacheConfig where all boxes are cached."""
+        return cls()
+
+    @classmethod
+    def off(cls):
+        """Generate a CacheConfig where no boxes are cached."""
+        return cls(
+            initial_conditions=False,
+            perturbed_field=False,
+            spin_temp=False,
+            ionized_box=False,
+            brightness_temp=False,
+            halobox=False,
+            perturbed_halo_field=False,
+            halo_field=False,
+            xray_source_box=False,
+        )
+
+    @classmethod
+    def noloop(cls):
+        """Generate a CacheConfig where only boxes not requiring evolution are cached."""
+        return cls(
+            initial_conditions=True,
+            perturbed_field=True,
+            spin_temp=False,
+            ionized_box=False,
+            brightness_temp=False,
+            halobox=False,
+            perturbed_halo_field=False,
+            halo_field=False,
+            xray_source_box=False,
+        )
diff --git a/src/py21cmfast/io/h5.py b/src/py21cmfast/io/h5.py
new file mode 100644
index 000000000..01dfa9c38
--- /dev/null
+++ b/src/py21cmfast/io/h5.py
@@ -0,0 +1,380 @@
+"""Module defining HDF5 backends for reading/writing output structures.
+
+These functions are those used by default in the caching system of 21cmFAST.
+In the future, it is possible that other backends might be implemented.
+
+As of version 4, all cache files from 21cmFAST will have the following heirarchical
+structure::
+
+    /attrs/
+      |-- 21cmFAST-version
+      |-- [redshift]
+    /<OutputStructName>/
+      /InputParameters/
+        /attrs/
+          |-- 21cmFAST-version
+          |-- random_seed
+        /user_params/
+        /cosmo_params/
+        /flag_options/
+        /astro_params/
+        /node_redshifts/
+      /OutputFields/
+        /attrs/
+          |-- [primitive_field_1]
+          |-- [primitive_field_2]
+          |-- [...]
+        /[field_1]/
+        /[field_2]/
+        /.../
+
+"""
+
+import attrs
+import h5py
+import numpy as np
+import warnings
+from pathlib import Path
+
+from .. import __version__
+from ..wrapper import inputs as istruct
+from ..wrapper import outputs as ostruct
+from ..wrapper._utils import snake_to_camel
+from ..wrapper.arrays import Array, H5Backend
+from ..wrapper.arraystate import ArrayState
+from ..wrapper.inputs import InputParameters
+
+
+def write_output_to_hdf5(
+    output: ostruct.OutputStruct,
+    path: Path,
+    group: str | None = None,
+    mode: str = "w",
+):
+    """
+    Write an output struct in standard HDF5 format.
+
+    Parameters
+    ----------
+    output
+        The OutputStruct to write.
+    path : Path
+        The path to write the output struct to.
+    group : str, optional
+        The HDF5 group into which to write the object. By default, this is the root.
+    mode : str
+        The mode in which to open the file.
+    """
+    if not all(v.state.is_computed for v in output.arrays.values()):
+        raise OSError(
+            "Not all boxes have been computed (or maybe some have been purged). Cannot write."
+            f"Non-computed boxes: {[k for k, v in output.arrays.items() if not v.state.is_computed]}. "
+            f"Computed boxes: {[k for k, v in output.arrays.items() if v.state.is_computed]}"
+        )
+
+    path = Path(path)
+    if not path.parent.exists():
+        path.parent.mkdir(exist_ok=True, parents=True)
+
+    with h5py.File(path, mode) as fl:
+        if group is not None:
+            if group in fl:
+                group = fl[group]
+            else:
+                group = fl.create_group(group)
+        else:
+            group = fl
+
+        group.attrs["21cmFAST-version"] = __version__
+        group = group.create_group(output._name)
+
+        if hasattr(output, "redshift"):
+            group.attrs["redshift"] = output.redshift
+
+        write_outputs_to_group(output, group)
+        _write_inputs_to_group(output.inputs, group)
+
+
+def write_input_struct(struct, fl: h5py.File | h5py.Group) -> None:
+    """Write a particular input struct (e.g. UserParams) to an HDF5 file."""
+    dct = struct.asdict()
+
+    for kk, v in dct.items():
+        try:
+            fl.attrs[kk] = "none" if v is None else v
+        except TypeError as e:
+            raise TypeError(
+                f"key {kk} with value {v} is not able to be written to HDF5 attrs!"
+            ) from e
+
+
+def _write_inputs_to_group(
+    inputs: InputParameters, group: h5py.Group | h5py.File | str | Path
+) -> None:
+    """Write an InputParameters object into a cache file.
+
+    Here we are careful to close the file only if a raw Path is given, and keep it open
+    if a h5py.File/Group is given (since then this is likely being called from another
+    function that is also writing other objects to the same file).
+
+    Parameters
+    ----------
+    inputs
+        The input parameters object to write.
+    group : h5py.Group | h5py.File | str | Path
+        The group or file into which to write the inputs. Note that a new group called
+        "InputParameters" will be created inside this group/file.
+    """
+    must_close = False
+    if isinstance(group, str | Path):
+        file = h5py.File(group, "a")
+        group = file
+        must_close = True
+
+    grp = group.create_group("InputParameters")
+
+    # Write 21cmFAST version to the file
+    grp.attrs["21cmFAST-version"] = __version__
+
+    write_input_struct(inputs.user_params, grp.create_group("user_params"))
+    write_input_struct(inputs.cosmo_params, grp.create_group("cosmo_params"))
+    write_input_struct(inputs.astro_params, grp.create_group("astro_params"))
+    write_input_struct(inputs.flag_options, grp.create_group("flag_options"))
+
+    grp.attrs["random_seed"] = inputs.random_seed
+    grp["node_redshifts"] = (
+        h5py.Empty(None)
+        if inputs.node_redshifts is None
+        else np.array(inputs.node_redshifts)
+    )
+
+    if must_close:
+        file.close()
+
+
+def write_outputs_to_group(
+    output: ostruct.OutputStruct, group: h5py.Group | h5py.File | str | Path
+):
+    """
+    Write the compute fields of an OutputStruct to a particular HDF5 subgroup.
+
+    Here we are careful to close the file only if a raw Path is given, and keep it open
+    if a h5py.File/Group is given (since then this is likely being called from another
+    function that is also writing other objects to the same file).
+
+    Parameters
+    ----------
+    output
+        The OutputStruct to write.
+    group
+        The HDF5 group into which to write the object. A new group "OutputFields" will
+        be created inside this group/file.
+    """
+    need_to_close = False
+    if isinstance(group, str | Path):
+        file = h5py.File(group, "r")
+        group = file
+        need_to_close = True
+
+    # Go through all fields in this struct, and save
+    group = group.create_group("OutputFields")
+
+    # First make sure we have everything in memory
+    output.load_all()
+
+    for k, array in output.arrays.items():
+        new = array.written_to_disk(H5Backend(group.file.filename, f"{group.name}/{k}"))
+        setattr(output, k, new)
+
+    for k in output.struct.primitive_fields:
+        group.attrs[k] = getattr(output, k)
+
+    group.attrs["21cmFAST-version"] = __version__
+
+    if need_to_close:
+        file.close()
+
+
+def read_output_struct(
+    path: Path, group: str = "/", struct: str | None = None, safe: bool = True
+) -> ostruct.OutputStruct:
+    """
+    Read an output struct from an HDF5 file.
+
+    Parameters
+    ----------
+    path : Path
+        The path to the HDF5 file.
+    group : str, optional
+        A path within the HDF5 heirarchy to the top-level of the OutputStruct. This is
+        usually the root of the file.
+    struct
+        A string specifying the kind of OutputStruct to read (e.g. InitialConditions).
+        Generally, this does not need to be provided, as cache files contain just a
+        single output struct.
+    safe
+        Whether to read the file in "safe" mode. If True, keys found in the file that
+        are not valid attributes of the struct will raise an exception. If False, only
+        a warning will be raised.
+
+    Returns
+    -------
+    OutputStruct
+        An OutputStruct that is contained in the cache file.
+    """
+    with h5py.File(path, "r") as fl:
+        group = fl[group]
+
+        if struct is not None and struct in group:
+            group = group[struct]
+        elif len(group.keys()) > 1:
+            raise ValueError(f"Multiple structs found in {path}:{group}")
+        else:
+            struct = list(group.keys())[0]
+            group = group[struct]
+
+        assert "InputParameters" in group
+        assert "OutputFields" in group
+
+        redshift = group.attrs.get("redshift")
+        inputs = read_inputs(group["InputParameters"], safe=safe)
+        outputs = _read_outputs(group["OutputFields"])
+
+    if redshift is not None:
+        outputs["redshift"] = redshift
+    kls = getattr(ostruct, struct)
+    out = kls(inputs=inputs, **outputs)
+    out.sync()  # maybe we shouldn't do this
+    return out
+
+
+def read_inputs(
+    group: h5py.Group | Path | h5py.File, safe: bool = True
+) -> InputParameters:
+    """Read the InputParameters from a cache file.
+
+    Parameters
+    ----------
+    group : h5py.Group | Path | h5py.File
+        A file, or HDF5 Group within a file, to read the input parameters from.
+    safe : bool, optional
+        If in safe mode, errors will be raised if keys exist in the file that are not
+        valid attributes of the InputParameters. Otherwise, only warnings will be raised.
+
+    Returns
+    -------
+    inputs : InputParameters
+        The input parameters contained in the file.
+    """
+    close_after = False
+    if isinstance(group, Path):
+        file = h5py.File(group, "r")
+        group = file["InputParameters"]
+        close_after = True
+    elif isinstance(group, h5py.File):
+        group = group["InputParameters"]
+
+    file_version = group.attrs.get("21cmFAST-version", None)
+    if file_version > __version__:
+        warnings.warn(
+            f"File created with a newer version {file_version} of 21cmFAST than this {__version__}. "
+            f"Reading may break. Consider updating 21cmFAST."
+        )
+
+    if file_version is None:
+        # pre-v4 file
+        out = _read_inputs_pre_v4(group, safe=safe)
+    else:
+        out = _read_inputs_v4(group, safe=safe)
+
+    if close_after:
+        file.close()
+
+    return out
+
+
+def _read_inputs_pre_v4(group: h5py.Group, safe: bool = True):
+
+    input_classes = [
+        istruct.UserParams,
+        istruct.CosmoParams,
+        istruct.AstroParams,
+        istruct.FlagOptions,
+    ]
+    input_class_names = [cls.__name__ for cls in input_classes]
+
+    # Read the input parameter dictionaries from file.
+    kwargs = {}
+    for k in attrs.fields_dict(InputParameters):
+        kfile = k.lstrip("_")
+        input_class_name = snake_to_camel(kfile)
+
+        if input_class_name in input_class_names:
+            kls = input_classes[input_class_names.index(input_class_name)]
+
+            subgrp = group[kfile]
+            dct = dict(subgrp.attrs)
+            kwargs[k] = kls.from_subdict(dct, safe=safe)
+        else:
+            kwargs[k] = group.attrs[kfile]
+    return InputParameters(**kwargs)
+
+
+def _read_inputs_v4(group: h5py.Group, safe: bool = True):
+    # Read the input parameter dictionaries from file.
+    kwargs = {}
+    for k, fld in attrs.fields_dict(InputParameters).items():
+        if fld.type in istruct.InputStruct._subclasses:
+            kls = istruct.InputStruct._subclasses[fld.type]
+
+            subgrp = group[k]
+            dct = dict(subgrp.attrs)
+            kwargs[k] = kls.from_subdict(dct, safe=safe)
+        elif k in group.attrs:
+            kwargs[k] = group.attrs[k]
+        else:
+            d = group[k][()]
+            if d is h5py.Empty(None):
+                kwargs[k] = None
+            else:
+                kwargs[k] = d
+
+    return InputParameters(**kwargs)
+
+
+def _read_outputs(group: h5py.Group):
+    file_version = group.attrs.get("21cmFAST-version", None)
+
+    if file_version > __version__:
+        warnings.warn(
+            f"File created with a newer version of 21cmFAST than this. Reading may break. Consider updating 21cmFAST to at least {file_version}"
+        )
+    if file_version is None:
+        # pre-v4 file
+        return _read_outputs_pre_v4(group)
+    else:
+        return _read_outputs_v4(group)
+
+
+def _read_outputs_pre_v4(group: h5py.Group):
+    arrays = {
+        name: Array(
+            dtype=box.dtype,
+            shape=box.shape,
+            state=ArrayState(on_disk=True),
+            cache_backend=H5Backend(path=group.file.filename, dataset=box.name),
+        )
+        for name, box in group.items()
+    }
+    for k, val in group.attrs.items():
+        if k == "21cmFAST-version":
+            continue
+
+        arrays[k] = val
+
+    return arrays
+
+
+def _read_outputs_v4(group: h5py.Group):
+    # I actually think the reader is the same in v4.
+    return _read_outputs_pre_v4(group)
diff --git a/src/py21cmfast/plotting.py b/src/py21cmfast/plotting.py
index 08f26bb55..091f21ebb 100644
--- a/src/py21cmfast/plotting.py
+++ b/src/py21cmfast/plotting.py
@@ -168,17 +168,15 @@ def coeval_sliceplot(
     and the `imshow_kw` argument, which allows arbitrary styling of the plot.
     """
     if kind is None:
-        if isinstance(struct, outputs._OutputStruct):
+        if isinstance(struct, outputs.OutputStruct):
             kind = struct.struct.fieldnames[0]
         elif isinstance(struct, Coeval):
             kind = "brightness_temp"
 
-    try:
+    if isinstance(struct, outputs.OutputStruct):
+        cube = struct.get(kind)
+    elif isinstance(struct, Coeval):
         cube = getattr(struct, kind)
-    except AttributeError:
-        raise AttributeError(
-            f"The given OutputStruct does not have the quantity {kind}"
-        )
 
     if kind != "brightness_temp" and "cmap" not in kwargs:
         kwargs["cmap"] = "viridis"
@@ -327,7 +325,7 @@ def lightcone_sliceplot(
     cbar_horizontal = kwargs.pop("cbar_horizontal", not vertical)
     if lightcone2 is None:
         fig, ax = _imshow_slice(
-            getattr(lightcone, kind)[:, :, plot_sel],
+            lightcone.lightcones[kind][:, :, plot_sel],
             extent=extent,
             slice_axis=slice_axis,
             rotate=not vertical,
@@ -338,7 +336,7 @@ def lightcone_sliceplot(
             **kwargs,
         )
     else:
-        d = (getattr(lightcone, kind)[:, :, plot_sel] - getattr(lightcone2, kind))[
+        d = (lightcone.lightcones[kind][:, :, plot_sel] - getattr(lightcone2, kind))[
             :, :, plot_sel
         ]
         fig, ax = _imshow_slice(
diff --git a/src/py21cmfast/run_templates.py b/src/py21cmfast/run_templates.py
index 46deb9f1a..3b3375b6d 100644
--- a/src/py21cmfast/run_templates.py
+++ b/src/py21cmfast/run_templates.py
@@ -16,8 +16,7 @@
 from pathlib import Path
 
 from .wrapper._utils import camel_to_snake
-from .wrapper.inputs import AstroParams, CosmoParams, FlagOptions, UserParams
-from .wrapper.structs import InputStruct
+from .wrapper.inputs import InputStruct
 
 TEMPLATE_PATH = Path(__file__).parent / "templates/"
 
@@ -43,6 +42,13 @@ def _construct_param_objects(template_dict, **kwargs):
     return input_dict
 
 
+def list_templates() -> list[dict]:
+    """Return a list of the available templates."""
+    with open(TEMPLATE_PATH / "manifest.toml", "rb") as f:
+        manifest = tomllib.load(f)
+    return manifest["templates"]
+
+
 def create_params_from_template(template_name: str, **kwargs):
     """
     Constructs the required InputStruct instances for a run from a given template.
diff --git a/src/py21cmfast/src/InitialConditions.c b/src/py21cmfast/src/InitialConditions.c
index fab923e3d..c9b308e3a 100644
--- a/src/py21cmfast/src/InitialConditions.c
+++ b/src/py21cmfast/src/InitialConditions.c
@@ -128,7 +128,7 @@ int ComputeInitialConditions(
     f_pixel_factor = user_params->DIM/(float)user_params->HII_DIM;
 
     // ************  END INITIALIZATION ****************** //
-    LOG_DEBUG("Finished initialization.");
+    LOG_SUPER_DEBUG("Finished initialization.");
     // ************ CREATE K-SPACE GAUSSIAN RANDOM FIELD *********** //
 
     init_ps();
@@ -179,7 +179,7 @@ int ComputeInitialConditions(
             }
         }
     }
-    LOG_DEBUG("Drawn random fields.");
+    LOG_SUPER_DEBUG("Drawn random fields.");
 
     // *****  Adjust the complex conjugate relations for a real array  ***** //
     adj_complex_conj(HIRES_box,user_params,cosmo_params);
@@ -189,7 +189,7 @@ int ComputeInitialConditions(
     // FFT back to real space
     int stat = dft_c2r_cube(user_params->USE_FFTW_WISDOM, user_params->DIM, D_PARA, user_params->N_THREADS, HIRES_box);
     if(stat>0) Throw(stat);
-    LOG_DEBUG("FFT'd hires boxes.");
+    LOG_SUPER_DEBUG("FFT'd hires boxes.");
 
 #pragma omp parallel shared(boxes,HIRES_box) private(i,j,k) num_threads(user_params->N_THREADS)
     {
@@ -203,6 +203,8 @@ int ComputeInitialConditions(
         }
     }
 
+    LOG_SUPER_DEBUG("Saved HIRES_box to struct.");
+
     // *** If required, let's also create a lower-resolution version of the density field  *** //
     memcpy(HIRES_box, HIRES_box_saved, sizeof(fftwf_complex)*KSPACE_NUM_PIXELS);
 
@@ -315,7 +317,7 @@ int ComputeInitialConditions(
         }
 
     }
-    LOG_DEBUG("Completed Relative velocities.");
+    LOG_SUPER_DEBUG("Completed Relative velocities.");
     // ******* End of Relative Velocity part ******* //
 
     // Now look at the velocities
@@ -428,7 +430,7 @@ int ComputeInitialConditions(
         }
     }
 
-    LOG_DEBUG("Done Inverse FT.");
+    LOG_SUPER_DEBUG("Done Inverse FT.");
 
     // * *************************************************** * //
     // *              BEGIN 2LPT PART                        * //
@@ -787,7 +789,7 @@ int ComputeInitialConditions(
         fftwf_free(phi_1);
 
     }
-    LOG_DEBUG("Done 2LPT.");
+    LOG_SUPER_DEBUG("Done 2LPT.");
 
     // * *********************************************** * //
     // *               END 2LPT PART                     * //
@@ -803,7 +805,7 @@ int ComputeInitialConditions(
     free_ps();
 
     free_rng_threads(r);
-    LOG_DEBUG("Cleaned Up.");
+    LOG_SUPER_DEBUG("Cleaned Up.");
     } // End of Try{}
 
     Catch(status){
diff --git a/src/py21cmfast/src/IonisationBox.c b/src/py21cmfast/src/IonisationBox.c
index 942f72d96..4c3eeddad 100644
--- a/src/py21cmfast/src/IonisationBox.c
+++ b/src/py21cmfast/src/IonisationBox.c
@@ -391,6 +391,7 @@ void setup_first_z_prevbox(IonizedBox *previous_ionize_box, PerturbedField *prev
             }
         }
     }
+
 }
 
 void calculate_mcrit_boxes(IonizedBox *prev_ionbox, TsBox *spin_temp, InitialConditions *ini_boxes, struct IonBoxConstants *consts,
diff --git a/src/py21cmfast/utils.py b/src/py21cmfast/utils.py
index 77d40c474..beda09c70 100644
--- a/src/py21cmfast/utils.py
+++ b/src/py21cmfast/utils.py
@@ -1,6 +1,6 @@
 """Utilities for interacting with 21cmFAST data structures."""
 
-from .wrapper.outputs import InitialConditions, _OutputStructZ
+from .wrapper.outputs import InitialConditions, OutputStruct
 
 
 def get_all_fieldnames(
@@ -18,7 +18,7 @@ def get_all_fieldnames(
         Whether to return results as a dictionary of ``quantity: class_name``.
         Otherwise returns a set of quantities.
     """
-    classes = [cls(redshift=0, dummy=True) for cls in _OutputStructZ._implementations()]
+    classes = [cls.dummy() for cls in OutputStruct._implementations()]
 
     if not lightcone_only:
         classes.append(InitialConditions())
diff --git a/src/py21cmfast/wrapper/arrays.py b/src/py21cmfast/wrapper/arrays.py
new file mode 100644
index 000000000..76532c971
--- /dev/null
+++ b/src/py21cmfast/wrapper/arrays.py
@@ -0,0 +1,163 @@
+"""Module for dealing with arrays that are input/output to C functions."""
+
+import attrs
+import h5py
+import numpy as np
+from abc import ABC, abstractmethod
+from attrs.validators import instance_of, optional
+from pathlib import Path
+from typing import Self, Sequence
+
+from .arraystate import ArrayState
+
+
+def _tuple_of_ints(x: Sequence[float | int]) -> tuple[int]:
+    return tuple(int(i) for i in x)
+
+
+class CacheBackend(ABC):
+    """Abstract base class for cache backends."""
+
+    @abstractmethod
+    def read(self) -> np.ndarray:
+        """Read an Array from the cache."""
+        pass
+
+    @abstractmethod
+    def write(self, val: np.ndarray) -> None:
+        """Write an Array to the cache."""
+        pass
+
+
+@attrs.define(frozen=True)
+class H5Backend(CacheBackend):
+    """Backend for caching arrays in a HDF5 file."""
+
+    path: Path = attrs.field(converter=Path)
+    dataset: str = attrs.field(converter=str)
+
+    def read(self) -> np.ndarray:
+        """Read an array from the cache."""
+        with h5py.File(self.path, "r") as f:
+            return f[self.dataset][()]
+
+    def write(self, val: np.ndarray, overwrite: bool = False) -> None:
+        """Write an array to the cache."""
+        if not self.path.parent.exists():
+            self.path.parent.mkdir(parents=True, exist_ok=True)
+
+        with h5py.File(self.path, "a") as f:
+            if self.dataset in f:
+                if overwrite:
+                    f[self.dataset] = val
+            else:
+                f.create_dataset(self.dataset, data=val)
+
+
+@attrs.define(slots=False, frozen=True)
+class Array:
+    """
+    A flexible array management class providing  state tracking and initialization capabilities.
+
+    The Array class supports dynamic array creation, caching, and state management with
+    immutable semantics.The class allows for creating arrays with configurable shape,
+    data type, initialization function, and optional caching backend.
+    It provides methods for initializing, setting values, removing values, writing to
+    disk, and loading from disk while maintaining a consistent state.
+
+    Attributes
+    ----------
+    shape
+        Dimensions of the array.
+    dtype
+        Data type of the array (default is float).
+    state
+        Current state of the array.
+    initfunc
+        Function used for array initialization (default is np.zeros).
+    value
+        Actual array data.
+    cache_backend
+        Optional backend for disk caching.
+
+    Examples
+    --------
+    # Create an array with specific shape and initialize
+    arr = Array(shape=(10, 10))
+    initialized_arr = arr.initialize()
+
+    # Set a value and write to disk
+    arr = arr.set_value(np.random.rand(10, 10))
+    arr = arr.written_to_disk(backend)
+
+    """
+
+    shape = attrs.field(converter=_tuple_of_ints)
+    dtype = attrs.field(default=float, kw_only=True)
+    state = attrs.field(factory=ArrayState, kw_only=True)
+    initfunc = attrs.field(default=np.zeros, kw_only=True)
+    value = attrs.field(
+        converter=attrs.converters.optional(np.asarray), default=None, kw_only=True
+    )
+    cache_backend = attrs.field(
+        default=None, validator=optional(instance_of(CacheBackend)), kw_only=True
+    )
+
+    @value.validator
+    def _value_validator(self, att, val):
+        if val is None:
+            return
+
+        if val.shape != self.shape:
+            raise ValueError(f"Shape mismatch: expected {self.shape}, got {val.shape}")
+
+    def initialize(self):
+        """Initialize the array to its initial/default allocated state."""
+        if self.state.initialized:
+            return self
+        else:
+            return attrs.evolve(
+                self,
+                value=self.initfunc(self.shape, dtype=self.dtype),
+                state=self.state.initialize(),
+            )
+
+    def with_value(self, val: np.ndarray) -> Self:
+        """Set the array to a given value and return a new Array."""
+        return attrs.evolve(self, value=val, state=self.state.computed())
+
+    def without_value(self) -> Self:
+        """Remove the allocated data from the array."""
+        return attrs.evolve(self, value=None, state=self.state.dropped())
+
+    def written_to_disk(self, backend: CacheBackend | None) -> Self:
+        """Write the array to disk and return a new object with correct state."""
+        backend = backend or self.cache_backend
+
+        if backend is None:
+            raise ValueError("backend must be specified")
+
+        backend.write(self.value)
+        return attrs.evolve(self, cache_backend=backend, state=self.state.written())
+
+    def purged_to_disk(self, backend: CacheBackend | None) -> Self:
+        """Move the array data to disk and return a new object with correct state."""
+        return attrs.evolve(self.written_to_disk(backend), value=None)
+
+    def loaded_from_disk(self, backend: CacheBackend | None = None) -> Self:
+        """Load values for the array from a cache backend, and return a new instance."""
+        if self.value is not None:
+            return attrs.evolve(self, cache_backend=backend)
+
+        backend = backend or self.cache_backend
+
+        if backend is None:
+            raise ValueError("backend must be specified")
+
+        value = backend.read()
+        return attrs.evolve(
+            self,
+            value=value,
+            cache_backend=backend,
+            state=self.state.loaded_from_disk(),
+        )
diff --git a/src/py21cmfast/wrapper/arraystate.py b/src/py21cmfast/wrapper/arraystate.py
index 6811946c9..58e85feb0 100644
--- a/src/py21cmfast/wrapper/arraystate.py
+++ b/src/py21cmfast/wrapper/arraystate.py
@@ -1,5 +1,8 @@
 """Classes dealing with the state of arrays."""
 
+import attrs
+from typing import Self
+
 
 class ArrayStateError(ValueError):
     """Errors arising from incorrectly modifying array state."""
@@ -7,70 +10,54 @@ class ArrayStateError(ValueError):
     pass
 
 
+@attrs.define(frozen=True)
 class ArrayState:
     """Define the memory state of a struct array."""
 
-    def __init__(
-        self, initialized=False, c_memory=False, computed_in_mem=False, on_disk=False
-    ):
-        self._initialized = initialized
-        self._c_memory = c_memory
-        self._computed_in_mem = computed_in_mem
-        self._on_disk = on_disk
-
-    @property
-    def initialized(self):
-        """Whether the array is initialized (i.e. allocated memory)."""
-        return self._initialized
+    initialized = attrs.field(default=False, converter=bool)
+    c_memory = attrs.field(default=False, converter=bool)
+    computed_in_mem = attrs.field(default=False, converter=bool)
+    on_disk = attrs.field(default=False, converter=bool)
 
-    @initialized.setter
-    def initialized(self, val):
-        if not val:
-            # if its not initialized, can't be computed in memory
-            self.computed_in_mem = False
-        self._initialized = bool(val)
+    def deinitialize(self) -> Self:
+        """Return new state that is not initialized."""
+        return attrs.evolve(self, initialized=False, computed_in_mem=False)
 
-    @property
-    def c_memory(self):
-        """Whether the array's memory (if any) is controlled by C."""
-        return self._c_memory
+    def initialize(self) -> Self:
+        """Return new state that is initialized."""
+        return attrs.evolve(self, initialized=True)
 
-    @c_memory.setter
-    def c_memory(self, val):
-        self._c_memory = bool(val)
+    def computed(self) -> Self:
+        """Return new state indicating the array has been computed."""
+        return attrs.evolve(self, computed_in_mem=True, initialized=True)
 
-    @property
-    def computed_in_mem(self):
-        """Whether the array is computed and stored in memory."""
-        return self._computed_in_mem
+    def dropped(self) -> Self:
+        """Return new state indicating the array has been dropped from memory."""
+        return attrs.evolve(self, initialized=False, computed_in_mem=False)
 
-    @computed_in_mem.setter
-    def computed_in_mem(self, val):
-        if val:
-            # any time we pull something into memory, it must be initialized.
-            self.initialized = True
-        self._computed_in_mem = bool(val)
+    def written(self) -> Self:
+        """Return new state indicating the array has been written to disk."""
+        return attrs.evolve(self, on_disk=True)
 
-    @property
-    def on_disk(self):
-        """Whether the array is computed and store on disk."""
-        return self._on_disk
+    def purged_to_disk(self) -> Self:
+        """Return new state indicating the array has been written to disk and dropped."""
+        return self.written().dropped()
 
-    @on_disk.setter
-    def on_disk(self, val):
-        self._on_disk = bool(val)
+    def loaded_from_disk(self) -> Self:
+        """Return new state indicating the array has been loaded from disk into memory."""
+        return self.computed().written()
 
     @property
-    def computed(self):
+    def is_computed(self) -> bool:
         """Whether the array is computed anywhere."""
         return self.computed_in_mem or self.on_disk
 
     @property
-    def c_has_active_memory(self):
+    def c_has_active_memory(self) -> bool:
         """Whether C currently has initialized memory for this array."""
         return self.c_memory and self.initialized
 
-    def __str__(self):
+    def __str__(self) -> str:
         """Returns a string representation of the ArrayState."""
         if self.computed_in_mem:
             return "computed (in mem)"
diff --git a/src/py21cmfast/wrapper/cfuncs.py b/src/py21cmfast/wrapper/cfuncs.py
index 570967f4d..60b7c823d 100644
--- a/src/py21cmfast/wrapper/cfuncs.py
+++ b/src/py21cmfast/wrapper/cfuncs.py
@@ -7,10 +7,9 @@
 from typing import Literal, Sequence
 
 from ..c_21cmfast import ffi, lib
-from ..drivers.param_config import InputParameters
 from ._utils import _process_exitcode
 from .globals import global_params
-from .inputs import AstroParams, CosmoParams, FlagOptions, UserParams
+from .inputs import AstroParams, CosmoParams, FlagOptions, InputParameters, UserParams
 from .outputs import InitialConditions, PerturbHaloField
 
 logger = logging.getLogger(__name__)
diff --git a/src/py21cmfast/wrapper/inputs.py b/src/py21cmfast/wrapper/inputs.py
index 79bff0ce1..14596fae2 100644
--- a/src/py21cmfast/wrapper/inputs.py
+++ b/src/py21cmfast/wrapper/inputs.py
@@ -15,19 +15,19 @@
 
 from __future__ import annotations
 
+import attrs
 import logging
 import warnings
 from astropy import units as un
 from astropy.cosmology import FLRW, Planck15
-from attrs import converters, define
+from attrs import asdict, define, evolve
 from attrs import field as _field
 from attrs import validators
+from functools import cached_property
 
 from .._cfg import config
-from .._data import DATA_PATH
-from ..c_21cmfast import ffi, lib
 from .globals import global_params
-from .structs import InputStruct
+from .structs import StructWrapper
 
 logger = logging.getLogger(__name__)
 
@@ -72,6 +72,8 @@ def vld(inst, att, val):
         if val < mn or val > mx:
             raise ValueError(f"{att.name} must be between {mn} and {mx}")
 
+    return vld
+
 
 # Cosmology is from https://arxiv.org/pdf/1807.06209.pdf
 # Table 2, last column. [TT,TE,EE+lowE+lensing+BAO]
@@ -83,6 +85,192 @@ def vld(inst, att, val):
 )
 
 
+define(frozen=True, kw_only=True)
+
+
+class InputStruct:
+    """
+    A convenient interface to create a C structure with defaults specified.
+
+    It is provided for the purpose of *creating* C structures in Python to be passed to
+    C functions, where sensible defaults are available. Structures which are created
+    within C and passed back do not need to be wrapped.
+
+    This provides a *fully initialised* structure, and will fail if not all fields are
+    specified with defaults.
+
+    .. note:: The actual C structure is gotten by calling an instance. This is
+              auto-generated when called, based on the parameters in the class.
+
+    .. warning:: This class will *not* deal well with parameters of the struct which are
+                 pointers. All parameters should be primitive types, except for strings,
+                 which are dealt with specially.
+
+    Parameters
+    ----------
+    ffi : cffi object
+        The ffi object from any cffi-wrapped library.
+    """
+
+    _subclasses = {}
+    _write_exclude_fields = ()
+
+    @classmethod
+    def new(cls, x: dict | InputStruct | None = None, **kwargs):
+        """
+        Create a new instance of the struct.
+
+        Parameters
+        ----------
+        x : dict | InputStruct | None
+            Initial values for the struct. If `x` is a dictionary, it should map field
+            names to their corresponding values. If `x` is an instance of this class,
+            its attributes will be used as initial values. If `x` is None, the
+            struct will be initialised with default values.
+
+        Other Parameters
+        ----------------
+        All other parameters should be passed as if directly to the class constructor
+        (i.e. as parameter names).
+
+        Examples
+        --------
+        >>> up = UserParams({'HII_DIM': 250})
+        >>> up.HII_DIM
+        250
+        >>> up = UserParams(up)
+        >>> up.HII_DIM
+        250
+        >>> up = UserParams()
+        >>> up.HII_DIM
+        200
+        >>> up = UserParams(HII_DIM=256)
+        >>> up.HII_DIM
+        256
+        """
+        if isinstance(x, dict):
+            return cls(**x, **kwargs)
+        elif isinstance(x, InputStruct):
+            return x.clone(**kwargs)
+        elif x is None:
+            return cls(**kwargs)
+        else:
+            raise ValueError(
+                f"Cannot instantiate {cls.__name__} with type {x.__class__}"
+            )
+
+    def __init_subclass__(cls) -> None:
+        """Store each subclass for easy access."""
+        InputStruct._subclasses[cls.__name__] = cls
+
+    @cached_property
+    def struct(self) -> StructWrapper:
+        """The python-wrapped struct associated with this input object."""
+        return StructWrapper(self.__class__.__name__)
+
+    @cached_property
+    def cstruct(self) -> StructWrapper:
+        """The object pointing to the memory accessed by C-code for this struct."""
+        cdict = self.cdict
+        for k in self.struct.fieldnames:
+            val = cdict[k]
+
+            if isinstance(val, str):
+                # If it is a string, need to convert it to C string ourselves.
+                val = self.ffi.new("char[]", val.encode())
+
+            setattr(self.struct.cstruct, k, val)
+
+        return self.struct.cstruct
+
+    def clone(self, **kwargs):
+        """Make a fresh copy of the instance with arbitrary parameters updated."""
+        return evolve(self, **kwargs)
+
+    def asdict(self) -> dict:
+        """Return a dict representation of the instance.
+
+        Examples
+        --------
+        This dict should be such that doing the following should work, i.e. it can be
+        used exactly to construct a new instance of the same object::
+
+        >>> inp = InputStruct(**params)
+        >>> newinp =InputStruct(**inp.asdict())
+        >>> inp == newinp
+        """
+        return asdict(self)
+
+    @cached_property
+    def cdict(self) -> dict:
+        """A python dictionary containing the properties of the wrapped C-struct.
+
+        The memory pointed to by this dictionary is *not* owned by the wrapped C-struct,
+        but is rather just a python dict. However, in contrast to :meth:`asdict`, this
+        method transforms the properties to what they should be in C (e.g. linear space
+        vs. log-space) before putting them into the dict.
+
+        This dict also contains *only* the properties of the wrapped C-struct, rather
+        than all properties of the :class:`InputStruct` instance (some attributes of the
+        python instance are there only to guide setting of defaults, and don't appear
+        in the C-struct at all).
+        """
+        fields = attrs.fields(self.__class__)
+        transformers = {
+            field.name: field.metadata.get("transformer", None) for field in fields
+        }
+
+        out = {}
+        for k in self.struct.fieldnames:
+            val = getattr(self, k)
+            # we assume properties (as opposed to attributes) are already converted
+            trns = transformers.get(k)
+            out[k] = val if trns is None else trns(val)
+        return out
+
+    def __str__(self) -> str:
+        """Human-readable string representation of the object."""
+        d = self.asdict()
+        biggest_k = max(len(k) for k in d)
+        params = "\n    ".join(sorted(f"{k:<{biggest_k}}: {v}" for k, v in d.items()))
+        return f"""{self.__class__.__name__}:{params} """
+
+    @classmethod
+    def from_subdict(cls, dct, safe=True):
+        """Construct an instance of a parameter structure from a dictionary."""
+        fieldnames = [
+            field.name
+            for field in attrs.fields(cls)
+            if field.eq and field.default is not None
+        ]
+        if set(fieldnames) != set(dct.keys()):
+            missing_items = [
+                (field.name, field.default)
+                for field in attrs.fields(cls)
+                if field.name not in dct.keys() and field.name in fieldnames
+            ]
+            extra_items = [(k, v) for k, v in dct.items() if k not in fieldnames]
+            message = (
+                f"There are extra or missing {cls.__name__} in the file to be read.\n"
+                f"EXTRAS: {extra_items}\n"
+                f"MISSING: {missing_items}\n"
+            )
+            if safe:
+                raise ValueError(
+                    message
+                    + "set `safe=False` to load structures from previous versions"
+                )
+            else:
+                warnings.warn(
+                    message
+                    + "\nExtras are ignored and missing are set to default (shown) values."
+                    + "\nUsing these parameter structures in further computation will give inconsistent results."
+                )
+            dct = {k: v for k, v in dct.items() if k in fieldnames}
+
+        return cls.new(dct)
+
+
 @define(frozen=True, kw_only=True)
 class CosmoParams(InputStruct):
     """
@@ -144,6 +332,22 @@ def from_astropy(cls, cosmo: FLRW, **kwargs):
             hlittle=cosmo.h, OMm=cosmo.Om0, OMb=cosmo.Ob0, base_cosmo=cosmo, **kwargs
         )
 
+    def asdict(self) -> dict:
+        """Return a dict representation of the instance.
+
+        Examples
+        --------
+        This dict is such that doing the following should work, i.e. it can be
+        used exactly to construct a new instance of the same object::
+
+        >>> inp = InputStruct(**params)
+        >>> newinp =InputStruct(**inp.asdict())
+        >>> inp == newinp
+        """
+        d = super().asdict()
+        del d["_base_cosmo"]
+        return d
+
 
 @define(frozen=True, kw_only=True)
 class UserParams(InputStruct):
@@ -821,3 +1025,244 @@ def _NU_X_THRESH_vld(self, att, val):
                 NU_X_MAX
                 """
             )
+
+
+class InputCrossValidationError(ValueError):
+    """Error when two parameters from different structs aren't consistent."""
+
+    pass
+
+
+def input_param_field(kls: InputStruct):
+    """An attrs field that must be an InputStruct.
+
+    Parameters
+    ----------
+    kls : InputStruct subclass
+        The parameter structure which should be returned as an attrs field
+
+    """
+    return _field(
+        default=kls.new(),
+        converter=kls.new,
+        validator=validators.instance_of(kls),
+    )
+
+
+def get_logspaced_redshifts(
+    min_redshift: float,
+    z_step_factor: float,
+    max_redshift: float,
+) -> tuple[float]:
+    """Compute a sequence of redshifts to evolve over that are log-spaced."""
+    redshifts = [min_redshift]
+    while redshifts[-1] < max_redshift:
+        redshifts.append((redshifts[-1] + 1.0) * z_step_factor - 1.0)
+
+    return tuple(redshifts[::-1])
+
+
+def _node_redshifts_converter(value) -> tuple[float] | None:
+    if value is None or len(value) == 0:
+        return ()
+    if hasattr(value, "__len__"):
+        return tuple(sorted([float(v) for v in value], reverse=True))
+    return (float(value),)
+
+
+@define(kw_only=True, frozen=True)
+class InputParameters:
+    """A class defining a collection of InputStruct instances.
+
+    This class simplifies combining different InputStruct instances together, performing
+    validation checks between them, and being able to cross-check compatibility between
+    different sets of instances.
+    """
+
+    random_seed = _field(converter=int)
+    user_params: UserParams = input_param_field(UserParams)
+    cosmo_params: CosmoParams = input_param_field(CosmoParams)
+    flag_options: FlagOptions = input_param_field(FlagOptions)
+    astro_params: AstroParams = input_param_field(AstroParams)
+    node_redshifts = _field(converter=_node_redshifts_converter)
+
+    @node_redshifts.default
+    def _node_redshifts_default(self):
+        return (
+            get_logspaced_redshifts(
+                min_redshift=5.5,
+                max_redshift=self.user_params.Z_HEAT_MAX,
+                z_step_factor=self.user_params.ZPRIME_STEP_FACTOR,
+            )
+            if (self.flag_options.INHOMO_RECO or self.flag_options.USE_TS_FLUCT)
+            else None
+        )
+
+    @node_redshifts.validator
+    def _node_redshifts_validator(self, att, val):
+        if (self.flag_options.INHOMO_RECO or self.flag_options.USE_TS_FLUCT) and max(
+            val
+        ) < self.user_params.Z_HEAT_MAX:
+            raise ValueError(
+                "For runs with inhomogeneous recombinations or spin temperature fluctuations,\n"
+                + f"your maximum passed node_redshifts {max(val)} must be above Z_HEAT_MAX {self.user_params.Z_HEAT_MAX}"
+            )
+
+    @flag_options.validator
+    def _flag_options_validator(self, att, val):
+        if self.user_params is not None:
+            if (
+                val.USE_MINI_HALOS
+                and not self.user_params.USE_RELATIVE_VELOCITIES
+                and not val.FIX_VCB_AVG
+            ):
+                warnings.warn(
+                    "USE_MINI_HALOS needs USE_RELATIVE_VELOCITIES to get the right evolution!"
+                )
+
+            if val.HALO_STOCHASTICITY and self.user_params.PERTURB_ON_HIGH_RES:
+                msg = (
+                    "Since the lowres density fields are required for the halo sampler"
+                    "We are currently unable to use PERTURB_ON_HIGH_RES and HALO_STOCHASTICITY"
+                    "Simultaneously."
+                )
+                raise NotImplementedError(msg)
+
+        if val.USE_EXP_FILTER and not val.USE_HALO_FIELD:
+            warnings.warn("USE_EXP_FILTER has no effect unless USE_HALO_FIELD is true")
+
+    @astro_params.validator
+    def _astro_params_validator(self, att, val):
+        if val.R_BUBBLE_MAX > self.user_params.BOX_LEN:
+            raise InputCrossValidationError(
+                f"R_BUBBLE_MAX is larger than BOX_LEN ({val.R_BUBBLE_MAX} > {self.user_params.BOX_LEN}). This is not allowed."
+            )
+
+        if val.R_BUBBLE_MAX != 50 and self.flag_options.INHOMO_RECO:
+            warnings.warn(
+                "You are setting R_BUBBLE_MAX != 50 when INHOMO_RECO=True. "
+                "This is non-standard (but allowed), and usually occurs upon manual "
+                "update of INHOMO_RECO"
+            )
+
+        if val.M_TURN > 8 and self.flag_options.USE_MINI_HALOS:
+            warnings.warn(
+                "You are setting M_TURN > 8 when USE_MINI_HALOS=True. "
+                "This is non-standard (but allowed), and usually occurs upon manual "
+                "update of M_TURN"
+            )
+
+        if (
+            global_params.HII_FILTER == 1
+            and val.R_BUBBLE_MAX > self.user_params.BOX_LEN / 3
+        ):
+            msg = (
+                "Your R_BUBBLE_MAX is > BOX_LEN/3 "
+                f"({val.R_BUBBLE_MAX} > {self.user_params.BOX_LEN / 3})."
+            )
+
+            if config["ignore_R_BUBBLE_MAX_error"]:
+                warnings.warn(msg)
+            else:
+                raise ValueError(msg)
+
+    @user_params.validator
+    def _user_params_validator(self, att, val):
+        # perform a very rudimentary check to see if we are underresolved and not using the linear approx
+        if val.BOX_LEN > val.DIM and not global_params.EVOLVE_DENSITY_LINEARLY:
+            warnings.warn(
+                "Resolution is likely too low for accurate evolved density fields\n It Is recommended"
+                + "that you either increase the resolution (DIM/BOX_LEN) or"
+                + "set the EVOLVE_DENSITY_LINEARLY flag to 1"
+            )
+
+    def __getitem__(self, key):
+        """Get an item from the instance in a dict-like manner."""
+        # Also allow using **input_parameters
+        return getattr(self, key)
+
+    def is_compatible_with(self, other: InputParameters) -> bool:
+        """Check if this object is compatible with another parameter struct.
+
+        Compatibility is slightly different from strict equality. Compatibility requires
+        that if a parameter struct *exists* on the other object, then it must be equal
+        to this one. That is, if astro_params is None on the other InputParameter object,
+        then this one can have astro_params as NOT None, and it will still be
+        compatible. However the inverse is not true -- if this one has astro_params as
+        None, then all others must have it as None as well.
+        """
+        if not isinstance(other, InputParameters):
+            return False
+
+        return not any(
+            other[key] is not None and self[key] is not None and self[key] != other[key]
+            for key in self.merge_keys()
+        )
+
+    def evolve_input_structs(self, **kwargs):
+        """Return an altered clone of the `InputParameters` structs.
+
+        Unlike clone(), this function takes fields from the constituent `InputStruct` classes
+        and only overwrites those sub-fields instead of the entire field
+        """
+        struct_args = {}
+        for inp_type in ("cosmo_params", "user_params", "astro_params", "flag_options"):
+            obj = getattr(self, inp_type)
+            struct_args[inp_type] = obj.clone(
+                **{k: v for k, v in kwargs.items() if hasattr(obj, k)}
+            )
+
+        return self.clone(**struct_args)
+
+    @classmethod
+    def from_template(cls, name, **kwargs):
+        """Construct full InputParameters instance from native or TOML file template.
+
+        Takes `InputStruct` fields as keyword arguments which overwrite the template/default fields
+        """
+        from ..run_templates import create_params_from_template
+
+        return cls(
+            **create_params_from_template(name),
+            **kwargs,
+        )
+
+    def clone(self, **kwargs):
+        """Generate a copy of the InputParameter structure with specified changes."""
+        return evolve(self, **kwargs)
+
+    def __repr__(self):
+        """
+        String representation of the structure.
+
+        Created by combining repr methods from the InputStructs
+        which make up this object
+        """
+        return (
+            f"cosmo_params: {repr(self.cosmo_params)}\n"
+            + f"user_params: {repr(self.user_params)}\n"
+            + f"astro_params: {repr(self.astro_params)}\n"
+            + f"flag_options: {repr(self.flag_options)}\n"
+        )
+
+    @cached_property
+    def _user_cosmo_hash(self):
+        """A hash generated from the user and cosmo params as well random seed."""
+        return hash((self.random_seed, self.user_params, self.cosmo_params))
+
+    @cached_property
+    def _zgrid_hash(self):
+        return hash((self._user_cosmo_hash, self.node_redshifts))
+
+    @cached_property
+    def _full_hash(self):
+        return hash(self)
+
+    @property
+    def evolution_required(self) -> bool:
+        """Whether evolution is required for these parameters."""
+        return (
+            self.flag_options.USE_TS_FLUCT
+            or self.flag_options.INHOMO_RECO
+            or self.flag_options.USE_MINI_HALOS
+        )
diff --git a/src/py21cmfast/wrapper/outputs.py b/src/py21cmfast/wrapper/outputs.py
index 41681bd28..8d3a75449 100644
--- a/src/py21cmfast/wrapper/outputs.py
+++ b/src/py21cmfast/wrapper/outputs.py
@@ -14,111 +14,525 @@
 
 from __future__ import annotations
 
+import attrs
 import logging
 import numpy as np
+import warnings
+from abc import ABC, abstractmethod
+from bidict import bidict
 from cached_property import cached_property
+from enum import Enum
+from typing import Any, Literal, Self, Sequence
 
 from .. import __version__
 from ..c_21cmfast import ffi, lib
-from ..drivers.param_config import InputParameters
-from .inputs import AstroParams, CosmoParams, FlagOptions, UserParams, global_params
-from .structs import OutputStruct as _BaseOutputStruct
+from .arrays import Array
+from .exceptions import _process_exitcode
+from .inputs import (
+    AstroParams,
+    CosmoParams,
+    FlagOptions,
+    InputParameters,
+    InputStruct,
+    UserParams,
+    global_params,
+)
+from .structs import StructWrapper
 
 logger = logging.getLogger(__name__)
 
+_ALL_OUTPUT_STRUCTS = {}
 
-# NOTE: The `inputs` arguments to the __init__ methods are set this way such that the
-#   required fields (`_inputs`) can be read either from the file
-#   (done in structs.OutputStruct.__init__) or the input struct (done here)
-# TODO: there is certainly a better way to organise it
-class _OutputStruct(_BaseOutputStruct):
-    _global_params = global_params
 
-    def __init__(self, *, inputs: InputParameters | None = None, **kwargs):
-        if inputs:
-            self.cosmo_params = inputs.cosmo_params
-            self.user_params = inputs.user_params
-            self.random_seed = inputs.random_seed
+def _arrayfield(optional: bool = False, **kw):
+    if optional:
+        return attrs.field(
+            default=None,
+            validator=attrs.validators.optional(attrs.validators.instance_of(Array)),
+            eq=False,
+            type=Array,
+        )
+    else:
+        return attrs.field(
+            validator=attrs.validators.instance_of(Array),
+            eq=False,
+            type=Array,
+        )
 
-        super().__init__(**kwargs)
 
+class _HashType(Enum):
+    user_cosmo = 0
+    zgrid = 1
+    full = 2
 
-class _OutputStructZ(_OutputStruct):
-    _inputs = _OutputStruct._inputs + ("redshift",)
 
-    def __init__(
-        self,
-        *,
-        redshift: float | None = None,
-        inputs: InputParameters | None = None,
-        **kwargs,
-    ):
-        self.redshift = redshift
+@attrs.define(slots=False, kw_only=True)
+class OutputStruct(ABC):
+    """Base class for any class that wraps a C struct meant to be output from a C function."""
 
-        super().__init__(inputs=inputs, **kwargs)
+    _meta = False
+    _fields_ = []
+    _global_params = None
+    _c_based_pointers = ()
+    _c_compute_function = None
+    _compat_hash = _HashType.full
 
+    _TYPEMAP = bidict({"float32": "float *", "float64": "double *", "int32": "int *"})
 
-class _AllParamsBox(_OutputStructZ):
-    _meta = True
-    _inputs = _OutputStructZ._inputs + ("flag_options", "astro_params")
+    inputs: InputParameters = attrs.field(
+        validator=attrs.validators.instance_of(InputParameters)
+    )
+    dummy: bool = attrs.field(default=False, converter=bool)
+    initial: bool = attrs.field(default=False, converter=bool)
+
+    @property
+    def _name(self):
+        """The name of the struct."""
+        return self.__class__.__name__
+
+    def __init_subclass__(cls):
+        """Store subclasses for easy access."""
+        if not cls._meta:
+            _ALL_OUTPUT_STRUCTS[cls.__name__] = cls
+
+        return super().__init_subclass__()
+
+    @property
+    def user_params(self) -> UserParams:
+        """The UserParams object for this output struct."""
+        return self.inputs.user_params
+
+    @property
+    def cosmo_params(self) -> CosmoParams:
+        """The CosmoParams object for this output struct."""
+        return self.inputs.cosmo_params
+
+    @property
+    def astro_params(self) -> AstroParams:
+        """The AstroParams object for this output struct."""
+        return self.inputs.astro_params
+
+    @property
+    def flag_options(self) -> FlagOptions:
+        """The FlagOptions object for this output struct."""
+        return self.inputs.flag_options
+
+    def _inputs_compatible_with(self, other: OutputStruct | InputParameters) -> bool:
+        """Check whether this objects' inputs are compatible with another object's.
+
+        This check is sensitive to the fact that the other object may be at a different
+        level of the simulation heirarchy, and therefore may be compatible even if the
+        params are different. As long as they are the same at the level higher than the
+        minimum level of the simulation, they are considered compatible.
+        """
+        if not isinstance(other, OutputStruct | InputParameters):
+            return False
+
+        if isinstance(other, InputParameters):
+            # Compare at the level required by this object only
+            return getattr(self.inputs, f"_{self._compat_hash.name}_hash") == getattr(
+                other, f"_{self._compat_hash.name}_hash"
+            )
+
+        min_req = min(self._compat_hash.value, other._compat_hash.value)
+        min_req = _HashType(min_req)
+
+        return getattr(self.inputs, f"_{min_req.name}_hash") == getattr(
+            other.inputs, f"_{min_req.name}_hash"
+        )
+
+    @property
+    def arrays(self) -> dict[str, Array]:
+        """A dictionary of Array objects whose memory is shared between this object and the C backend."""
+        me = attrs.asdict(self, recurse=False)
+        return {k: x for k, x in me.items() if isinstance(x, Array)}
+
+    @cached_property
+    def struct(self) -> StructWrapper:
+        """The python-wrapped struct associated with this input object."""
+        return StructWrapper(self._name)
+
+    @cached_property
+    def cstruct(self) -> StructWrapper:
+        """The object pointing to the memory accessed by C-code for this struct."""
+        return self.struct.cstruct
+
+    def _init_arrays(self):
+        for k, array in self.arrays.items():
+            # Don't initialize C-based pointers or already-inited stuff, or stuff
+            # that's computed on disk (if it's on disk, accessing the array should
+            # just give the computed version, which is what we would want, not a
+            # zero-inited array).
+            if array.state.c_memory or array.state.initialized or array.state.on_disk:
+                continue
+
+            setattr(self, k, array.initialize())
 
-    _filter_params = _OutputStruct._filter_params + [
-        "T_USE_VELOCITIES",  # bt
-        "MAX_DVDR",  # bt
-    ]
+    @property
+    def random_seed(self) -> int:
+        """The random seed for this particular instance."""
+        return self.inputs.random_seed
+
+    def sync(self):
+        """Sync the current state of the object with the underlying C-struct.
+
+        This will link any memory initialized by numpy in this object with the underlying
+        C-struct, and also update this object with any values computed from within C.
+        """
+        # Initialize all uninitialized arrays.
+        self._init_arrays()
+        for name, array in self.arrays.items():
+            # We do *not* set COMPUTED_ON_DISK items to the C-struct here, because we have no
+            # way of knowing (in this function) what is required to load in, and we don't want
+            # to unnecessarily load things in. We leave it to the user to ensure that all
+            # required arrays are loaded into memory before calling this function.
+            if array.state.initialized:
+                self.struct.expose_to_c(array, name)
+
+        for k in self.struct.primitive_fields:
+            if getattr(self, k) is not None:
+                setattr(self.cstruct, k, getattr(self, k))
+
+            setattr(self, k, getattr(self.cstruct, k))
+
+    def get(self, ary: str | Array):
+        """If possible, load an array from disk, storing it and returning the underlying array."""
+        if isinstance(ary, str):
+            name = ary
+            try:
+                ary = self.arrays[ary]
+            except KeyError as e:
+                try:
+                    return getattr(self, ary)  # could be a different attribute...
+                except AttributeError:
+                    raise AttributeError(f"The array {ary} does not exist") from e
+        elif names := [name for name, x in self.arrays.items() if x is ary]:
+            name = names[0]
+
+        else:
+            raise ValueError("The given array is not a part of this instance.")
+        if not ary.state.on_disk and not ary.state.initialized:
+            raise ValueError(f"Array '{ary.name}' is not on disk and not initialized.")
+
+        if ary.state.on_disk and not ary.state.computed_in_mem:
+            ary = ary.loaded_from_disk()
+            setattr(self, name, ary)
+
+        return ary.value
+
+    def set(self, name: str, value: Any):  # noqa: A003
+        """Set the value of an array."""
+        if name not in self.arrays:
+            try:
+                setattr(self, name, value)
+            except AttributeError:
+                raise AttributeError(f"The attribute '{name}' does not exist") from None
+        else:
+            setattr(self, name, self.arrays[name].with_value(value))
 
-    def __init__(
+    def prepare(
         self,
-        *,
-        inputs: InputParameters | None = None,
-        **kwargs,
+        flush: Sequence[str] | None = None,
+        keep: Sequence[str] | None = None,
+        force: bool = False,
     ):
-        if inputs:
-            self.flag_options = inputs.flag_options
-            self.astro_params = inputs.astro_params
+        """Prepare the instance for being passed to another function.
+
+        This will flush all arrays in "flush" from memory, and ensure all arrays
+        in "keep" are in memory. At least one of these must be provided. By default,
+        the complement of the given parameter is all flushed/kept.
+
+        Parameters
+        ----------
+        flush
+            Arrays to flush out of memory. Note that if no file is associated with this
+            instance, these arrays will be lost forever.
+        keep
+            Arrays to keep or load into memory. Note that if these do not already
+            exist, they will be loaded from file (if the file exists). Only one of
+            ``flush`` and ``keep`` should be specified.
+        force
+            Whether to force flushing arrays even if no disk storage exists.
+        """
+        if flush is None and keep is None:
+            raise ValueError("Must provide either flush or keep")
+
+        if flush is not None and keep is None:
+            keep = [k for k in self.arrays if k not in flush]
+        elif flush is None:
+            flush = [
+                k
+                for k, array in self.arrays.items()
+                if k not in keep and array.state.initialized
+            ]
 
-        # TODO: This only seems to be used in IonizedBox
-        self.log10_Mturnover_ave = 0.0
-        self.log10_Mturnover_MINI_ave = 0.0
+        flush = flush or []
+        keep = keep or []
 
-        super().__init__(inputs=inputs, **kwargs)
+        for k in flush:
+            self._remove_array(k, force=force)
 
+        # For everything we want to keep, we check if it is computed in memory,
+        # and if not, load it from disk.
+        for k in keep:
+            self.get(k)
 
-class InitialConditions(_OutputStruct):
-    """A class containing all initial conditions boxes."""
+    def _remove_array(self, k: str, *, force=False):
+        array = self.arrays[k]
+        state = array.state
 
-    _c_compute_function = lib.ComputeInitialConditions
+        if (
+            not state.initialized
+        ):  # TODO: how to handle the case where some arrays aren't required at all?
+            warnings.warn(f"Trying to remove array that isn't yet created: {k}")
+            return
+
+        if state.computed_in_mem and not state.on_disk and not force:
+            raise OSError(
+                f"Trying to purge array '{k}' from memory that hasn't been stored! Use force=True if you meant to do this."
+            )
+
+        if state.c_has_active_memory:  # TODO: do we need C-managed memory any more?
+            lib.free(getattr(self.cstruct, k))
+
+        setattr(self, k, array.without_value())
+
+    def purge(self, force=False):
+        """Flush all the boxes out of memory.
+
+        Parameters
+        ----------
+        force
+            Whether to force the purge even if no disk storage exists.
+        """
+        self.prepare(keep=[], force=force)
+
+    def load_all(self):
+        """Load all possible arrays into memory."""
+        for x in self.arrays:
+            self.get(x)
+
+    @property
+    def is_computed(self) -> bool:
+        """Whether this instance has been computed at all.
+
+        This is true either if the current instance has called :meth:`compute`,
+        or if it has a current existing :attr:`path` pointing to stored data,
+        or if such a path exists.
+
+        Just because the instance has been computed does *not* mean that all
+        relevant quantities are available -- some may have been purged from
+        memory without writing. Use :meth:`has` to check whether certain arrays
+        are available.
+        """
+        return any(v.state.is_computed for v in self.arrays.values())
+
+    def ensure_arrays_computed(self, *arrays, load=False) -> bool:
+        """Check if the given arrays are computed (not just initialized)."""
+        if not self.is_computed:
+            return False
+
+        computed = all(self.arrays[k].state.is_computed for k in arrays)
+
+        if computed and load:
+            self.prepare(keep=arrays, flush=[])
+
+        return computed
+
+    def ensure_arrays_inited(self, *arrays, init=False) -> bool:
+        """Check if the given arrays are initialized (or computed)."""
+        inited = all(self.arrays[k].state.initialized for k in arrays)
+
+        if init and not inited:
+            self._init_arrays()
+        return True
+
+    @abstractmethod
+    def get_required_input_arrays(self, input_box: Self) -> list[str]:
+        """Return all input arrays required to compute this object."""
+        pass
+
+    def ensure_input_computed(self, input_box: Self, load: bool = False) -> bool:
+        """Ensure all the inputs have been computed."""
+        if input_box.dummy:
+            return True
+
+        arrays = self.get_required_input_arrays(input_box)
+        if input_box.initial:
+            return input_box.ensure_arrays_inited(*arrays, init=load)
+
+        return input_box.ensure_arrays_computed(*arrays, load=load)
+
+    def summarize(self, indent: int = 0) -> str:
+        """Generate a string summary of the struct."""
+        indent = indent * "    "
+
+        # print array type and column headings
+        out = (
+            f"\n{indent}{self.__class__.__name__:>25}    "
+            + "   1st:         End:         Min:         Max:         Mean:         \n"
+        )
+
+        # print array extrema and means
+        for fieldname, array in self.arrays.items():
+            state = array.state
+            if not state.initialized:
+                out += f"{indent}    {fieldname:>25}:  uninitialized\n"
+            elif not state.is_computed:
+                out += f"{indent}    {fieldname:>25}:  initialized\n"
+            elif not state.computed_in_mem:
+                out += f"{indent}    {fieldname:>25}:  computed on disk\n"
+            else:
+                x = getattr(self, fieldname).flatten()
+                if len(x) > 0:
+                    out += f"{indent}    {fieldname:>25}: {x[0]:11.4e}, {x[-1]:11.4e}, {x.min():11.4e}, {x.max():11.4e}, {np.mean(x):11.4e}\n"
+                else:
+                    out += f"{indent}    {fieldname:>25}: size zero\n"
+
+        # print primitive fields
+        out += "".join(
+            f"{indent}    {fieldname:>25}: {getattr(self, fieldname, 'non-existent')}\n"
+            for fieldname in self.struct.primitive_fields
+        )
+
+        return out
+
+    @classmethod
+    def _log_call_arguments(cls, *args):
+        logger.debug(f"Calling {cls._c_compute_function.__name__} with following args:")
+
+        for arg in args:
+            if isinstance(arg, OutputStruct):
+                for line in arg.summarize(indent=1).split("\n"):
+                    logger.debug(line)
+            elif isinstance(arg, InputStruct):
+                for line in str(arg).split("\n"):
+                    logger.debug(f"    {line}")
+            else:
+                logger.debug(f"    {arg}")
+
+    def _ensure_arguments_exist(self, *args):
+        for arg in args:
+            if (
+                isinstance(arg, OutputStruct)
+                and not arg.dummy
+                and not self.ensure_input_computed(arg, load=True)
+            ):
+                raise ValueError(
+                    f"Trying to use {arg.__class__.__name__} to compute "
+                    f"{self.__class__.__name__}, but some required arrays "
+                    f"are not computed!\nArrays required: "
+                    f"{self.get_required_input_arrays(arg)}\n"
+                    f"Current State: {[(k, str(v.state)) for k, v in self.arrays.items()]}"
+                )
+
+    def _compute(self, allow_already_computed: bool = False, *args):
+        """Compute the actual function that fills this struct."""
+        # Check that all required inputs are really computed, and load them into memory
+        # if they're not already.
+        self._ensure_arguments_exist(*args)
+
+        # Write a detailed message about call arguments if debug turned on.
+        if logger.getEffectiveLevel() <= logging.DEBUG:
+            self._log_call_arguments(*args)
+
+        # Construct the args. All StructWrapper objects need to actually pass their
+        # underlying cstruct, rather than themselves.
+        inputs = [
+            arg.cstruct if isinstance(arg, (OutputStruct, InputStruct)) else arg
+            for arg in args
+        ]
+        # Sync the python/C memory
+        self.sync()
+        for arg in args:
+            if isinstance(arg, OutputStruct):
+                arg.sync()
+
+        # Ensure we haven't already tried to compute this instance.
+        if self.is_computed and not allow_already_computed:
+            raise ValueError(
+                f"You are trying to compute {self.__class__.__name__}, but it has already been computed."
+            )
+
+        # Perform the C computation
+        try:
+            exitcode = self._c_compute_function(*inputs, self.cstruct)
+        except TypeError as e:
+            logger.error(
+                f"Arguments to {self._c_compute_function.__name__}: " f"{inputs}"
+            )
+            raise e
+
+        _process_exitcode(exitcode, self._c_compute_function, args)
+
+        for name, array in self.arrays.items():
+            setattr(self, name, array.with_value(array.value))
 
-    # The filter params indicates parameters to overlook when deciding if a cached box
-    # matches current parameters.
-    # It is useful for ignoring certain global parameters which may not apply to this
-    # step or its dependents.
+        self.sync()
+        return self
+
+
+@attrs.define(slots=False, kw_only=True)
+class InitialConditions(OutputStruct):
+    """A class representing an InitialConditions C-struct."""
+
+    _c_compute_function = lib.ComputeInitialConditions
     _meta = False
-    _filter_params = _OutputStruct._filter_params + [
-        "ALPHA_UVB",  # ionization
-        "EVOLVE_DENSITY_LINEARLY",  # perturb
-        "SMOOTH_EVOLVED_DENSITY_FIELD",  # perturb
-        "R_smooth_density",  # perturb
-        "HII_ROUND_ERR",  # ionization
-        "FIND_BUBBLE_ALGORITHM",  # ib
-        "N_POISSON",  # ib
-        "T_USE_VELOCITIES",  # bt
-        "MAX_DVDR",  # bt
-        "DELTA_R_HII_FACTOR",  # ib
-        "HII_FILTER",  # ib
-        "INITIAL_REDSHIFT",  # pf
-        "HEAT_FILTER",  # st
-        "CLUMPING_FACTOR",  # st
-        "R_XLy_MAX",  # st
-        "NUM_FILTER_STEPS_FOR_Ts",  # ts
-        "TK_at_Z_HEAT_MAX",  # ts
-        "XION_at_Z_HEAT_MAX",  # ts
-        "Pop",  # ib
-        "Pop2_ion",  # ib
-        "Pop3_ion",  # ib
-        "NU_X_BAND_MAX",  # st
-        "NU_X_MAX",  # ib
-    ]
+    _compat_hash = _HashType.user_cosmo
+
+    lowres_density = _arrayfield()
+    lowres_vx = _arrayfield()
+    lowres_vy = _arrayfield()
+    lowres_vz = _arrayfield()
+    hires_density = _arrayfield()
+    hires_vx = _arrayfield()
+    hires_vy = _arrayfield()
+    hires_vz = _arrayfield()
+
+    lowres_vx_2LPT = _arrayfield(optional=True)
+    lowres_vy_2LPT = _arrayfield(optional=True)
+    lowres_vz_2LPT = _arrayfield(optional=True)
+    hires_vx_2LPT = _arrayfield(optional=True)
+    hires_vy_2LPT = _arrayfield(optional=True)
+    hires_vz_2LPT = _arrayfield(optional=True)
+
+    lowres_vcb = _arrayfield(optional=True)
+
+    @classmethod
+    def new(cls, inputs: InputParameters, **kw) -> Self:
+        """Create a new instance, given a set of input parameters."""
+        shape = (inputs.user_params.HII_DIM,) * 2 + (
+            int(inputs.user_params.NON_CUBIC_FACTOR * inputs.user_params.HII_DIM),
+        )
+        hires_shape = (inputs.user_params.DIM,) * 2 + (
+            int(inputs.user_params.NON_CUBIC_FACTOR * inputs.user_params.DIM),
+        )
+
+        out = {
+            "lowres_density": Array(shape, dtype=np.float32),
+            "lowres_vx": Array(shape, dtype=np.float32),
+            "lowres_vy": Array(shape, dtype=np.float32),
+            "lowres_vz": Array(shape, dtype=np.float32),
+            "hires_density": Array(hires_shape, dtype=np.float32),
+            "hires_vx": Array(hires_shape, dtype=np.float32),
+            "hires_vy": Array(hires_shape, dtype=np.float32),
+            "hires_vz": Array(hires_shape, dtype=np.float32),
+        }
+
+        if inputs.user_params.USE_2LPT:
+            out |= {
+                "lowres_vx_2LPT": Array(shape, dtype=np.float32),
+                "lowres_vy_2LPT": Array(shape, dtype=np.float32),
+                "lowres_vz_2LPT": Array(shape, dtype=np.float32),
+                "hires_vx_2LPT": Array(hires_shape, dtype=np.float32),
+                "hires_vy_2LPT": Array(hires_shape, dtype=np.float32),
+                "hires_vz_2LPT": Array(hires_shape, dtype=np.float32),
+            }
+
+        if inputs.user_params.USE_RELATIVE_VELOCITIES:
+            out["lowres_vcb"] = Array(shape, dtype=np.float32)
+
+        return cls(inputs=inputs, **out, **kw)
 
     def prepare_for_perturb(self, flag_options: FlagOptions, force: bool = False):
         """Ensure the ICs have all the boxes loaded for perturb, but no extra."""
@@ -159,98 +573,82 @@ def prepare_for_spin_temp(self, flag_options: FlagOptions, force: bool = False):
             keep.append("lowres_vcb")
         self.prepare(keep=keep, force=force)
 
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        shape = (self.user_params.HII_DIM,) * 2 + (
-            int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),
-        )
-        hires_shape = (self.user_params.DIM,) * 2 + (
-            int(self.user_params.NON_CUBIC_FACTOR * self.user_params.DIM),
-        )
-
-        out = {
-            "lowres_density": shape,
-            "lowres_vx": shape,
-            "lowres_vy": shape,
-            "lowres_vz": shape,
-            "hires_density": hires_shape,
-            "hires_vx": hires_shape,
-            "hires_vy": hires_shape,
-            "hires_vz": hires_shape,
-        }
-
-        if self.user_params.USE_2LPT:
-            out.update(
-                {
-                    "lowres_vx_2LPT": shape,
-                    "lowres_vy_2LPT": shape,
-                    "lowres_vz_2LPT": shape,
-                    "hires_vx_2LPT": hires_shape,
-                    "hires_vy_2LPT": hires_shape,
-                    "hires_vz_2LPT": hires_shape,
-                }
-            )
-
-        if self.user_params.USE_RELATIVE_VELOCITIES:
-            out.update({"lowres_vcb": shape})
-
-        return out
-
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         return []
 
-    def compute(self, hooks: dict):
+    def compute(self, allow_already_computed: bool = False):
         """Compute the function."""
         return self._compute(
-            self.random_seed,
-            self.user_params,
-            self.cosmo_params,
-            hooks=hooks,
+            allow_already_computed,
+            self.inputs.random_seed,
+            self.inputs.user_params,
+            self.inputs.cosmo_params,
         )
 
 
-class PerturbedField(_OutputStructZ):
+@attrs.define(slots=False, kw_only=True)
+class OutputStructZ(OutputStruct):
+    """The same as an OutputStruct, but containing a redshift."""
+
+    _meta = True
+    redshift: float = attrs.field(converter=float)
+
+    @classmethod
+    def dummy(cls, inputs: InputParameters = InputParameters(random_seed=1)):
+        """Create a dummy instance with the given inputs."""
+        return cls.new(inputs=inputs, redshift=-1.0, dummy=True)
+
+    @classmethod
+    def initial(cls, inputs: InputParameters = InputParameters(random_seed=1)):
+        """Create a dummy instance with the given inputs."""
+        return cls.new(inputs=inputs, redshift=-1.0, initial=True)
+
+
+@attrs.define(slots=False, kw_only=True)
+class PerturbedField(OutputStructZ):
     """A class containing all perturbed field boxes."""
 
     _c_compute_function = lib.ComputePerturbField
-
     _meta = False
-    _filter_params = _OutputStruct._filter_params + [
-        "ALPHA_UVB",  # ionization
-        "HII_ROUND_ERR",  # ionization
-        "FIND_BUBBLE_ALGORITHM",  # ib
-        "N_POISSON",  # ib
-        "T_USE_VELOCITIES",  # bt
-        "MAX_DVDR",  # bt
-        "DELTA_R_HII_FACTOR",  # ib
-        "HII_FILTER",  # ib
-        "HEAT_FILTER",  # st
-        "CLUMPING_FACTOR",  # st
-        "R_XLy_MAX",  # st
-        "NUM_FILTER_STEPS_FOR_Ts",  # ts
-        "TK_at_Z_HEAT_MAX",  # ts
-        "XION_at_Z_HEAT_MAX",  # ts
-        "Pop",  # ib
-        "Pop2_ion",  # ib
-        "Pop3_ion",  # ib
-        "NU_X_BAND_MAX",  # st
-        "NU_X_MAX",  # ib
-    ]
-
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
+    _compat_hash = _HashType.zgrid
+
+    density = _arrayfield()
+    velocity_z = _arrayfield()
+    velocity_x = _arrayfield(optional=True)
+    velocity_y = _arrayfield(optional=True)
+
+    @classmethod
+    def new(cls, inputs: InputParameters, redshift: float, **kw) -> Self:
+        """Create a new PerturbedField instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`PerturbedField`
+        constructor.
+        """
+        dim = inputs.user_params.HII_DIM
+
+        shape = (dim, dim, int(inputs.user_params.NON_CUBIC_FACTOR * dim))
+
         out = {
-            "density": (self.user_params.HII_DIM,) * 2
-            + (int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),),
-            "velocity_z": (self.user_params.HII_DIM,) * 2
-            + (int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),),
+            "density": Array(shape, dtype=np.float32),
+            "velocity_z": Array(shape, dtype=np.float32),
         }
-        if self.user_params.KEEP_3D_VELOCITIES:
-            out["velocity_x"] = out["density"]
-            out["velocity_y"] = out["density"]
+        if inputs.user_params.KEEP_3D_VELOCITIES:
+            out["velocity_x"] = Array(shape, dtype=np.float32)
+            out["velocity_y"] = Array(shape, dtype=np.float32)
 
-        return out
+        return cls(redshift=redshift, inputs=inputs, **out, **kw)
 
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
 
@@ -283,14 +681,14 @@ def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
 
         return required
 
-    def compute(self, *, ics: InitialConditions, hooks: dict):
+    def compute(self, *, allow_already_computed: bool = False, ics: InitialConditions):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.redshift,
             self.user_params,
             self.cosmo_params,
             ics,
-            hooks=hooks,
         )
 
     @property
@@ -299,40 +697,112 @@ def velocity(self):
         return self.velocity_z  # for backwards compatibility
 
 
-class HaloField(_AllParamsBox):
+@attrs.define(slots=False, kw_only=True)
+class PerturbHaloField(OutputStructZ):
     """A class containing all fields related to halos."""
 
+    _c_compute_function = lib.ComputePerturbHaloField
     _meta = False
-    _inputs = _AllParamsBox._inputs + (
-        "desc_redshift",
-        "buffer_size",
-    )
-    _c_compute_function = lib.ComputeHaloField
+    _compat_hash = _HashType.zgrid
+
+    halo_masses = _arrayfield()
+    star_rng = _arrayfield()
+    sfr_rng = _arrayfield()
+    xray_rng = _arrayfield()
+    halo_coords = _arrayfield()
+    n_halos: int = attrs.field(default=None)
+    buffer_size: int = attrs.field(default=None)
+
+    @classmethod
+    def new(
+        cls, inputs: InputParameters, redshift: float, buffer_size: int = 0, **kw
+    ) -> Self:
+        """Create a new PerturbedHaloField instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`PerturbedHaloField`
+        constructor.
+        """
+        from .cfuncs import get_halo_list_buffer_size
+
+        buffer_size = get_halo_list_buffer_size(
+            redshift, inputs.user_params, inputs.cosmo_params
+        )
+
+        return cls(
+            inputs=inputs,
+            halo_masses=Array((buffer_size,), dtype=np.float32),
+            star_rng=Array((buffer_size,), dtype=np.float32),
+            sfr_rng=Array((buffer_size,), dtype=np.float32),
+            xray_rng=Array((buffer_size,), dtype=np.float32),
+            halo_coords=Array((buffer_size, 3), dtype=np.int32),
+            redshift=redshift,
+            buffer_size=buffer_size,
+            **kw,
+        )
+
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
+        """Return all input arrays required to compute this object."""
+        required = []
+        if isinstance(input_box, InitialConditions):
+            if self.user_params.PERTURB_ON_HIGH_RES:
+                required += ["hires_vx", "hires_vy", "hires_vz"]
+            else:
+                required += ["lowres_vx", "lowres_vy", "lowres_vz"]
+
+            if self.user_params.USE_2LPT:
+                required += [f"{k}_2LPT" for k in required]
+        elif isinstance(input_box, HaloField):
+            required += [
+                "halo_coords",
+                "halo_masses",
+                "star_rng",
+                "sfr_rng",
+                "xray_rng",
+            ]
+        else:
+            raise ValueError(
+                f"{type(input_box)} is not an input required for PerturbHaloField!"
+            )
 
-    def __init__(
+        return required
+
+    def compute(
         self,
         *,
-        desc_redshift: float | None = None,
-        buffer_size: int = 0,
-        **kwargs,
+        ics: InitialConditions,
+        halo_field: HaloField,
+        allow_already_computed: bool = False,
     ):
-        self.desc_redshift = desc_redshift
-        self.buffer_size = buffer_size
+        """Compute the function."""
+        return self._compute(
+            allow_already_computed,
+            self.redshift,
+            self.user_params,
+            self.cosmo_params,
+            self.astro_params,
+            self.flag_options,
+            ics,
+            halo_field,
+        )
 
-        super().__init__(**kwargs)
 
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        out = {
-            "halo_masses": (self.buffer_size,),
-            "star_rng": (self.buffer_size,),
-            "sfr_rng": (self.buffer_size,),
-            "xray_rng": (self.buffer_size,),
-            "halo_coords": (self.buffer_size, 3),
-        }
+@attrs.define(slots=False, kw_only=True)
+class HaloField(PerturbHaloField):
+    """A class containing all fields related to halos."""
 
-        return out
+    _c_compute_function = lib.ComputeHaloField
+    desc_redshift: float | None = attrs.field(default=None)
 
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
         if isinstance(input_box, InitialConditions):
@@ -360,10 +830,15 @@ def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
         return required
 
     def compute(
-        self, *, descendant_halos: HaloField, ics: InitialConditions, hooks: dict
+        self,
+        *,
+        descendant_halos: HaloField,
+        ics: InitialConditions,
+        allow_already_computed: bool = False,
     ):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.desc_redshift,
             self.redshift,
             self.user_params,
@@ -373,105 +848,66 @@ def compute(
             ics,
             ics.random_seed,
             descendant_halos,
-            hooks=hooks,
-        )
-
-
-class PerturbHaloField(_AllParamsBox):
-    """A class containing all fields related to halos."""
-
-    _c_compute_function = lib.ComputePerturbHaloField
-    _meta = False
-    _inputs = _AllParamsBox._inputs + ("buffer_size",)
-
-    def __init__(
-        self,
-        buffer_size: int = 0,
-        **kwargs,
-    ):
-        self.buffer_size = buffer_size
-        super().__init__(**kwargs)
-
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        out = {
-            "halo_masses": (self.buffer_size,),
-            "star_rng": (self.buffer_size,),
-            "sfr_rng": (self.buffer_size,),
-            "xray_rng": (self.buffer_size,),
-            "halo_coords": (self.buffer_size, 3),
-        }
-
-        return out
-
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
-        """Return all input arrays required to compute this object."""
-        required = []
-        if isinstance(input_box, InitialConditions):
-            if self.user_params.PERTURB_ON_HIGH_RES:
-                required += ["hires_vx", "hires_vy", "hires_vz"]
-            else:
-                required += ["lowres_vx", "lowres_vy", "lowres_vz"]
-
-            if self.user_params.USE_2LPT:
-                required += [k + "_2LPT" for k in required]
-        elif isinstance(input_box, HaloField):
-            required += [
-                "halo_coords",
-                "halo_masses",
-                "star_rng",
-                "sfr_rng",
-                "xray_rng",
-            ]
-        else:
-            raise ValueError(
-                f"{type(input_box)} is not an input required for PerturbHaloField!"
-            )
-
-        return required
-
-    def compute(self, *, ics: InitialConditions, halo_field: HaloField, hooks: dict):
-        """Compute the function."""
-        return self._compute(
-            self.redshift,
-            self.user_params,
-            self.cosmo_params,
-            self.astro_params,
-            self.flag_options,
-            ics,
-            halo_field,
-            hooks=hooks,
         )
 
 
-class HaloBox(_AllParamsBox):
+@attrs.define(slots=False, kw_only=True)
+class HaloBox(OutputStructZ):
     """A class containing all gridded halo properties."""
 
     _meta = False
     _c_compute_function = lib.ComputeHaloBox
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        shape = (self.user_params.HII_DIM,) * 2 + (
-            int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),
+    halo_mass = _arrayfield()
+    halo_stars = _arrayfield()
+    halo_stars_mini = _arrayfield()
+    count = _arrayfield()
+    halo_sfr = _arrayfield()
+    halo_sfr_mini = _arrayfield()
+    halo_xray = _arrayfield()
+    n_ion = _arrayfield()
+    whalo_sfr = _arrayfield()
+
+    log10_Mcrit_ACG_ave: float = attrs.field(default=None)
+    log10_Mcrit_MCG_ave: float = attrs.field(default=None)
+
+    @classmethod
+    def new(cls, inputs: InputParameters, redshift: float, **kw) -> Self:
+        """Create a new HaloBox instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`HaloBox`
+        constructor.
+        """
+        dim = inputs.user_params.HII_DIM
+        shape = (dim, dim, int(inputs.user_params.NON_CUBIC_FACTOR * dim))
+
+        return cls(
+            inputs=inputs,
+            redshift=redshift,
+            **{
+                "halo_mass": Array(shape, dtype=np.float32),
+                "halo_stars": Array(shape, dtype=np.float32),
+                "halo_stars_mini": Array(shape, dtype=np.float32),
+                "count": Array(shape, dtype=np.int32),
+                "halo_sfr": Array(shape, dtype=np.float32),
+                "halo_sfr_mini": Array(shape, dtype=np.float32),
+                "halo_xray": Array(shape, dtype=np.float32),
+                "n_ion": Array(shape, dtype=np.float32),
+                "whalo_sfr": Array(shape, dtype=np.float32),
+            },
+            **kw,
         )
 
-        out = {
-            "halo_mass": shape,
-            "halo_stars": shape,
-            "halo_stars_mini": shape,
-            "count": shape,
-            "halo_sfr": shape,
-            "halo_sfr_mini": shape,
-            "halo_xray": shape,
-            "n_ion": shape,
-            "whalo_sfr": shape,
-        }
-
-        return out
-
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
         if isinstance(input_box, PerturbHaloField):
@@ -511,10 +947,11 @@ def compute(
         perturbed_field: PerturbedField,
         previous_spin_temp: TsBox,
         previous_ionize_box: IonizedBox,
-        hooks: dict,
+        allow_already_computed: bool = False,
     ):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.redshift,
             self.user_params,
             self.cosmo_params,
@@ -525,47 +962,66 @@ def compute(
             pt_halos,
             previous_spin_temp,
             previous_ionize_box,
-            hooks=hooks,
         )
 
 
-class XraySourceBox(_AllParamsBox):
+@attrs.define(slots=False, kw_only=True)
+class XraySourceBox(OutputStructZ):
     """A class containing the filtered sfr grids."""
 
     _meta = False
     _c_compute_function = lib.UpdateXraySourceBox
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
+    filtered_sfr = _arrayfield()
+    filtered_sfr_mini = _arrayfield()
+    filtered_xray = _arrayfield()
+    mean_sfr = _arrayfield()
+    mean_sfr_mini = _arrayfield()
+    mean_log10_Mcrit_LW = _arrayfield()
+
+    @classmethod
+    def new(cls, inputs: InputParameters, redshift: float, **kw) -> Self:
+        """Create a new XraySourceBox instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`XraySourceBox`
+        constructor.
+        """
         shape = (
             (global_params.NUM_FILTER_STEPS_FOR_Ts,)
-            + (self.user_params.HII_DIM,) * 2
-            + (int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),)
+            + (inputs.user_params.HII_DIM,) * 2
+            + (int(inputs.user_params.NON_CUBIC_FACTOR * inputs.user_params.HII_DIM),)
         )
 
-        out = {
-            "filtered_sfr": shape,
-            "filtered_sfr_mini": shape,
-            "filtered_xray": shape,
-            "mean_sfr": (global_params.NUM_FILTER_STEPS_FOR_Ts,),
-            "mean_sfr_mini": (global_params.NUM_FILTER_STEPS_FOR_Ts,),
-            "mean_log10_Mcrit_LW": (global_params.NUM_FILTER_STEPS_FOR_Ts,),
-        }
-
-        return out
+        return cls(
+            inputs=inputs,
+            redshift=redshift,
+            filtered_sfr=Array(shape, dtype=np.float32),
+            filtered_sfr_mini=Array(shape, dtype=np.float32),
+            filtered_xray=Array(shape, dtype=np.float32),
+            mean_sfr=Array(shape),
+            mean_sfr_mini=Array((global_params.NUM_FILTER_STEPS_FOR_Ts,)),
+            mean_log10_Mcrit_LW=Array((global_params.NUM_FILTER_STEPS_FOR_Ts,)),
+            **kw,
+        )
 
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
-        if isinstance(input_box, HaloBox):
-            required += ["halo_sfr", "halo_xray"]
-            if self.flag_options.USE_MINI_HALOS:
-                required += ["halo_sfr_mini"]
-        else:
+        if not isinstance(input_box, HaloBox):
             raise ValueError(f"{type(input_box)} is not an input required for HaloBox!")
 
+        required += ["halo_sfr", "halo_xray"]
+        if self.flag_options.USE_MINI_HALOS:
+            required += ["halo_sfr_mini"]
         return required
 
     def compute(
@@ -575,10 +1031,11 @@ def compute(
         R_inner,
         R_outer,
         R_ct,
-        hooks: dict,
+        allow_already_computed: bool = False,
     ):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.user_params,
             self.cosmo_params,
             self.astro_params,
@@ -587,37 +1044,49 @@ def compute(
             R_inner,
             R_outer,
             R_ct,
-            hooks=hooks,
         )
 
 
-class TsBox(_AllParamsBox):
+@attrs.define(slots=False, kw_only=True)
+class TsBox(OutputStructZ):
     """A class containing all spin temperature boxes."""
 
     _c_compute_function = lib.ComputeTsBox
     _meta = False
-    _inputs = _AllParamsBox._inputs + ("prev_spin_redshift",)
-
-    def __init__(
-        self,
-        *,
-        prev_spin_redshift: float | None = None,
-        perturbed_field_redshift: float | None = None,
-        **kwargs,
-    ):
-        self.prev_spin_redshift = prev_spin_redshift
-        super().__init__(**kwargs)
 
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        shape = (self.user_params.HII_DIM,) * 2 + (
-            int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),
+    Ts_box = _arrayfield()
+    x_e_box = _arrayfield()
+    Tk_box = _arrayfield()
+    J_21_LW_box = _arrayfield()
+
+    @classmethod
+    def new(cls, inputs: InputParameters, redshift: float, **kw) -> Self:
+        """Create a new TsBox instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`TsBox`
+        constructor.
+        """
+        shape = (inputs.user_params.HII_DIM,) * 2 + (
+            int(inputs.user_params.NON_CUBIC_FACTOR * inputs.user_params.HII_DIM),
+        )
+        return cls(
+            inputs=inputs,
+            redshift=redshift,
+            Ts_box=Array(shape, dtype=np.float32),
+            x_e_box=Array(shape, dtype=np.float32),
+            Tk_box=Array(shape, dtype=np.float32),
+            J_21_LW_box=Array(shape, dtype=np.float32),
+            **kw,
         )
-        return {
-            "Ts_box": shape,
-            "x_e_box": shape,
-            "Tk_box": shape,
-            "J_21_LW_box": shape,
-        }
 
     @cached_property
     def global_Ts(self):
@@ -649,7 +1118,7 @@ def global_x_e(self):
         else:
             return np.mean(self.x_e_box)
 
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
         if isinstance(input_box, InitialConditions):
@@ -682,14 +1151,15 @@ def compute(
         cleanup: bool,
         perturbed_field: PerturbedField,
         xray_source_box: XraySourceBox,
-        prev_spin_temp,
+        prev_spin_temp: TsBox,
         ics: InitialConditions,
-        hooks: dict,
+        allow_already_computed: bool = False,
     ):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.redshift,
-            self.prev_spin_redshift,
+            prev_spin_temp.redshift,
             self.user_params,
             self.cosmo_params,
             self.astro_params,
@@ -700,35 +1170,58 @@ def compute(
             xray_source_box,
             prev_spin_temp,
             ics,
-            hooks=hooks,
         )
 
 
-class IonizedBox(_AllParamsBox):
+@attrs.define(slots=False, kw_only=True)
+class IonizedBox(OutputStructZ):
     """A class containing all ionized boxes."""
 
     _meta = False
     _c_compute_function = lib.ComputeIonizedBox
-    _inputs = _AllParamsBox._inputs + ("prev_ionize_redshift",)
 
-    def __init__(self, *, prev_ionize_redshift: float | None = None, **kwargs):
-        self.prev_ionize_redshift = prev_ionize_redshift
-        super().__init__(**kwargs)
-
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        if self.flag_options.USE_MINI_HALOS:
+    xH_box = _arrayfield()
+    Gamma12_box = _arrayfield()
+    MFP_box = _arrayfield()
+    z_re_box = _arrayfield()
+    dNrec_box = _arrayfield()
+    temp_kinetic_all_gas = _arrayfield()
+    Fcoll = _arrayfield()
+    Fcoll_MINI = _arrayfield(optional=True)
+    log10_Mturnover_ave: float = attrs.field(default=None)
+    log10_Mturnover_MINI_ave: float = attrs.field(default=None)
+    mean_f_coll: float = attrs.field(default=None)
+    mean_f_coll_MINI: float = attrs.field(default=None)
+
+    @classmethod
+    def new(cls, inputs, redshift: float, **kw) -> Self:
+        """Create a new IonizedBox instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`IonizedBox`
+        constructor.
+        """
+        if inputs.flag_options.USE_MINI_HALOS:
             n_filtering = (
                 int(
                     np.log(
                         min(
-                            self.astro_params.R_BUBBLE_MAX,
-                            0.620350491 * self.user_params.BOX_LEN,
+                            inputs.astro_params.R_BUBBLE_MAX,
+                            0.620350491 * inputs.user_params.BOX_LEN,
                         )
                         / max(
                             global_params.R_BUBBLE_MIN,
                             0.620350491
-                            * self.user_params.BOX_LEN
-                            / self.user_params.HII_DIM,
+                            * inputs.user_params.BOX_LEN
+                            / inputs.user_params.HII_DIM,
                         )
                     )
                     / np.log(global_params.DELTA_R_HII_FACTOR)
@@ -738,25 +1231,25 @@ def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
         else:
             n_filtering = 1
 
-        shape = (self.user_params.HII_DIM,) * 2 + (
-            int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),
+        shape = (inputs.user_params.HII_DIM,) * 2 + (
+            int(inputs.user_params.NON_CUBIC_FACTOR * inputs.user_params.HII_DIM),
         )
         filter_shape = (n_filtering,) + shape
 
         out = {
-            "xH_box": {"init": np.ones, "shape": shape},
-            "Gamma12_box": shape,
-            "MFP_box": shape,
-            "z_re_box": shape,
-            "dNrec_box": shape,
-            "temp_kinetic_all_gas": shape,
-            "Fcoll": filter_shape,
+            "xH_box": Array(shape, initfunc=np.ones, dtype=np.float32),
+            "Gamma12_box": Array(shape, dtype=np.float32),
+            "MFP_box": Array(shape, dtype=np.float32),
+            "z_re_box": Array(shape, dtype=np.float32),
+            "dNrec_box": Array(shape, dtype=np.float32),
+            "temp_kinetic_all_gas": Array(shape, dtype=np.float32),
+            "Fcoll": Array(filter_shape, dtype=np.float32),
         }
 
-        if self.flag_options.USE_MINI_HALOS:
-            out["Fcoll_MINI"] = filter_shape
+        if inputs.flag_options.USE_MINI_HALOS:
+            out["Fcoll_MINI"] = Array(filter_shape, dtype=np.float32)
 
-        return out
+        return cls(inputs=inputs, redshift=redshift, **out, **kw)
 
     @cached_property
     def global_xH(self):
@@ -768,7 +1261,7 @@ def global_xH(self):
         else:
             return np.mean(self.xH_box)
 
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
         if isinstance(input_box, InitialConditions):
@@ -783,13 +1276,13 @@ def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
             required += ["J_21_LW_box", "x_e_box", "Tk_box"]
         elif isinstance(input_box, IonizedBox):
             required += ["z_re_box", "Gamma12_box"]
-            if self.flag_options.INHOMO_RECO:
+            if self.inputs.flag_options.INHOMO_RECO:
                 required += [
                     "dNrec_box",
                 ]
             if (
-                self.flag_options.USE_MASS_DEPENDENT_ZETA
-                and self.flag_options.USE_MINI_HALOS
+                self.inputs.flag_options.USE_MASS_DEPENDENT_ZETA
+                and self.inputs.flag_options.USE_MINI_HALOS
             ):
                 required += ["Fcoll", "Fcoll_MINI"]
         elif isinstance(input_box, HaloBox):
@@ -810,39 +1303,61 @@ def compute(
         spin_temp: TsBox,
         halobox: HaloBox,
         ics: InitialConditions,
-        hooks: dict,
+        allow_already_computed: bool = False,
     ):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.redshift,
-            self.prev_ionize_redshift,
-            self.user_params,
-            self.cosmo_params,
-            self.astro_params,
-            self.flag_options,
+            prev_perturbed_field.redshift,
+            self.inputs.user_params,
+            self.inputs.cosmo_params,
+            self.inputs.astro_params,
+            self.inputs.flag_options,
             perturbed_field,
             prev_perturbed_field,
             prev_ionize_box,
             spin_temp,
             halobox,
             ics,
-            hooks=hooks,
         )
 
 
-class BrightnessTemp(_AllParamsBox):
+@attrs.define(slots=False, kw_only=True)
+class BrightnessTemp(OutputStructZ):
     """A class containing the brightness temperature box."""
 
     _c_compute_function = lib.ComputeBrightnessTemp
 
     _meta = False
-    _filter_params = _OutputStructZ._filter_params
+    brightness_temp = _arrayfield()
+
+    @classmethod
+    def new(cls, inputs: InputParameters, redshift: float, **kw) -> Self:
+        """Create a new BrightnessTemp instance with the given inputs.
+
+        Parameters
+        ----------
+        inputs : InputParameters
+            The input parameters defining the output struct.
+        redshift : float
+            The redshift at which to compute fields.
+
+        Other Parameters
+        ----------------
+        All other parameters are passed through to the :class:`BrightnessTemp`
+        constructor.
+        """
+        shape = (inputs.user_params.HII_DIM,) * 2 + (
+            int(inputs.user_params.NON_CUBIC_FACTOR * inputs.user_params.HII_DIM),
+        )
 
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        return {
-            "brightness_temp": (self.user_params.HII_DIM,) * 2
-            + (int(self.user_params.NON_CUBIC_FACTOR * self.user_params.HII_DIM),)
-        }
+        return cls(
+            inputs=inputs,
+            redshift=redshift,
+            brightness_temp=Array(shape, dtype=np.float32),
+            **kw,
+        )
 
     @cached_property
     def global_Tb(self):
@@ -854,12 +1369,12 @@ def global_Tb(self):
         else:
             return np.mean(self.brightness_temp)
 
-    def get_required_input_arrays(self, input_box: _BaseOutputStruct) -> list[str]:
+    def get_required_input_arrays(self, input_box: OutputStruct) -> list[str]:
         """Return all input arrays required to compute this object."""
         required = []
         if isinstance(input_box, PerturbedField):
             required += ["density"]
-            if self.flag_options.APPLY_RSDS:
+            if self.inputs.flag_options.APPLY_RSDS:
                 required += ["velocity_z"]
         elif isinstance(input_box, TsBox):
             required += ["Ts_box"]
@@ -878,17 +1393,17 @@ def compute(
         spin_temp: TsBox,
         ionized_box: IonizedBox,
         perturbed_field: PerturbedField,
-        hooks: dict,
+        allow_already_computed: bool = False,
     ):
         """Compute the function."""
         return self._compute(
+            allow_already_computed,
             self.redshift,
-            self.user_params,
-            self.cosmo_params,
-            self.astro_params,
-            self.flag_options,
+            self.inputs.user_params,
+            self.inputs.cosmo_params,
+            self.inputs.astro_params,
+            self.inputs.flag_options,
             spin_temp,
             ionized_box,
             perturbed_field,
-            hooks=hooks,
         )
diff --git a/src/py21cmfast/wrapper/photoncons.py b/src/py21cmfast/wrapper/photoncons.py
index 6b7f0741c..2dfe8c16b 100644
--- a/src/py21cmfast/wrapper/photoncons.py
+++ b/src/py21cmfast/wrapper/photoncons.py
@@ -2,43 +2,30 @@
 Module for the photon conservation models.
 
 The excursion set reionisation model applied in ionize_box does not conserve photons.
-as a result there is an offset between the expected global ionized fraction and the value
+As a result there is an offset between the expected global ionized fraction and the value
 calculated from the ionized bubble maps. These models apply approximate corrections in order
 to bring the bubble maps more in line with the expected global values.
 
-The application of the model is controlled by the flag option PHOTON_CONS_TYPE, which can take 4 values:
-
-0: No correction is applied
-
-1: We use the ionizing emissivity grids from a different redshift when calculating ionized bubble maps, this
-    mapping from one redshift to another is obtained by performing a calibration simulation and measuring its
-    redshift difference with the expected global evolution.
-
-2: The power-law slope of the ionizing escape fraction is adjusted, using a fit ALPHA_ESC -> X + Y*Q(z), where Q is the
-    expected global ionized fraction. This relation is fit by performing a calibration simulation as in (1), and
-    comparing it to a range of expected global histories with different power-law slopes
-
-3: The normalisation of the ionizing escape fraction is adjusted, using a fit F_ESC10 -> X + Y*Q(z), where Q is the
-    expected global ionized fraction. This relation is fit by performing a calibration simulation as in (1), and
-    taking its ratio with the expected global evolution
-
-"""
-
-import logging
-import numpy as np
-from copy import deepcopy
-from scipy.optimize import curve_fit
-
-from ..c_21cmfast import ffi, lib
-from ._utils import _process_exitcode
-from .inputs import AstroParams, CosmoParams, FlagOptions, UserParams, global_params
-
-logger = logging.getLogger(__name__)
-
-
-"""
-NOTES:
-    The function map for the photon conservation model looks like:
+The application of the model is controlled by the flag option PHOTON_CONS_TYPE, which
+can take 4 values:
+
+0. No correction is applied
+1. We use the ionizing emissivity grids from a different redshift when calculating
+   ionized bubble maps, this mapping from one redshift to another is obtained by
+   performing a calibration simulation and measuring its redshift difference with the
+   expected global evolution.
+2. The power-law slope of the ionizing escape fraction is adjusted, using a fit
+   ALPHA_ESC -> X + Y*Q(z), where Q is the expected global ionized fraction. This
+   relation is fit by performing a calibration simulation as in (1), and comparing it to
+   a range of expected global histories with different power-law slopes.
+3. The normalisation of the ionizing escape fraction is adjusted, using a fit
+   F_ESC10 -> X + Y*Q(z), where Q is the expected global ionized fraction. This relation
+   is fit by performing a calibration simulation as in (1), and taking its ratio with
+   the expected global evolution.
+
+Notes
+-----
+The function map for the photon conservation model looks like::
 
     wrapper.run_lightcone/coeval()
         setup_photon_cons_correction()
@@ -62,6 +49,25 @@
 
 """
 
+import logging
+import numpy as np
+from scipy.optimize import curve_fit
+
+from ..c_21cmfast import ffi, lib
+from ..drivers._param_config import check_consistency_of_outputs_with_inputs
+from ._utils import _process_exitcode
+from .inputs import (
+    AstroParams,
+    CosmoParams,
+    FlagOptions,
+    InputParameters,
+    UserParams,
+    global_params,
+)
+from .outputs import InitialConditions
+
+logger = logging.getLogger(__name__)
+
 
 def _init_photon_conservation_correction(
     *, user_params=None, cosmo_params=None, astro_params=None, flag_options=None
@@ -106,7 +112,7 @@ def _calc_zstart_photon_cons():
     return _call_c_simple(lib.ComputeZstart_PhotonCons)
 
 
-def _get_photon_nonconservation_data():
+def _get_photon_nonconservation_data() -> dict:
     """
     Access C global data representing the photon-nonconservation corrections.
 
@@ -128,7 +134,7 @@ def _get_photon_nonconservation_data():
     """
     # Check if photon conservation has been initialised at all
     if not lib.photon_cons_allocated:
-        return None
+        return {}
 
     arbitrary_large_size = 2000
 
@@ -186,14 +192,9 @@ def _get_photon_nonconservation_data():
 
 
 def setup_photon_cons(
-    inputs,
-    regenerate,
-    hooks,
-    direc,
-    initial_conditions=None,
-    user_params=None,
-    cosmo_params=None,
-    **global_kwargs,
+    inputs: InputParameters | None = None,
+    initial_conditions: InitialConditions | None = None,
+    **kwargs,
 ):
     r"""
     Set up the photon non-conservation correction.
@@ -208,45 +209,34 @@ def setup_photon_cons(
 
     Parameters
     ----------
-    user_params : `~UserParams`, optional
-        Defines the overall options and parameters of the run.
-    astro_params : :class:`~AstroParams`, optional
-        Defines the astrophysical parameters of the run.
-    cosmo_params : :class:`~CosmoParams`, optional
-        Defines the cosmological parameters used to compute initial conditions.
-    flag_options: :class:`~FlagOptions`, optional
-        Options concerning how the reionization process is run, eg. if spin temperature
-        fluctuations are required.
+    inputs
+        An InputParameters instance.
     initial_conditions : :class:`~InitialConditions`, optional
-        If given, the user and cosmo params will be set from this object, and it will not be
+        If given, the `inputs` will be set from this object, and it will not be
         re-calculated.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Other Parameters
     ----------------
-    regenerate, write
-        See docs of :func:`initial_conditions` for more information.
+    Any other parameters able to be passed to :func:`compute_initial_conditions`.
     """
-    from ..drivers.single_field import _get_config_options
-
-    direc, regenerate, hooks = _get_config_options(direc, regenerate, None, hooks)
+    if inputs is None:
+        if initial_conditions is None:
+            raise ValueError(
+                "At least one of 'inputs' or 'initial_conditions' must be provided."
+            )
+        else:
+            inputs = initial_conditions.inputs
 
     if inputs.flag_options.PHOTON_CONS_TYPE == "no-photoncons":
-        return
+        return {}
 
-    inputs.check_output_compatibility([initial_conditions])
+    check_consistency_of_outputs_with_inputs(inputs, [initial_conditions])
 
     # calculate global and calibration simulation xH histories and save them in C
     calibrate_photon_cons(
         inputs=inputs,
-        regenerate=regenerate,
-        hooks=hooks,
-        direc=direc,
         initial_conditions=initial_conditions,
-        **global_kwargs,
+        **kwargs,
     )
 
     # The PHOTON_CONS_TYPE == 1 case is handled in C (for now....), but we get the data anyway
@@ -273,127 +263,100 @@ def setup_photon_cons(
 
 
 def calibrate_photon_cons(
-    inputs,
-    regenerate,
-    hooks,
-    direc,
-    initial_conditions,
-    **global_kwargs,
+    inputs: InputParameters,
+    initial_conditions: InitialConditions | None,
+    **kwargs,
 ):
     r"""
-    Performs the photon conservation calibration simulation.
+    Perform a photon conservation calibration simulation.
 
     Parameters
     ----------
-    user_params : `~UserParams`, optional
-        Defines the overall options and parameters of the run.
-    astro_params : :class:`~AstroParams`, optional
-        Defines the astrophysical parameters of the run.
-    cosmo_params : :class:`~CosmoParams`, optional
-        Defines the cosmological parameters used to compute initial conditions.
-    flag_options: :class:`~FlagOptions`, optional
-        Options concerning how the reionization process is run, eg. if spin temperature
-        fluctuations are required.
+    inputs
+        An InputParameters instance.
     initial_conditions : :class:`~InitialConditions`, optional
-        If given, the user and cosmo params will be set from this object, and it will not be
+        If given, the `inputs` will be set from this object, and it will not be
         re-calculated.
-    \*\*global_kwargs :
-        Any attributes for :class:`~py21cmfast.inputs.GlobalParams`. This will
-        *temporarily* set global attributes for the duration of the function. Note that
-        arguments will be treated as case-insensitive.
 
     Other Parameters
     ----------------
-    regenerate, write
-        See docs of :func:`initial_conditions` for more information.
+    See docs of :func:`compute_initial_conditions` for more information.
     """
     # avoiding circular imports by importing here
     from ..drivers.single_field import compute_ionization_field, perturb_field
 
-    with global_params.use(**global_kwargs):
-        # Create a new astro_params and flag_options just for the photon_cons correction
-        inputs_calibration = inputs.evolve_input_structs(
-            USE_TS_FLUCT=False,
-            INHOMO_RECO=False,
-            USE_MINI_HALOS=False,
-            USE_HALO_FIELD=False,
-            PHOTON_CONS_TYPE="no-photoncons",
-        )
-        ib = None
-        prev_perturb = None
+    # Create a new astro_params and flag_options just for the photon_cons correction
+    inputs_calibration = inputs.evolve_input_structs(
+        USE_TS_FLUCT=False,
+        INHOMO_RECO=False,
+        USE_MINI_HALOS=False,
+        USE_HALO_FIELD=False,
+        PHOTON_CONS_TYPE="no-photoncons",
+    )
 
-        # Arrays for redshift and neutral fraction for the calibration curve
-        z_for_photon_cons = []
-        neutral_fraction_photon_cons = []
+    # Arrays for redshift and neutral fraction for the calibration curve
+    neutral_fraction_photon_cons = []
 
-        # Initialise the analytic expression for the reionisation history
-        logger.info("About to start photon conservation correction")
-        _init_photon_conservation_correction(
-            user_params=inputs.user_params,
-            cosmo_params=inputs.cosmo_params,
-            astro_params=inputs.astro_params,
-            flag_options=inputs.flag_options,
-        )
+    # Initialise the analytic expression for the reionisation history
+    logger.info("About to start photon conservation correction")
+    _init_photon_conservation_correction(
+        user_params=inputs.user_params,
+        cosmo_params=inputs.cosmo_params,
+        astro_params=inputs.astro_params,
+        flag_options=inputs.flag_options,
+    )
 
-        # Determine the starting redshift to start scrolling through to create the
-        # calibration reionisation history
-        logger.info("Calculating photon conservation zstart")
-        z = _calc_zstart_photon_cons()
-
-        while z > global_params.PhotonConsEndCalibz:
-            # Determine the ionisation box with recombinations, spin temperature etc.
-            # turned off.
-            this_perturb = perturb_field(
-                redshift=z,
-                inputs=inputs_calibration,
-                initial_conditions=initial_conditions,
-                regenerate=regenerate,
-                hooks=hooks,
-                direc=direc,
-            )
+    # Determine the starting redshift to start scrolling through to create the
+    # calibration reionisation history
+    logger.info("Calculating photon conservation zstart")
+    z = _calc_zstart_photon_cons()
 
-            ib2 = compute_ionization_field(
-                redshift=z,
-                inputs=inputs_calibration,
-                previous_ionize_box=ib,
-                initial_conditions=initial_conditions,
-                perturbed_field=this_perturb,
-                previous_perturbed_field=prev_perturb,
-                spin_temp=None,
-                regenerate=regenerate,
-                hooks=hooks,
-                direc=direc,
-            )
+    fast_node_redshifts = [z]
+
+    inputs_calibration = inputs_calibration.clone(node_redshifts=fast_node_redshifts)
+
+    while z > global_params.PhotonConsEndCalibz:
+        # Determine the ionisation box with recombinations, spin temperature etc.
+        # turned off.
+        this_perturb = perturb_field(
+            redshift=z,
+            inputs=inputs_calibration,
+            initial_conditions=initial_conditions,
+            **kwargs,
+        )
 
-            mean_nf = np.mean(ib2.xH_box)
-
-            # Save mean/global quantities
-            neutral_fraction_photon_cons.append(mean_nf)
-            z_for_photon_cons.append(z)
-
-            # Can speed up sampling in regions where the evolution is slower
-            if 0.3 < mean_nf <= 0.9:
-                z -= 0.15
-            elif 0.01 < mean_nf <= 0.3:
-                z -= 0.05
-            else:
-                z -= 0.5
-
-            ib = ib2
-            if inputs.flag_options.USE_MINI_HALOS:
-                prev_perturb = this_perturb
-
-        z_for_photon_cons = np.array(z_for_photon_cons[::-1])
-        neutral_fraction_photon_cons = np.array(neutral_fraction_photon_cons[::-1])
-
-        # Construct the spline for the calibration curve
-        logger.info("Calibrating photon conservation correction")
-        _calibrate_photon_conservation_correction(
-            redshifts_estimate=z_for_photon_cons,
-            nf_estimate=neutral_fraction_photon_cons,
-            NSpline=len(z_for_photon_cons),
+        ib2 = compute_ionization_field(
+            initial_conditions=initial_conditions,
+            perturbed_field=this_perturb,
+            **kwargs,
         )
 
+        mean_nf = np.mean(ib2.get("xH_box"))
+
+        # Can speed up sampling in regions where the evolution is slower
+        if 0.3 < mean_nf <= 0.9:
+            z -= 0.15
+        elif 0.01 < mean_nf <= 0.3:
+            z -= 0.05
+        else:
+            z -= 0.5
+
+        fast_node_redshifts.append(z)
+
+        # Save mean/global quantities
+        neutral_fraction_photon_cons.append(mean_nf)
+
+    fast_node_redshifts = np.array(fast_node_redshifts[::-1])
+    neutral_fraction_photon_cons = np.array(neutral_fraction_photon_cons[::-1])
+
+    # Construct the spline for the calibration curve
+    logger.info("Calibrating photon conservation correction")
+    _calibrate_photon_conservation_correction(
+        redshifts_estimate=fast_node_redshifts,
+        nf_estimate=neutral_fraction_photon_cons,
+        NSpline=len(fast_node_redshifts),
+    )
+
 
 # (Jdavies): I needed a function to access the delta z from the wrapper
 # get_photoncons_data does not have the edge cases that adjust_redshifts_for_photoncons does
diff --git a/src/py21cmfast/wrapper/structs.py b/src/py21cmfast/wrapper/structs.py
index 1657770b8..434e9c772 100644
--- a/src/py21cmfast/wrapper/structs.py
+++ b/src/py21cmfast/wrapper/structs.py
@@ -4,28 +4,13 @@
 
 import attrs
 import contextlib
-import h5py
 import logging
-import numpy as np
-import warnings
-from abc import ABCMeta, abstractmethod
 from bidict import bidict
-from functools import cached_property
-from hashlib import md5
-from pathlib import Path
-from typing import Any, Sequence
+from typing import Any
 
 from .. import __version__
-from .._cfg import config
-from ..c_21cmfast import ffi, lib
-from ._utils import (
-    asarray,
-    float_to_string_precision,
-    get_all_subclasses,
-    snake_to_camel,
-)
-from .arraystate import ArrayState
-from .exceptions import _process_exitcode
+from ..c_21cmfast import ffi
+from .arrays import Array
 
 logger = logging.getLogger(__name__)
 
@@ -46,6 +31,8 @@ class StructWrapper:
     cstruct = attrs.field(default=None)
     _ffi = attrs.field(default=ffi)
 
+    _TYPEMAP = bidict({"float32": "float *", "float64": "double *", "int32": "int *"})
+
     @_name.default
     def _name_default(self):
         return self.__class__.__name__
@@ -90,1109 +77,20 @@ def __getstate__(self):
             if k not in ["_strings", "cstruct", "_ffi"]
         }
 
+    def expose_to_c(self, array: Array, name: str):
+        """Expose the memory of a particular Array to the backend C code."""
+        if not array.state.initialized:
+            raise ValueError("Array must be initialized before exposing to C")
 
-@attrs.define(frozen=True, kw_only=True)
-class InputStruct:
-    """
-    A convenient interface to create a C structure with defaults specified.
-
-    It is provided for the purpose of *creating* C structures in Python to be passed to
-    C functions, where sensible defaults are available. Structures which are created
-    within C and passed back do not need to be wrapped.
-
-    This provides a *fully initialised* structure, and will fail if not all fields are
-    specified with defaults.
-
-    .. note:: The actual C structure is gotten by calling an instance. This is
-              auto-generated when called, based on the parameters in the class.
-
-    .. warning:: This class will *not* deal well with parameters of the struct which are
-                 pointers. All parameters should be primitive types, except for strings,
-                 which are dealt with specially.
-
-    Parameters
-    ----------
-    ffi : cffi object
-        The ffi object from any cffi-wrapped library.
-    """
-
-    _write_exclude_fields = ()
-
-    @classmethod
-    def new(cls, x: dict | InputStruct | None = None, **kwargs):
-        """
-        Create a new instance of the struct.
-
-        Parameters
-        ----------
-        x : dict | InputStruct | None
-            Initial values for the struct. If `x` is a dictionary, it should map field
-            names to their corresponding values. If `x` is an instance of this class,
-            its attributes will be used as initial values. If `x` is None, the
-            struct will be initialised with default values.
-        """
-        if isinstance(x, dict):
-            return cls(**x, **kwargs)
-        elif isinstance(x, InputStruct):
-            return x.clone(**kwargs)
-        elif x is None:
-            return cls(**kwargs)
-        else:
-            raise ValueError(
-                f"Cannot instantiate {cls.__name__} with type {x.__class__}"
-            )
-
-    @cached_property
-    def struct(self) -> StructWrapper:
-        """The python-wrapped struct associated with this input object."""
-        return StructWrapper(self.__class__.__name__)
-
-    @cached_property
-    def cstruct(self) -> StructWrapper:
-        """The object pointing to the memory accessed by C-code for this struct."""
-        cdict = self.cdict
-        for k in self.struct.fieldnames:
-            val = cdict[k]
-
-            if isinstance(val, str):
-                # If it is a string, need to convert it to C string ourselves.
-                val = self.ffi.new("char[]", val.encode())
-
-            setattr(self.struct.cstruct, k, val)
-
-        return self.struct.cstruct
-
-    def clone(self, **kwargs):
-        """Make a fresh copy of the instance with arbitrary parameters updated."""
-        return attrs.evolve(self, **kwargs)
-
-    def asdict(self) -> dict:
-        """Return a dict representation of the instance.
-
-        Examples
-        --------
-        This dict should be such that doing the following should work, i.e. it can be
-        used exactly to construct a new instance of the same object::
-
-        >>> inp = InputStruct(**params)
-        >>> newinp =InputStruct(**inp.asdict())
-        >>> inp == newinp
-        """
-        return attrs.asdict(self)
-
-    @cached_property
-    def cdict(self) -> dict:
-        """A python dictionary containing the properties of the wrapped C-struct.
-
-        The memory pointed to by this dictionary is *not* owned by the wrapped C-struct,
-        but is rather just a python dict. However, in contrast to :meth:`asdict`, this
-        method transforms the properties to what they should be in C (e.g. linear space
-        vs. log-space) before putting them into the dict.
-
-        This dict also contains *only* the properties of the wrapped C-struct, rather
-        than all properties of the :class:`InputStruct` instance (some attributes of the
-        python instance are there only to guide setting of defaults, and don't appear
-        in the C-struct at all).
-        """
-        fields = attrs.fields(self.__class__)
-        transformers = {
-            field.name: field.metadata.get("transformer", None) for field in fields
-        }
-
-        out = {}
-        for k in self.struct.fieldnames:
-            val = getattr(self, k)
-            # we assume properties (as opposed to attributes) are already converted
-            trns = transformers[k] if k in transformers.keys() else None
-            out[k] = val if trns is None else trns(val)
-        return out
-
-    def __str__(self):
-        """Human-readable string representation of the object."""
-        d = self.asdict()
-        biggest_k = max(len(k) for k in d)
-        params = "\n    ".join(sorted(f"{k:<{biggest_k}}: {v}" for k, v in d.items()))
-        return f"""{self.__class__.__name__}:{params} """
-
-    @classmethod
-    def from_subdict(cls, dct, safe=True):
-        """Construct an instance of a parameter structure from a dictionary."""
-        fieldnames = [
-            field.name
-            for field in attrs.fields(cls)
-            if field.eq and field.default is not None
-        ]
-        if set(fieldnames) != set(dct.keys()):
-            missing_items = [
-                (field.name, field.default)
-                for field in attrs.fields(cls)
-                if field.name not in dct.keys() and field.name in fieldnames
-            ]
-            extra_items = [(k, v) for k, v in dct.items() if k not in fieldnames]
-            message = (
-                f"There are extra or missing {cls.__name__} in the file to be read.\n"
-                f"EXTRAS: {extra_items}\n"
-                f"MISSING: {missing_items}\n"
-            )
-            if safe:
-                raise ValueError(
-                    message
-                    + "set `safe=False` to load structures from previous versions"
-                )
-            else:
-                warnings.warn(
-                    message
-                    + "\nExtras are ignored and missing are set to default (shown) values."
-                    + "\nUsing these parameter structures in further computation will give inconsistent results."
-                )
-            dct = {k: v for k, v in dct.items() if k in fieldnames}
-
-        return cls.new(dct)
-
-
-class OutputStruct(metaclass=ABCMeta):
-    """Base class for any class that wraps a C struct meant to be output from a C function."""
-
-    _meta = True
-    _fields_ = []
-    _global_params = None
-    _inputs = (
-        "user_params",
-        "cosmo_params",
-        "random_seed",
-    )  # inputs provided in the InputParameter class
-    _filter_params = [
-        "external_table_path",
-        "wisdoms_path",
-        "_flag_options",
-        "_base_cosmo",
-    ]
-    _c_based_pointers = ()
-    _c_compute_function = None
-
-    _TYPEMAP = bidict({"float32": "float *", "float64": "double *", "int32": "int *"})
-
-    def __init__(self, *, dummy=False, initial=False, **kwargs):
-        """
-        Base type for output structures from C functions.
-
-        Parameters
-        ----------
-        random_seed
-            Seed associated with the output.
-        dummy
-            Specify this as a dummy struct, in which no arrays are to be
-            initialized or computed.
-        initial
-            Specify this as an initial struct, where arrays are to be
-            initialized, but do not need to be computed to pass into another
-            struct's compute().
-        """
-        self._name = self.__class__.__name__
-        self.version = ".".join(__version__.split(".")[:2])
-        self.patch_version = ".".join(__version__.split(".")[2:])
-        self._paths = []
-
-        for k in self._inputs:
-            if k not in self.__dict__:
-                try:
-                    setattr(self, k, kwargs.pop(k))
-                except KeyError as e:
-                    raise KeyError(
-                        f"{self.__class__.__name__} requires the keyword argument {k}"
-                    ) from e
-                if getattr(self, k) is None:
-                    raise KeyError(
-                        f"{self.__class__.__name__} has required input {k} == None"
-                    )
-
-        if kwargs:
-            warnings.warn(
-                f"{self.__class__.__name__} received the following unexpected "
-                f"arguments: {list(kwargs.keys())}, these are ignored."
-            )
-
-        self.dummy = dummy
-        self.initial = initial
-
-        self._array_structure = self._get_box_structures()
-        self._array_state = {k: ArrayState() for k in self._array_structure} | {
-            k: ArrayState() for k in self._c_based_pointers
-        }
-        for k in self._array_structure:
-            if k not in self.struct.pointer_fields:
-                raise TypeError(f"Key {k} in {self} not a defined pointer field in C.")
-
-    @cached_property
-    def struct(self) -> StructWrapper:
-        """The python-wrapped struct associated with this input object."""
-        return StructWrapper(self._name)
-
-    @cached_property
-    def cstruct(self) -> StructWrapper:
-        """The object pointing to the memory accessed by C-code for this struct."""
-        self._init_cstruct()
-        return self.struct.cstruct
-
-    @property
-    def _all_inputs(self):
-        return self._inputs + ("_global_params",)
-
-    @property
-    def path(self) -> tuple[None, Path]:
-        """The path to an on-disk version of this object."""
-        if not self._paths:
-            return None
-
-        for pth in self._paths:
-            if pth.exists():
-                return pth
-
-        logger.info(f"All paths that defined {self} have been deleted on disk.")
-        return None
-
-    @abstractmethod
-    def _get_box_structures(self) -> dict[str, dict | tuple[int]]:
-        """Return a dictionary of names mapping to shapes for each array in the struct.
-
-        The reason this is a function, not a simple attribute, is that we may need to
-        decide on what arrays need to be initialized based on the inputs (eg. if USE_2LPT
-        is True or False).
-
-        Each actual OutputStruct subclass needs to implement this. Note that the arrays
-        are not actually initialized here -- that's done automatically by :func:`_init_arrays`
-        using this information. This function means that the names of the actually required
-        arrays can be accessed without doing any actual initialization.
-
-        Note also that this only contains arrays allocated *by Python* not C. Arrays
-        allocated by C are specified in :func:`_c_shape`.
-        """
-        pass
-
-    def _c_shape(self, cstruct) -> dict[str, tuple[int]]:
-        """Return a dictionary of field: shape for arrays allocated within C."""
-        return {}
-
-    @classmethod
-    def _implementations(cls):
-        all_classes = get_all_subclasses(cls)
-        return [c for c in all_classes if not c._meta]
-
-    def _init_arrays(self):
-        for k, state in self._array_state.items():
-            # Don't initialize C-based pointers or already-inited stuff, or stuff
-            # that's computed on disk (if it's on disk, accessing the array should
-            # just give the computed version, which is what we would want, not a
-            # zero-inited array).
-            if k in self._c_based_pointers or state.initialized or state.on_disk:
-                continue
-
-            params = self._array_structure[k]
-            tp = self._TYPEMAP.inverse[self.struct.fields[k].type.cname]
-
-            if isinstance(params, tuple):
-                shape = params
-                fnc = np.zeros
-            elif isinstance(params, dict):
-                fnc = params.get("init", np.zeros)
-                shape = params.get("shape")
-            else:
-                raise ValueError("params is not a tuple or dict")
-
-            setattr(self, k, fnc(shape, dtype=tp))
-
-            # Add it to initialized arrays.
-            state.initialized = True
-
-    def _init_cstruct(self):
-        # Initialize all uninitialized arrays.
-        self._init_arrays()
-
-        for k, state in self._array_state.items():
-            # We do *not* set COMPUTED_ON_DISK items to the C-struct here, because we have no
-            # way of knowing (in this function) what is required to load in, and we don't want
-            # to unnecessarily load things in. We leave it to the user to ensure that all
-            # required arrays are loaded into memory before calling this function.
-            if state.initialized:
-                setattr(self.struct.cstruct, k, self._ary2buf(getattr(self, k)))
-
-        for k in self.struct.primitive_fields:
-            with contextlib.suppress(AttributeError):
-                setattr(self.struct.cstruct, k, getattr(self, k))
-
-    def _ary2buf(self, ary):
-        if not isinstance(ary, np.ndarray):
-            raise ValueError("ary must be a numpy array")
-        return self.struct._ffi.cast(
-            OutputStruct._TYPEMAP[ary.dtype.name], self.struct._ffi.from_buffer(ary)
-        )
-
-    def __call__(self):
-        """Return the C structure, will initialise if not already initialised."""
-        return self.cstruct
-
-    def __expose(self):
-        """Expose the non-array primitives of the ctype to the top-level object."""
-        for k in self.struct.primitive_fields:
-            setattr(self, k, getattr(self.cstruct, k))
-
-    @property
-    def _fname_skeleton(self):
-        """The filename without specifying the random seed."""
-        return f"{self._name}_{self._md5}" + "_r{seed}.h5"
-
-    def prepare(
-        self,
-        flush: Sequence[str] | None = None,
-        keep: Sequence[str] | None = None,
-        force: bool = False,
-    ):
-        """Prepare the instance for being passed to another function.
-
-        This will flush all arrays in "flush" from memory, and ensure all arrays
-        in "keep" are in memory. At least one of these must be provided. By default,
-        the complement of the given parameter is all flushed/kept.
-
-
-        Parameters
-        ----------
-        flush
-            Arrays to flush out of memory. Note that if no file is associated with this
-            instance, these arrays will be lost forever.
-        keep
-            Arrays to keep or load into memory. Note that if these do not already
-            exist, they will be loaded from file (if the file exists). Only one of
-            ``flush`` and ``keep`` should be specified.
-        force
-            Whether to force flushing arrays even if no disk storage exists.
-        """
-        if flush is None and keep is None:
-            raise ValueError("Must provide either flush or keep")
-
-        if flush is not None and keep is None:
-            keep = [k for k in self._array_state if k not in flush]
-        elif flush is None:
-            flush = [
-                k
-                for k in self._array_state
-                if k not in keep and self._array_state[k].initialized
-            ]
-
-        flush = flush or []
-        keep = keep or []
-
-        for k in flush:
-            self._remove_array(k, force)
-
-        # Accessing the array loads it into memory.
-        for k in keep:
-            getattr(self, k)
-
-    def _remove_array(self, k, force=False):
-        state = self._array_state[k]
-
-        if not state.initialized and k in self._array_structure:
-            warnings.warn(f"Trying to remove array that isn't yet created: {k}")
-            return
-
-        if state.computed_in_mem and not state.on_disk and not force:
-            raise OSError(
-                f"Trying to purge array '{k}' from memory that hasn't been stored! Use force=True if you meant to do this."
-            )
-
-        if state.c_has_active_memory:
-            lib.free(getattr(self.cstruct, k))
-
-        delattr(self, k)
-        state.initialized = False
-
-    def __getattr__(self, item):
-        """Gets arrays that aren't already in memory."""
-        # Have to use __dict__ here to test membership, otherwise we get recursion error.
-        if "_array_state" not in self.__dict__ or item not in self._array_state:
-            raise self.__getattribute__(item)
-
-        if not self._array_state[item].on_disk:
-            raise OSError(
-                f"Cannot get {item} as it is not in memory, and this object is not cached to disk."
-            )
-
-        self.read(fname=self.path, keys=[item])
-        return getattr(self, item)
-
-    def purge(self, force=False):
-        """Flush all the boxes out of memory.
-
-        Parameters
-        ----------
-        force
-            Whether to force the purge even if no disk storage exists.
-        """
-        self.prepare(keep=[], force=force)
-
-    def load_all(self):
-        """Load all possible arrays into memory."""
-        self.prepare(flush=[])
-
-    @property
-    def filename(self):
-        """The base filename of this object."""
-        return self._fname_skeleton.format(seed=self.random_seed)
-
-    def _get_fname(self, direc=None):
-        direc = Path(direc or config["direc"]).expanduser().absolute()
-        return direc / self.filename
-
-    def _find_file_without_seed(self, direc):
-        if allfiles := list(Path(direc).glob(self._fname_skeleton.format(seed="*"))):
-            return allfiles[0]
-        else:
-            return None
-
-    def find_existing(self, direc=None):
-        """
-        Try to find existing boxes which match the parameters of this instance.
-
-        Parameters
-        ----------
-        direc : str, optional
-            The directory in which to search for the boxes. By default, this is the
-            centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
-
-        Returns
-        -------
-        str
-            The filename of an existing set of boxes, or None.
-        """
-        direc = Path(direc or config["direc"]).expanduser()
-        f = self._get_fname(direc)
-        if f.exists() and self._check_parameters(f):
-            return f
-        return None
-
-    def _check_parameters(self, fname):
-        with h5py.File(fname, "r") as f:
-            for k in self._all_inputs:
-                q = getattr(self, k)
-
-                # The key name as it should appear in file.
-                kfile = k.lstrip("_")
-
-                # If this particular variable is set to None, this is interpreted
-                # as meaning that we don't care about matching it to file.
-                if q is None:
-                    continue
-
-                if (
-                    not isinstance(q, InputStruct)
-                    and not isinstance(q, StructInstanceWrapper)
-                    and f.attrs[kfile] != q
-                ):
-                    if not isinstance(q, (float, np.float32)) or not (
-                        float_to_string_precision(q, config["cache_param_sigfigs"])
-                        == float_to_string_precision(
-                            f.attrs[kfile], config["cache_param_sigfigs"]
-                        )
-                    ):
-                        logger.debug(f"For file {fname}:")
-                        logger.debug(
-                            f"\tThough md5 and seed matched, the parameter {kfile} did not match,"
-                            f" with values {f.attrs[kfile]} and {q} in file and user respectively"
-                        )
-                        return False
-                elif isinstance(q, (InputStruct, StructInstanceWrapper)):
-                    grp = f[kfile]
-
-                    dct = q.asdict() if isinstance(q, InputStruct) else q
-                    for kk, v in dct.items():
-                        if kk not in self._filter_params:
-                            file_v = grp.attrs[kk]
-                            if file_v == "none":
-                                file_v = None
-                            if file_v != v:
-                                logger.debug(f"For file {fname}:")
-                                logger.debug(
-                                    f"\tThough md5 and seed matched, the parameter {kk} did not match,"
-                                    f" with values {file_v} and {v} in file and user respectively"
-                                )
-                                return False
-        return True
-
-    def exists(self, direc=None):
-        """
-        Return a bool indicating whether a box matching the parameters of this instance is in cache.
-
-        Parameters
-        ----------
-        direc : str, optional
-            The directory in which to search for the boxes. By default, this is the
-            centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
-        """
-        return self.find_existing(direc) is not None
-
-    def write(
-        self,
-        direc=None,
-        fname: str | Path | None | h5py.File | h5py.Group = None,
-        write_inputs=True,
-        mode="w",
-    ):
-        """
-        Write the struct in standard HDF5 format.
-
-        Parameters
-        ----------
-        direc : str, optional
-            The directory in which to write the boxes. By default, this is the
-            centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
-        fname : str, optional
-            The filename to write to. By default creates a unique filename from the hash.
-        write_inputs : bool, optional
-            Whether to write the inputs to the file. Can be useful to set to False if
-            the input file already exists and has parts already written.
-        """
-        if not all(v.computed for v in self._array_state.values()):
-            raise OSError(
-                "Not all boxes have been computed (or maybe some have been purged). Cannot write."
-                f"Non-computed boxes: {[k for k, v in self._array_state.items() if not v.computed]}"
-            )
-
-        if not write_inputs:
-            mode = "a"
-
-        try:
-            if not isinstance(fname, (h5py.File, h5py.Group)):
-                direc = Path(direc or config["direc"]).expanduser()
-
-                if not direc.exists():
-                    direc.mkdir()
-
-                fname = Path(fname or self._get_fname(direc))
-                if not fname.is_absolute():
-                    fname = direc / fname
-
-                fl = h5py.File(fname, mode)
-            else:
-                fl = fname
-
-            try:
-                # Save input parameters to the file
-                if write_inputs:
-                    for k in self._all_inputs:
-                        q = getattr(self, k)
-
-                        kfile = k.lstrip("_")
-
-                        if isinstance(q, (InputStruct, StructInstanceWrapper)):
-                            grp = fl.create_group(kfile)
-                            dct = q.asdict() if isinstance(q, InputStruct) else q
-                            for kk, v in dct.items():
-                                if kk not in self._filter_params:
-                                    try:
-                                        grp.attrs[kk] = "none" if v is None else v
-                                    except TypeError as e:
-                                        raise TypeError(
-                                            f"key {kk} with value {v} is not able to be written to HDF5 attrs!"
-                                        ) from e
-                        else:
-                            try:
-                                fl.attrs[kfile] = q
-                            except TypeError as e:
-                                logger.info(f"name {k} val {q}, type {type(q)}")
-                                raise e
-
-                    # Write 21cmFAST version to the file
-                    fl.attrs["version"] = __version__
-
-                # Save the boxes to the file
-                boxes = fl.create_group(self._name)
-
-                self.write_data_to_hdf5_group(boxes)
-
-            finally:
-                if not isinstance(fname, (h5py.File, h5py.Group)):
-                    fl.close()
-                    self._paths.insert(0, Path(fname))
-
-        except OSError as e:
-            logger.warning(
-                f"When attempting to write {self._name} to file, write failed with the following error. Continuing without caching."
+        def _ary2buf(ary):
+            return self._ffi.cast(
+                self._TYPEMAP[ary.dtype.name], self._ffi.from_buffer(ary)
             )
 
-            logger.warning(e)
-
-    def write_data_to_hdf5_group(self, group: h5py.Group):
-        """
-        Write out this object to a particular HDF5 subgroup.
-
-        Parameters
-        ----------
-        group
-            The HDF5 group into which to write the object.
-        """
-        # Go through all fields in this struct, and save
-        for k, state in self._array_state.items():
-            group.create_dataset(k, data=getattr(self, k))
-            state.on_disk = True
-
-        for k in self.struct.primitive_fields:
-            group.attrs[k] = getattr(self, k)
-
-    def save(self, fname=None, direc=".", h5_group=None):
-        """Save the box to disk.
-
-        In detail, this just calls write, but changes the default directory to the
-        local directory. This is more user-friendly, while :meth:`write` is for
-        automatic use under-the-hood.
-
-        Parameters
-        ----------
-        fname : str, optional
-            The filename to write. Can be an absolute or relative path. If relative,
-            by default it is relative to the current directory (otherwise relative
-            to ``direc``). By default, the filename is auto-generated as unique to
-            the set of parameters that go into producing the data.
-        direc : str, optional
-            The directory into which to write the data. By default the current directory.
-            Ignored if ``fname`` is an absolute path.
-        """
-        # If fname is absolute path, then get direc from it, otherwise assume current dir.
-        fname = Path(fname)
-        if fname.is_absolute():
-            direc = fname.parent
-            fname = Path(fname.name)
-
-        if h5_group is not None:
-            fl = h5py.File(direc / fname, "a")
-
-            try:
-                grp = fl.create_group(h5_group)
-                self.write(direc, grp)
-            finally:
-                fl.close()
-        else:
-            self.write(direc, fname)
-
-    def _get_path(
-        self, direc: str | Path | None = None, fname: str | Path | None = None
-    ) -> Path:
-        if direc is None and fname is None and self.path:
-            return self.path
-
-        if fname is None:
-            pth = self.find_existing(direc)
-
-            if pth is None:
-                raise OSError(f"No boxes exist for these parameters. {pth} {direc}")
-        else:
-            direc = Path(direc or config["direc"]).expanduser()
-            fname = Path(fname)
-            pth = fname if fname.exists() else direc / fname
-        return pth
-
-    def read(
-        self,
-        direc: str | Path | None = None,
-        fname: str | Path | None | h5py.File | h5py.Group = None,
-        keys: Sequence[str] | None = (),
-    ):
-        """
-        Try find and read existing boxes from cache, which match the parameters of this instance.
-
-        Parameters
-        ----------
-        direc
-            The directory in which to search for the boxes. By default, this is the
-            centrally-managed directory, given by the ``config.yml`` in ``~/.21cmfast/``.
-        fname
-            The filename to read. By default, use the filename associated with this
-            object. Can be an open h5py File or Group, which will be directly written to.
-        keys
-            The names of boxes to read in (can be a subset). By default, read nothing.
-            If `None` is explicitly passed, read everything
-        """
-        if not isinstance(fname, (h5py.File, h5py.Group)):
-            pth = self._get_path(direc, fname)
-            fl = h5py.File(pth, "r")
-        else:
-            fl = fname
-
-        if keys is None:
-            keys = self._array_structure
         try:
-            try:
-                boxes = fl[self._name]
-            except KeyError as e:
-                raise OSError(
-                    f"While trying to read in {self._name}, the file exists, but does not have the "
-                    "correct structure."
-                ) from e
-
-            # Set our arrays.
-            for k in boxes.keys():
-                self._array_state[k].on_disk = True
-                if k in keys:
-                    setattr(self, k, boxes[k][...])
-                    self._array_state[k].computed_in_mem = True
-                    setattr(self.cstruct, k, self._ary2buf(getattr(self, k)))
-
-            for k in boxes.attrs.keys():
-                if k == "version":
-                    version = ".".join(boxes.attrs[k].split(".")[:2])
-                    patch = ".".join(boxes.attrs[k].split(".")[2:])
-
-                    if version != ".".join(__version__.split(".")[:2]):
-                        # Ensure that the major and minor versions are the same.
-                        warnings.warn(
-                            f"The file {pth} is out of date (version = {version}.{patch}). "
-                            f"Consider using another box and removing it!"
-                        )
-
-                    self.version = version
-                    self.patch_version = patch
-
-                setattr(self, k, boxes.attrs[k])
-                with contextlib.suppress(AttributeError):
-                    setattr(self.cstruct, k, getattr(self, k))
-
-            # Need to make sure that the seed is set to the one that's read in.
-            seed = fl.attrs["random_seed"]
-            self.random_seed = int(seed)
-        finally:
-            self.__expose()
-            if isinstance(fl, h5py.File):
-                self._paths.insert(0, Path(fl.filename))
-            else:
-                self._paths.insert(0, Path(fl.file.filename))
-
-            if not isinstance(fname, (h5py.File, h5py.Group)):
-                fl.close()
-
-    @classmethod
-    def from_file(
-        cls,
-        fname,
-        direc=None,
-        load_data=True,
-        h5_group: str | None = None,
-        arrays_to_load=(),
-        safe=True,
-    ):
-        """Create an instance from a file on disk.
-
-        Parameters
-        ----------
-        fname : str, optional
-            Path to the file on disk. May be relative or absolute.
-        direc : str, optional
-            The directory from which fname is relative to (if it is relative). By
-            default, will be the cache directory in config.
-        h5_group
-            The path to the group within the file in which the object is stored.
-        arrays_to_load : list of str, optional
-            A list of array names to load into memory
-            If the list is empty (default), a bare instance is created with input parameters
-            -- the instance can read data with the :func:`read` method.
-            If `None` is explicitly passed, all arrays are loaded into memory
-        """
-        direc = Path(direc or config["direc"]).expanduser()
-        fname = Path(fname)
-
-        if not fname.exists():
-            fname = direc / fname
-
-        with h5py.File(fname, "r") as fl:
-            fl_inp = fl[h5_group] if h5_group else fl
-            self = cls(**cls._read_inputs(fl_inp, safe=safe))
-            self.read(fname=fl_inp, keys=arrays_to_load)
-
-        return self
-
-    @classmethod
-    def _read_inputs(cls, grp: h5py.File | h5py.Group, safe=True):
-        input_classes = [c.__name__ for c in InputStruct.__subclasses__()]
-
-        # Read the input parameter dictionaries from file.
-        kwargs = {}
-        for k in cls._inputs:
-            kfile = k.lstrip("_")
-            input_class_name = snake_to_camel(kfile)
-
-            if input_class_name in input_classes:
-                kls = InputStruct.__subclasses__()[
-                    input_classes.index(input_class_name)
-                ]
-                subgrp = grp[kfile]
-                dct = dict(subgrp.attrs)
-                kwargs[k] = kls.from_subdict(dct, safe=safe)
-            else:
-                kwargs[k] = grp.attrs[kfile]
-        return kwargs
-
-    def __repr__(self):
-        """Return a fully unique representation of the instance."""
-        # This is the class name and all parameters which belong to C-based input structs,
-        # eg. InitialConditions(HII_DIM:100,SIGMA_8:0.8,...)
-        # eg. InitialConditions(HII_DIM:100,SIGMA_8:0.8,...)
-        return f"{self._seedless_repr()}_random_seed={self.random_seed}"
-
-    def _seedless_repr(self):
-        # The same as __repr__ except without the seed.
-        return (
-            (
-                self._name
-                + "("
-                + "; ".join(
-                    (
-                        repr(v)
-                        if isinstance(v, InputStruct)
-                        else (
-                            v.filtered_repr(self._filter_params)
-                            if isinstance(v, StructInstanceWrapper)
-                            else k.lstrip("_")
-                            + ":"
-                            + (
-                                float_to_string_precision(
-                                    v, config["cache_param_sigfigs"]
-                                )
-                                if isinstance(v, (float, np.float32))
-                                else repr(v)
-                            )
-                        )
-                    )
-                    for k, v in [
-                        (k, getattr(self, k))
-                        for k in self._all_inputs
-                        if k != "random_seed"
-                    ]
-                )
-            )
-            + f"; v{self.version}"
-            + ")"
-        )
-
-    def __str__(self):
-        """Return a human-readable representation of the instance."""
-        # this is *not* a unique representation, and doesn't include global params.
-        return (
-            self._name
-            + "("
-            + ";\n\t".join(
-                (
-                    repr(v)
-                    if isinstance(v, InputStruct)
-                    else k.lstrip("_") + ":" + repr(v)
-                )
-                for k, v in [(k, getattr(self, k)) for k in self._inputs]
-            )
-        ) + ")"
-
-    def __hash__(self):
-        """Return a unique hsh for this instance, even global params and random seed."""
-        return hash(repr(self))
-
-    @property
-    def _md5(self):
-        """Return a unique hsh of the object, *not* taking into account the random seed."""
-        return md5(self._seedless_repr().encode()).hexdigest()
-
-    def __eq__(self, other):
-        """Check equality with another object via its __repr__."""
-        return repr(self) == repr(other)
-
-    @property
-    def is_computed(self) -> bool:
-        """Whether this instance has been computed at all.
-
-        This is true either if the current instance has called :meth:`compute`,
-        or if it has a current existing :attr:`path` pointing to stored data,
-        or if such a path exists.
-
-        Just because the instance has been computed does *not* mean that all
-        relevant quantities are available -- some may have been purged from
-        memory without writing. Use :meth:`has` to check whether certain arrays
-        are available.
-        """
-        return any(v.computed for v in self._array_state.values())
-
-    def ensure_arrays_computed(self, *arrays, load=False) -> bool:
-        """Check if the given arrays are computed (not just initialized)."""
-        if not self.is_computed:
-            return False
-
-        computed = all(self._array_state[k].computed for k in arrays)
-
-        if computed and load:
-            self.prepare(keep=arrays, flush=[])
-
-        return computed
-
-    def ensure_arrays_inited(self, *arrays, init=False) -> bool:
-        """Check if the given arrays are initialized (or computed)."""
-        inited = all(self._array_state[k].initialized for k in arrays)
-
-        if init and not inited:
-            self._init_arrays()
-
-        return True
-
-    @abstractmethod
-    def get_required_input_arrays(self, input_box) -> list[str]:
-        """Return all input arrays required to compute this object."""
-        pass
-
-    def ensure_input_computed(self, input_box, load=False) -> bool:
-        """Ensure all the inputs have been computed."""
-        if input_box.dummy:
-            return True
-
-        arrays = self.get_required_input_arrays(input_box)
-
-        if input_box.initial:
-            return input_box.ensure_arrays_inited(*arrays, init=load)
-
-        return input_box.ensure_arrays_computed(*arrays, load=load)
-
-    def summarize(self, indent=0) -> str:
-        """Generate a string summary of the struct."""
-        indent = indent * "    "
-
-        # print array type and column headings
-        out = (
-            f"\n{indent}{self.__class__.__name__:>25}    "
-            + "   1st:         End:         Min:         Max:         Mean:         \n"
-        )
-
-        # print array extrema and means
-        for fieldname, state in self._array_state.items():
-            if not state.initialized:
-                out += f"{indent}    {fieldname:>25}:  uninitialized\n"
-            elif not state.computed:
-                out += f"{indent}    {fieldname:>25}:  initialized\n"
-            elif not state.computed_in_mem:
-                out += f"{indent}    {fieldname:>25}:  computed on disk\n"
-            else:
-                x = getattr(self, fieldname).flatten()
-                if len(x) > 0:
-                    out += f"{indent}    {fieldname:>25}: {x[0]:11.4e}, {x[-1]:11.4e}, {x.min():11.4e}, {x.max():11.4e}, {np.mean(x):11.4e}\n"
-                else:
-                    out += f"{indent}    {fieldname:>25}: size zero\n"
-
-        # print primitive fields
-        out += "".join(
-            f"{indent}    {fieldname:>25}: {getattr(self, fieldname, 'non-existent')}\n"
-            for fieldname in self.struct.primitive_fields
-        )
-
-        return out
-
-    @classmethod
-    def _log_call_arguments(cls, *args):
-        logger.debug(f"Calling {cls._c_compute_function.__name__} with following args:")
-
-        for arg in args:
-            if isinstance(arg, OutputStruct):
-                for line in arg.summarize(indent=1).split("\n"):
-                    logger.debug(line)
-            elif isinstance(arg, InputStruct):
-                for line in str(arg).split("\n"):
-                    logger.debug(f"    {line}")
-            else:
-                logger.debug(f"    {arg}")
-
-    def _ensure_arguments_exist(self, *args):
-        for arg in args:
-            if (
-                isinstance(arg, OutputStruct)
-                and not arg.dummy
-                and not self.ensure_input_computed(arg, load=True)
-            ):
-                raise ValueError(
-                    f"Trying to use {arg.__class__.__name__} to compute "
-                    f"{self.__class__.__name__}, but some required arrays "
-                    f"are not computed!\nArrays required: "
-                    f"{self.get_required_input_arrays(arg)}\n"
-                    f"Current State: {[(k, str(v)) for k, v in self._array_state.items()]}"
-                )
-
-    def _compute(
-        self, *args, hooks: dict[str | callable, dict[str, Any]] | None = None
-    ):
-        """Compute the actual function that fills this struct."""
-        # Check that all required inputs are really computed, and load them into memory
-        # if they're not already.
-        self._ensure_arguments_exist(*args)
-
-        # Write a detailed message about call arguments if debug turned on.
-        if logger.getEffectiveLevel() <= logging.DEBUG:
-            self._log_call_arguments(*args)
-
-        # Construct the args. All StructWrapper objects need to actually pass their
-        # underlying cstruct, rather than themselves. OutputStructs also pass the
-        # class in that's calling this.
-        inputs = [
-            (
-                arg()
-                if isinstance(arg, OutputStruct)
-                else arg.cstruct if isinstance(arg, InputStruct) else arg
-            )
-            for arg in args
-        ]
-
-        # Ensure we haven't already tried to compute this instance.
-        if self.is_computed:
-            raise ValueError(
-                f"You are trying to compute {self.__class__.__name__}, but it has already been computed."
-            )
-
-        # Perform the C computation
-        try:
-            exitcode = self._c_compute_function(*inputs, self())
+            setattr(self.cstruct, name, _ary2buf(array.value))
         except TypeError as e:
-            logger.error(
-                f"Arguments to {self._c_compute_function.__name__}: " f"{inputs}"
-            )
-            raise e
-
-        _process_exitcode(exitcode, self._c_compute_function, args)
-
-        # Ensure memory created in C gets mapped to numpy arrays in this struct.
-        for k, state in self._array_state.items():
-            if state.initialized:
-                state.computed_in_mem = True
-
-        self.__memory_map()
-        self.__expose()
-
-        # Optionally do stuff with the result (like writing it)
-        self._call_hooks(hooks)
-
-        return self
-
-    def _call_hooks(self, hooks):
-        if hooks is None:
-            hooks = {"write": {"direc": config["direc"]}}
-
-        for hook, params in hooks.items():
-            if callable(hook):
-                hook(self, **params)
-            else:
-                getattr(self, hook)(**params)
-
-    def __memory_map(self):
-        shapes = self._c_shape(self.cstruct)
-        for item in self._c_based_pointers:
-            setattr(self, item, asarray(getattr(self.cstruct, item), shapes[item]))
-            self._array_state[item].c_memory = True
-            self._array_state[item].computed_in_mem = True
-
-    def __del__(self):
-        """Safely delete the object and its C-allocated memory."""
-        # TODO: figure out why this breaks the C memory if purged, _remove_array should set .initialised to false,
-        #       which should make .c_has_active_memory false
-        for k in self._c_based_pointers:
-            if self._array_state[k].c_has_active_memory:
-                lib.free(getattr(self.cstruct, k))
+            raise TypeError(f"Error setting {name}") from e
 
 
 class StructInstanceWrapper:
diff --git a/tests/conftest.py b/tests/conftest.py
index 2bd0394c9..26dab81be 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -2,23 +2,23 @@
 
 import logging
 import os
-from astropy import units as un
+from pathlib import Path
 
 from py21cmfast import (
     AstroParams,
     CosmoParams,
     FlagOptions,
+    InitialConditions,
     InputParameters,
+    OutputCache,
     UserParams,
     compute_initial_conditions,
     config,
-    exhaust_lightcone,
     get_logspaced_redshifts,
-    global_params,
     perturb_field,
     run_lightcone,
 )
-from py21cmfast.cache_tools import clear_cache
+from py21cmfast.io.caching import CacheConfig
 from py21cmfast.lightcones import RectilinearLightconer
 
 
@@ -62,9 +62,6 @@ def module_direc(tmp_path_factory):
 
     printdir(direc)
 
-    # Clear all cached items created.
-    clear_cache(direc=str(direc))
-
     # Set direc back to original.
     config["direc"] = original
 
@@ -79,8 +76,6 @@ def test_direc(tmp_path_factory):
     yield direc
 
     printdir(direc)
-    # Clear all cached items created.
-    clear_cache(direc=str(direc))
 
     # Set direc back to original.
     config["direc"] = original
@@ -95,7 +90,6 @@ def setup_and_teardown_package(tmpdirec, request):
     # ------ #
 
     # Set default config parameters for all tests.
-    config["direc"] = str(tmpdirec)
     config["regenerate"] = True
     config["write"] = False
 
@@ -107,8 +101,6 @@ def setup_and_teardown_package(tmpdirec, request):
 
     printdir(tmpdirec)
 
-    clear_cache(direc=str(tmpdirec))
-
 
 # ======================================================================================
 # Create a default set of boxes that can be used throughout.
@@ -176,7 +168,7 @@ def default_input_struct(
         astro_params=default_astro_params,
         user_params=default_user_params,
         flag_options=default_flag_options,
-        node_redshifts=[],
+        node_redshifts=(),
     )
 
 
@@ -193,22 +185,27 @@ def default_input_struct_ts(redshift, default_input_struct, default_flag_options
 
 
 @pytest.fixture(scope="session")
-def default_input_struct_lc(lightcone_min_redshift, max_redshift, default_input_struct):
+def default_input_struct_lc(lightcone_min_redshift, default_input_struct):
     return default_input_struct.clone(
         node_redshifts=get_logspaced_redshifts(
             min_redshift=lightcone_min_redshift,
-            max_redshift=max_redshift,
+            max_redshift=default_input_struct.user_params.Z_HEAT_MAX,
             z_step_factor=default_input_struct.user_params.ZPRIME_STEP_FACTOR,
         )
     )
 
 
 @pytest.fixture(scope="session")
-def ic(default_input_struct, tmpdirec):
+def cache(tmpdirec: Path):
+    return OutputCache(tmpdirec)
+
+
+@pytest.fixture(scope="session")
+def ic(default_input_struct, cache) -> InitialConditions:
     return compute_initial_conditions(
         inputs=default_input_struct,
         write=True,
-        direc=tmpdirec,
+        cache=cache,
     )
 
 
@@ -236,14 +233,30 @@ def low_redshift():
 
 
 @pytest.fixture(scope="session")
-def perturbed_field(ic, default_input_struct, redshift, tmpdirec):
-    """A default perturb_field"""
+def perturbed_field(ic, redshift, cache):
+    """A default PerturbedField"""
     return perturb_field(
         redshift=redshift,
-        inputs=default_input_struct,
         initial_conditions=ic,
         write=True,
-        direc=tmpdirec,
+        cache=cache,
+    )
+
+
+@pytest.fixture(scope="session")
+def perturbed_field_lc(
+    ic: InitialConditions,
+    default_input_struct_lc: InputParameters,
+    redshift: float,
+    cache,
+):
+    """A default PerturbedField for a lightcone (which requires node_redshifts)."""
+    return perturb_field(
+        redshift=redshift,
+        inputs=default_input_struct_lc,
+        initial_conditions=ic,
+        write=True,
+        cache=cache,
     )
 
 
@@ -260,11 +273,12 @@ def rectlcn(
 
 
 @pytest.fixture(scope="session")
-def lc(rectlcn, ic, default_input_struct_lc):
-    iz, z, coev, lc = exhaust_lightcone(
+def lc(rectlcn, ic, cache, default_input_struct_lc):
+    *_, lc = run_lightcone(
         lightconer=rectlcn,
         initial_conditions=ic,
         inputs=default_input_struct_lc,
-        write=True,
+        write=CacheConfig(),
+        cache=cache,
     )
     return lc
diff --git a/tests/produce_integration_test_data.py b/tests/produce_integration_test_data.py
index 9413afb5b..90fc91360 100644
--- a/tests/produce_integration_test_data.py
+++ b/tests/produce_integration_test_data.py
@@ -30,12 +30,12 @@
     compute_initial_conditions,
     config,
     determine_halo_list,
-    exhaust_lightcone,
     get_logspaced_redshifts,
     global_params,
     perturb_field,
     perturb_halo_list,
     run_coeval,
+    run_lightcone,
 )
 from py21cmfast.lightcones import RectilinearLightconer
 
@@ -411,7 +411,7 @@ def produce_lc_power_spectra(redshift, **kwargs):
     )
 
     with config.use(ignore_R_BUBBLE_MAX_error=True):
-        _, _, _, lightcone = exhaust_lightcone(
+        _, _, _, lightcone = run_lightcone(
             lightconer=lcn,
             write=write_ics_only_hook,
             **options,
diff --git a/tests/test_cache_tools.py b/tests/test_cache_tools.py
deleted file mode 100644
index 1d805143a..000000000
--- a/tests/test_cache_tools.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""
-Tests for the tools in the wrapper.
-"""
-
-import pytest
-
-import numpy as np
-
-from py21cmfast import cache_tools
-
-
-def test_query(ic):
-    things = list(cache_tools.query_cache())
-
-    print(things)
-
-    classes = [t[1] for t in things]
-    assert ic in classes
-
-
-def test_bad_fname(tmpdirec):
-    with pytest.raises(ValueError):
-        cache_tools.readbox(direc=str(tmpdirec), fname="a_really_fake_file.h5")
-
-
-def test_readbox_data(tmpdirec, ic):
-    box = cache_tools.readbox(direc=str(tmpdirec), fname=ic.filename)
-
-    assert np.all(box.hires_density == ic.hires_density)
-
-
-def test_readbox_filter(ic, tmpdirec):
-    ic2 = cache_tools.readbox(
-        kind="InitialConditions", hsh=ic._md5, direc=str(tmpdirec)
-    )
-    assert np.all(ic2.hires_density == ic.hires_density)
-
-
-def test_readbox_seed(ic, tmpdirec):
-    ic2 = cache_tools.readbox(
-        kind="InitialConditions",
-        hsh=ic._md5,
-        seed=ic.random_seed,
-        direc=str(tmpdirec),
-    )
-    assert np.all(ic2.hires_density == ic.hires_density)
-
-
-def test_readbox_nohash(ic, tmpdirec):
-    with pytest.raises(ValueError):
-        cache_tools.readbox(
-            kind="InitialConditions", seed=ic.random_seed, direc=str(tmpdirec)
-        )
-
-
-def test_get_boxes_at_redshift(redshift, tmpdirec, perturbed_field):
-    boxes = cache_tools.get_boxes_at_redshift(redshift, direc=tmpdirec)
-    assert len(boxes["PerturbedField"]) == 1
-    assert boxes["PerturbedField"][0].redshift == redshift
-    assert boxes["PerturbedField"][0] == perturbed_field
-
-
-def test_get_boxes_at_redshift_range(redshift, tmpdirec, perturbed_field):
-    boxes = cache_tools.get_boxes_at_redshift(
-        (redshift - 3, redshift + 3), direc=tmpdirec
-    )
-    assert len(boxes["PerturbedField"]) == 1
-    assert boxes["PerturbedField"][0].redshift == redshift
-    assert boxes["PerturbedField"][0] == perturbed_field
-
-    # But at a different range...
-    boxes = cache_tools.get_boxes_at_redshift(
-        (redshift - 3, redshift - 1), direc=tmpdirec
-    )
-    assert len(boxes["PerturbedField"]) == 0
-
-
-def test_get_boxes_at_redshift_badfile(redshift, tmpdirec, perturbed_field):
-    # Add a file that should not be read
-    badpath = tmpdirec / "I_am_a_bad_file.h5"
-    badpath.touch()
-
-    # And it still works fine.
-    boxes = cache_tools.get_boxes_at_redshift(redshift, direc=tmpdirec)
-    assert len(boxes["PerturbedField"]) == 1
-    assert boxes["PerturbedField"][0].redshift == redshift
-    assert boxes["PerturbedField"][0] == perturbed_field
-
-
-def test_get_boxes_at_redshift_seed(redshift, tmpdirec, perturbed_field):
-    boxes = cache_tools.get_boxes_at_redshift(redshift, seed=12, direc=tmpdirec)
-    assert len(boxes["PerturbedField"]) == 1
-    assert boxes["PerturbedField"][0].redshift == redshift
-    assert boxes["PerturbedField"][0] == perturbed_field
-
-    # Use a different seed...
-    boxes = cache_tools.get_boxes_at_redshift(redshift, seed=13, direc=tmpdirec)
-    assert len(boxes["PerturbedField"]) == 0
-
-
-def test_get_boxes_at_redshift_with_params(
-    redshift, tmpdirec, default_user_params, perturbed_field
-):
-    boxes = cache_tools.get_boxes_at_redshift(
-        redshift, direc=tmpdirec, user_params=default_user_params
-    )
-    assert len(boxes["PerturbedField"]) == 1
-    assert boxes["PerturbedField"][0].redshift == redshift
-    assert boxes["PerturbedField"][0] == perturbed_field
-
-    # Use a different set of params
-    new = default_user_params.clone(DIM=2 * default_user_params.DIM)
-    boxes = cache_tools.get_boxes_at_redshift(redshift, direc=tmpdirec, user_params=new)
-    assert len(boxes["PerturbedField"]) == 0
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 330730bdf..7d8001fdb 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -3,7 +3,8 @@
 import yaml
 from click.testing import CliRunner
 
-from py21cmfast import InitialConditions, cli, query_cache
+from py21cmfast import InitialConditions, cli
+from py21cmfast.io.caching import OutputCache
 
 
 @pytest.fixture(scope="module")
@@ -33,49 +34,11 @@ def test_init(module_direc, default_input_struct, runner, cfg):
 
     assert result.exit_code == 0
 
-    ic = InitialConditions(
+    ic = InitialConditions.new(
         inputs=default_input_struct.clone(random_seed=101010),
     )
-    assert ic.exists(direc=str(module_direc))
-
-
-def test_init_param_override(module_direc, runner, cfg):
-    # Run the CLI
-    result = runner.invoke(
-        cli.main,
-        [
-            "init",
-            "--direc",
-            str(module_direc),
-            "--seed",
-            "102030",
-            "--config",
-            cfg,
-            "--",
-            "HII_DIM",
-            "37",
-            "DIM=52",
-            "--OMm",
-            "0.33",
-        ],
-    )
-    assert result.exit_code == 0
-
-    boxes = [
-        res[1]
-        for res in query_cache(
-            direc=str(module_direc), kind="InitialConditions", seed=102030
-        )
-    ]
-
-    assert len(boxes) == 1
-
-    box = boxes[0]
-
-    assert box.user_params.HII_DIM == 37
-    assert box.user_params.DIM == 52
-    assert box.cosmo_params.OMm == 0.33
-    assert box.cosmo_params.cosmo.Om0 == 0.33
+    cache = OutputCache(module_direc)
+    assert cache.find_existing(ic) is not None
 
 
 # TODO: we could generate a single "prev" box in a temp cache directory to make these tests work
@@ -150,7 +113,7 @@ def test_coeval(module_direc, runner, cfg):
         [
             "coeval",
             "35",
-            "--direc",
+            "--cache-dir",
             str(module_direc),
             "--seed",
             "101010",
@@ -179,21 +142,3 @@ def test_lightcone(module_direc, runner, cfg):
         ],
     )
     assert result.exit_code == 0
-
-
-def test_query(test_direc, runner, cfg):
-    # Quickly run the default example once again.
-    # Run the CLI
-    result = runner.invoke(
-        cli.main,
-        ["init", "--direc", str(test_direc), "--seed", "101010", "--config", cfg],
-    )
-    assert result.exit_code == 0
-
-    result = runner.invoke(
-        cli.main, ["query", "--direc", str(test_direc), "--seed", "101010"]
-    )
-
-    assert result.output.startswith("1 Data Sets Found:")
-    assert "random_seed:101010" in result.output
-    assert "InitialConditions(" in result.output
diff --git a/tests/test_config.py b/tests/test_config.py
deleted file mode 100644
index 4cfdc7c12..000000000
--- a/tests/test_config.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import pytest
-
-import yaml
-from os import path
-
-import py21cmfast as p21
-from py21cmfast._cfg import Config
-
-
-@pytest.fixture(scope="module")
-def cfgdir(tmp_path_factory):
-    return tmp_path_factory.mktemp("config_test_dir")
-
-
-def test_config_context(cfgdir, default_input_struct):
-    with p21.config.use(direc=cfgdir, write=True):
-        init = p21.compute_initial_conditions(
-            inputs=default_input_struct,
-        )
-
-    assert (cfgdir / init.filename).exists()
-    assert "config_test_dir" not in p21.config["direc"]
-
-
-def test_config_write(cfgdir):
-    with p21.config.use(direc=str(cfgdir)):
-        p21.config.write(cfgdir / "config.yml")
-
-    with open(cfgdir / "config.yml") as fl:
-        new_config = yaml.load(fl, Loader=yaml.FullLoader)
-
-    # Test adding new kind of string alias
-    new_config["boxdir"] = new_config["direc"]
-    del new_config["direc"]
-
-    with open(cfgdir / "config.yml", "w") as fl:
-        yaml.dump(new_config, fl)
-
-    with pytest.warns(UserWarning):
-        new_config = Config.load(cfgdir / "config.yml")
-
-    assert "boxdir" not in new_config
-    assert "direc" in new_config
-
-    with open(cfgdir / "config.yml") as fl:
-        new_config = yaml.load(fl, Loader=yaml.FullLoader)
-
-    assert "boxdir" not in new_config
-    assert "direc" in new_config
diff --git a/tests/test_data_exists.py b/tests/test_data_exists.py
index eb2c4456c..6c6d719ed 100644
--- a/tests/test_data_exists.py
+++ b/tests/test_data_exists.py
@@ -1,6 +1,6 @@
 import pytest
 
-from py21cmfast.wrapper.inputs import DATA_PATH
+from py21cmfast import DATA_PATH
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/test_drivers_coev.py b/tests/test_drivers_coev.py
index fb17cabe5..b7e56b7bf 100644
--- a/tests/test_drivers_coev.py
+++ b/tests/test_drivers_coev.py
@@ -10,35 +10,25 @@
 from py21cmfast import run_coeval
 
 
-def test_coeval_st(ic, default_input_struct_ts, perturbed_field):
+def test_coeval_st(ic, default_input_struct_ts, cache):
     coeval = run_coeval(
         initial_conditions=ic,
-        perturbed_field=perturbed_field,
         inputs=default_input_struct_ts,
+        cache=cache,
     )
-    assert isinstance(coeval.spin_temp_struct, p21c.TsBox)
+    assert isinstance(coeval[0].ts_box, p21c.TsBox)
 
 
-def test_run_coeval_bad_inputs(
-    ic, perturbed_field, default_input_struct, default_flag_options
-):
+def test_run_coeval_bad_inputs(ic, perturbed_field, default_input_struct, cache):
     with pytest.raises(
         ValueError, match="Either out_redshifts or perturb must be given"
     ):
-        run_coeval(
-            initial_conditions=ic,
-            inputs=default_input_struct,
-        )
+        run_coeval(initial_conditions=ic, inputs=default_input_struct, cache=cache)
 
-    with pytest.raises(ValueError, match="Input redshifts"):
-        run_coeval(
-            out_redshifts=20.0,
-            inputs=default_input_struct,
-            perturbed_field=perturbed_field,
-        )
 
-
-def test_coeval_lowerz_than_photon_cons(ic, default_input_struct, default_flag_options):
+def test_coeval_lowerz_than_photon_cons(
+    ic, default_input_struct, default_flag_options, cache
+):
     with pytest.raises(ValueError, match="You have passed a redshift"):
         run_coeval(
             initial_conditions=ic,
@@ -48,4 +38,5 @@ def test_coeval_lowerz_than_photon_cons(ic, default_input_struct, default_flag_o
                     PHOTON_CONS_TYPE="z-photoncons",
                 )
             ),
+            cache=cache,
         )
diff --git a/tests/test_drivers_lc.py b/tests/test_drivers_lc.py
index 6b3c31115..413a3b9a2 100644
--- a/tests/test_drivers_lc.py
+++ b/tests/test_drivers_lc.py
@@ -6,7 +6,6 @@
 
 import pytest
 
-import h5py
 import numpy as np
 
 import py21cmfast as p21c
@@ -19,7 +18,7 @@ def test_lightcone(lc, default_user_params, lightcone_min_redshift, max_redshift
 
 
 def test_lightcone_quantities(
-    ic, default_input_struct_lc, lightcone_min_redshift, max_redshift
+    ic, default_input_struct_lc, lightcone_min_redshift, max_redshift, cache
 ):
     lcn = p21c.RectilinearLightconer.with_equal_cdist_slices(
         min_redshift=lightcone_min_redshift,
@@ -29,29 +28,33 @@ def test_lightcone_quantities(
         quantities=("dNrec_box", "density", "brightness_temp", "Gamma12_box"),
     )
 
-    _, _, _, lc = p21c.exhaust_lightcone(
+    _, _, _, lc = p21c.run_lightcone(
         lightconer=lcn,
         initial_conditions=ic,
         inputs=default_input_struct_lc,
         global_quantities=("density", "Gamma12_box"),
+        cache=cache,
     )
 
-    print(lc.lightcones.keys())
-    print(lc.global_quantities.keys())
-
-    assert hasattr(lc, "dNrec_box")
-    assert hasattr(lc, "density")
-    assert hasattr(lc, "global_density")
-    assert hasattr(lc, "global_Gamma12")
+    assert "dNrec_box" in lc.lightcones
+    assert "density" in lc.lightcones
+    assert "brightness_temp" in lc.lightcones
+    assert "Gamma12_box" in lc.lightcones
+    assert "Gamma12_box" in lc.global_quantities
+    assert "density" in lc.global_quantities
 
     # dNrec is not filled because we're not doing INHOMO_RECO
-    assert lc.dNrec_box.max() == lc.dNrec_box.min() == 0
+    assert lc.lightcones["dNrec_box"].max() == lc.lightcones["dNrec_box"].min() == 0
 
     # density should be filled with not zeros.
-    assert lc.density.min() != lc.density.max() != 0
+    assert lc.lightcones["density"].min() != lc.lightcones["density"].max() != 0
 
     # Simply ensure that different quantities are not getting crossed/referred to each other.
-    assert lc.density.min() != lc.brightness_temp.min() != lc.brightness_temp.max()
+    assert (
+        lc.lightcones["density"].min()
+        != lc.lightcones["brightness_temp"].min()
+        != lc.lightcones["brightness_temp"].max()
+    )
 
     lcn_ts = p21c.RectilinearLightconer.with_equal_cdist_slices(
         min_redshift=lightcone_min_redshift,
@@ -63,98 +66,24 @@ def test_lightcone_quantities(
 
     # Raise an error since we're not doing spin temp.
     with pytest.raises(AttributeError):
-        p21c.exhaust_lightcone(
+        p21c.run_lightcone(
             lightconer=lcn_ts,
             initial_conditions=ic,
             inputs=default_input_struct_lc,
+            cache=cache,
         )
 
     # And also raise an error for global quantities.
     with pytest.raises(AttributeError):
-        p21c.exhaust_lightcone(
+        p21c.run_lightcone(
             lightconer=lcn_ts,
             initial_conditions=ic,
             inputs=default_input_struct_lc,
             global_quantities=("Ts_box",),
+            cache=cache,
         )
 
 
-# TODO: decide if we want callbacks at all, since run_lightcone is now a generator
-# def _global_Tb(coeval_box):
-#     assert isinstance(coeval_box, p21c.Coeval)
-#     global_Tb = coeval_box.brightness_temp.mean(dtype=np.float64).astype(np.float32)
-#     assert np.isclose(global_Tb, coeval_box.brightness_temp_struct.global_Tb)
-#     return global_Tb
-
-# def test_coeval_callback(
-#     rectlcn, ic, max_redshift, perturbed_field, default_flag_options
-# ):
-#     iz, z, coeval_output, lc = p21c.exhaust_lightcone(
-#         lightconer=rectlcn,
-#         initial_conditions=ic,
-#         flag_options=default_flag_options,
-#         lightcone_quantities=("brightness_temp",),
-#         global_quantities=("brightness_temp",),
-#         coeval_callback=_global_Tb,
-#     )
-#     assert isinstance(lc, p21c.LightCone)
-#     assert isinstance(coeval_output, list)
-#     assert len(lc.node_redshifts) == len(coeval_output)
-#     assert np.allclose(
-#         lc.global_brightness_temp, np.array(coeval_output, dtype=np.float32)
-#     )
-
-
-# def test_coeval_callback_redshifts(
-#     rectlcn, ic, redshift, max_redshift, perturbed_field, default_flag_options
-# ):
-#     coeval_callback_redshifts = np.array(
-#         [max_redshift, max_redshift, (redshift + max_redshift) / 2, redshift],
-#         dtype=np.float32,
-#     )
-#     iz, z, coeval_output, lc = p21c.exhaust_lightcone(
-#         lightconer=rectlcn,
-#         initial_conditions=ic,
-#         flag_options=default_flag_options,
-#         coeval_callback=lambda x: x.redshift,
-#         coeval_callback_redshifts=coeval_callback_redshifts,
-#     )
-#     assert len(coeval_callback_redshifts) - 1 == len(coeval_output)
-#     computed_redshifts = [
-#         lc.node_redshifts[np.argmin(np.abs(i - lc.node_redshifts))]
-#         for i in coeval_callback_redshifts[1:]
-#     ]
-#     assert np.allclose(coeval_output, computed_redshifts)
-
-
-# def Heaviside(x):
-#     return 1 if x > 0 else 0
-
-
-# def test_coeval_callback_exceptions(
-#     rectlcn, ic, redshift, max_redshift, perturbed_field, default_flag_options
-# ):
-#     # should output warning in logs and not raise an error
-#     iz, z, coeval_output, lc = p21c.exhaust_lightcone(
-#         lightconer=rectlcn,
-#         initial_conditions=ic,
-#         flag_options=default_flag_options,
-#         coeval_callback=lambda x: 1
-#         / Heaviside(x.redshift - (redshift + max_redshift) / 2),
-#         coeval_callback_redshifts=[max_redshift, redshift],
-#     )
-#     # should raise an error
-#     with pytest.raises(RuntimeError) as excinfo:
-#         iz, z, coeval_output, lc = p21c.exhaust_lightcone(
-#             lightconer=rectlcn,
-#             initial_conditions=ic,
-#             max_redshift=max_redshift,
-#             coeval_callback=lambda x: 1 / 0,
-#             coeval_callback_redshifts=[max_redshift, redshift],
-#         )
-#     assert "coeval_callback computation failed on first trial" in str(excinfo.value)
-
-
 def test_lightcone_coords(lc):
     assert lc.lightcone_coords.shape == (lc.lightcone_distances.shape[0],)
     assert lc.lightcone_coords[0] == 0.0
@@ -164,48 +93,45 @@ def test_lightcone_coords(lc):
     )
 
 
-def test_run_lc_bad_inputs(rectlcn, perturbed_field, default_input_struct_lc):
+def test_run_lc_bad_inputs(
+    rectlcn,
+    default_input_struct_lc: p21c.InputParameters,
+    cache,
+):
     with pytest.raises(
         ValueError,
         match="You are attempting to run a lightcone with no node_redshifts.",
     ):
-        p21c.exhaust_lightcone(
+        p21c.run_lightcone(
             lightconer=rectlcn,
             inputs=default_input_struct_lc.clone(node_redshifts=[]),
         )
-    with pytest.raises(
-        ValueError,
-        match="If perturbed_fields are provided, initial_conditions must be provided",
-    ):
-        p21c.exhaust_lightcone(
-            inputs=default_input_struct_lc,
-            lightconer=rectlcn,
-            perturbed_fields=[
-                perturbed_field,
-            ],
-        )
 
 
-def test_lc_with_lightcone_filename(ic, rectlcn, default_input_struct_lc, tmpdirec):
+def test_lc_with_lightcone_filename(
+    ic, rectlcn, default_input_struct_lc, tmpdirec, cache
+):
     fname = tmpdirec / "lightcone.h5"
-    _, _, _, lc = p21c.exhaust_lightcone(
+    _, _, _, lc = p21c.run_lightcone(
         lightconer=rectlcn,
         initial_conditions=ic,
         inputs=default_input_struct_lc,
         lightcone_filename=fname,
+        cache=cache,
     )
     assert fname.exists()
 
-    lc_loaded = p21c.LightCone.read(fname)
+    lc_loaded = p21c.LightCone.from_file(fname)
     assert lc_loaded == lc
     del lc_loaded
 
     # This one should NOT run anything.
-    _, _, _, lc2 = p21c.exhaust_lightcone(
+    _, _, _, lc2 = p21c.run_lightcone(
         lightconer=rectlcn,
         initial_conditions=ic,
         inputs=default_input_struct_lc,
         lightcone_filename=fname,
+        cache=cache,
     )
 
     assert lc2 == lc
@@ -214,56 +140,42 @@ def test_lc_with_lightcone_filename(ic, rectlcn, default_input_struct_lc, tmpdir
     fname.unlink()
 
 
-def test_lc_partial_eval(rectlcn, ic, default_input_struct_lc, tmpdirec, lc):
+def test_lc_partial_eval(rectlcn, ic, default_input_struct_lc, tmpdirec, lc, cache):
     fname = tmpdirec / "lightcone_partial.h5"
 
     z = rectlcn.lc_redshifts.max()
-    lc_gen = p21c.run_lightcone(
+    lc_gen = p21c.generate_lightcone(
         lightconer=rectlcn,
         initial_conditions=ic,
         inputs=default_input_struct_lc,
         lightcone_filename=fname,
         write=True,
+        cache=cache,
     )
     while z > 20.0:
         iz, z, _, partial = next(lc_gen)
 
-    assert partial._current_index < len(rectlcn.lc_redshifts)
-    assert partial._current_index > 0
-    assert partial._current_redshift <= 20.0
-    assert partial._current_redshift > 15.0
+    assert 0 < partial._last_completed_node < len(rectlcn.lc_redshifts) - 1
 
-    _, _, _, finished = p21c.exhaust_lightcone(
+    _, _, _, finished = p21c.run_lightcone(
         lightconer=rectlcn,
         initial_conditions=ic,
         inputs=default_input_struct_lc,
         lightcone_filename=fname,
+        cache=cache,
     )
 
     assert finished == lc
 
-    # Test that if _current redshift is not calculated, a good error is
-    # raised
-    with h5py.File(fname, "a") as fl:
-        fl.attrs["current_redshift"] = 2 * partial._current_redshift
-
-    with pytest.raises(IOError, match="No component boxes found at z"):
-        p21c.exhaust_lightcone(
-            lightconer=rectlcn,
-            initial_conditions=ic,
-            inputs=default_input_struct_lc,
-            lightcone_filename=fname,
-        )
-
 
 def test_lc_lowerz_than_photon_cons(
-    ic, default_input_struct_lc, default_flag_options, max_redshift
+    ic, default_input_struct_lc, default_flag_options, max_redshift, cache
 ):
     with pytest.raises(ValueError, match="You have passed a redshift"):
         inputs = default_input_struct_lc.clone(
             node_redshifts=p21c.get_logspaced_redshifts(
                 min_redshift=1.9,
-                max_redshift=default_input_struct_lc.node_redshifts.max(),
+                max_redshift=max(default_input_struct_lc.node_redshifts),
                 z_step_factor=default_input_struct_lc.user_params.ZPRIME_STEP_FACTOR,
             ),
             flag_options=default_flag_options.clone(PHOTON_CONS_TYPE="z-photoncons"),
@@ -275,8 +187,6 @@ def test_lc_lowerz_than_photon_cons(
             cosmo=ic.cosmo_params.cosmo,
         )
 
-        p21c.exhaust_lightcone(
-            lightconer=lcn,
-            initial_conditions=ic,
-            inputs=inputs,
+        p21c.run_lightcone(
+            lightconer=lcn, initial_conditions=ic, inputs=inputs, cache=cache
         )
diff --git a/tests/test_high_level_io.py b/tests/test_high_level_io.py
index 5a8a9f779..ba7286cfa 100644
--- a/tests/test_high_level_io.py
+++ b/tests/test_high_level_io.py
@@ -3,34 +3,35 @@
 import attrs
 import h5py
 import numpy as np
+from pathlib import Path
 
 from py21cmfast import (
-    BrightnessTemp,
     Coeval,
     InitialConditions,
     LightCone,
-    TsBox,
+    OutputCache,
     UserParams,
-    exhaust_lightcone,
-    global_params,
     run_coeval,
     run_lightcone,
 )
-from py21cmfast.lightcones import AngularLightconer, RectilinearLightconer
+from py21cmfast.drivers.lightcone import AngularLightcone
+from py21cmfast.io import h5
+from py21cmfast.lightcones import AngularLightconer
 
 
 @pytest.fixture(scope="module")
-def coeval(ic, default_input_struct_ts):
+def coeval(ic, default_input_struct_ts, cache) -> Coeval:
     return run_coeval(
         out_redshifts=25.0,
         initial_conditions=ic,
         write=True,
         inputs=default_input_struct_ts,
-    )
+        cache=cache,
+    )[0]
 
 
 @pytest.fixture(scope="module")
-def ang_lightcone(ic, lc, default_input_struct_lc, default_flag_options):
+def ang_lightcone(ic, lc, default_input_struct_lc, default_flag_options, cache):
     lcn = AngularLightconer.like_rectilinear(
         match_at_z=lc.lightcone_redshifts.min(),
         max_redshift=lc.lightcone_redshifts.max(),
@@ -38,7 +39,7 @@ def ang_lightcone(ic, lc, default_input_struct_lc, default_flag_options):
         get_los_velocity=True,
     )
 
-    iz, z, coev, anglc = exhaust_lightcone(
+    iz, z, coev, anglc = run_lightcone(
         lightconer=lcn,
         initial_conditions=ic,
         write=True,
@@ -47,39 +48,39 @@ def ang_lightcone(ic, lc, default_input_struct_lc, default_flag_options):
                 APPLY_RSDS=False,
             )
         ),
+        cache=cache,
     )
     return anglc
 
 
-def test_read_bad_file_lc(test_direc, lc):
+def test_read_bad_file_lc(test_direc: Path, lc: LightCone):
     # create a bad hdf5 file with some good fields,
     #  some bad fields, and some missing fields
     #  in both input parameters and box structures
-    fname = lc.save(direc=test_direc)
+    fname = test_direc / "_lc.h5"
+    lc.save(path=fname)
+
     with h5py.File(fname, "r+") as f:
         # make gluts, these should be ignored on reading
-        f["user_params"].attrs["NotARealParameter"] = "fake_param"
-        f["_globals"].attrs["NotARealGlobal"] = "fake_param"
+        f["InputParameters"]["user_params"].attrs["NotARealParameter"] = "fake_param"
+        # f["_globals"].attrs["NotARealGlobal"] = "fake_param"
 
         # make gaps
-        del f["user_params"].attrs["BOX_LEN"]
-        del f["_globals"].attrs["OPTIMIZE_MIN_MASS"]
+        del f["InputParameters"]["user_params"].attrs["BOX_LEN"]
 
     # load without compatibility mode, make sure we throw the right error
     with pytest.raises(ValueError, match="There are extra or missing"):
-        LightCone.read(fname, direc=test_direc, safe=True)
+        LightCone.from_file(fname, safe=True)
 
     # load in compatibility mode, check that we warn correctly
     with pytest.warns(UserWarning, match="There are extra or missing"):
-        lc2 = LightCone.read(fname, direc=test_direc, safe=False)
+        lc2 = LightCone.from_file(fname, safe=False)
 
     # check that the fake fields didn't show up in the struct
     assert not hasattr(lc2.user_params, "NotARealParameter")
-    assert "NotARealGlobal" not in lc2.global_params.keys()
 
     # check that missing fields are set to default
     assert lc2.user_params.BOX_LEN == UserParams().BOX_LEN
-    assert lc2.global_params["OPTIMIZE_MIN_MASS"] == global_params.OPTIMIZE_MIN_MASS
 
     # check that the fields which are good are read in the struct
     assert all(
@@ -87,41 +88,36 @@ def test_read_bad_file_lc(test_direc, lc):
         for field in attrs.fields(UserParams)
         if field.name != "BOX_LEN"
     )
-    assert all(
-        lc2.global_params[k] == lc.global_params[k]
-        for k in global_params.keys()
-        if k != "OPTIMIZE_MIN_MASS"
-    )
 
 
-def test_read_bad_file_coev(test_direc, coeval):
+def test_read_bad_file_coev(test_direc: Path, coeval: Coeval):
     # create a bad hdf5 file with some good fields,
     #  some bad fields, and some missing fields
     #  in both input parameters and box structures
-    fname = coeval.save(direc=test_direc)
+    fname = test_direc / "_a_bad_file.h5"
+
+    coeval.save(path=fname)
     with h5py.File(fname, "r+") as f:
         # make gluts, these should be ignored on reading
-        f["user_params"].attrs["NotARealParameter"] = "fake_param"
-        f["_globals"].attrs["NotARealGlobal"] = "fake_param"
+        f["BrightnessTemp"]["InputParameters"]["user_params"].attrs[
+            "NotARealParameter"
+        ] = "fake_param"
 
         # make gaps
-        del f["user_params"].attrs["BOX_LEN"]
-        del f["_globals"].attrs["OPTIMIZE_MIN_MASS"]
+        del f["BrightnessTemp"]["InputParameters"]["user_params"].attrs["BOX_LEN"]
 
     # load in the coeval check that we warn correctly
     with pytest.raises(ValueError, match="There are extra or missing"):
-        Coeval.read(fname, direc=test_direc, safe=True)
+        Coeval.from_file(fname, safe=True)
 
     with pytest.warns(UserWarning, match="There are extra or missing"):
-        cv2 = Coeval.read(fname, direc=test_direc, safe=False)
+        cv2 = Coeval.from_file(fname, safe=False)
 
     # check that the fake params didn't show up in the struct
     assert not hasattr(cv2.user_params, "NotARealParameter")
-    assert "NotARealGlobal" not in cv2.global_params.keys()
 
     # check that missing fields are set to default
     assert cv2.user_params.BOX_LEN == UserParams().BOX_LEN
-    assert cv2.global_params["OPTIMIZE_MIN_MASS"] == global_params.OPTIMIZE_MIN_MASS
 
     # check that the fields which are good are read in the struct
     assert all(
@@ -129,110 +125,36 @@ def test_read_bad_file_coev(test_direc, coeval):
         for k in coeval.user_params.asdict().keys()
         if k != "BOX_LEN"
     )
-    assert all(
-        cv2.global_params[k] == coeval.global_params[k]
-        for k in global_params.keys()
-        if k != "OPTIMIZE_MIN_MASS"
-    )
 
 
 def test_lightcone_roundtrip(test_direc, lc):
-    fname = lc.save(direc=test_direc)
-    lc2 = LightCone.read(fname)
+    fname = test_direc / "lc.h5"
+    lc.save(path=fname)
+    lc2 = LightCone.from_file(fname)
 
     assert lc == lc2
-    assert lc.get_unique_filename() == lc2.get_unique_filename()
     assert np.allclose(lc.lightcone_redshifts, lc2.lightcone_redshifts)
-    assert np.all(np.isclose(lc.brightness_temp, lc2.brightness_temp))
-
-
-def test_lightcone_io_abspath(lc, test_direc):
-    lc.save(test_direc / "abs_path_lightcone.h5")
-    assert (test_direc / "abs_path_lightcone.h5").exists()
-
-
-def test_coeval_roundtrip(test_direc, coeval):
-    fname = coeval.save(direc=test_direc)
-    coeval2 = Coeval.read(fname)
-
-    assert coeval == coeval2
-    assert coeval.get_unique_filename() == coeval2.get_unique_filename()
     assert np.all(
-        np.isclose(
-            coeval.brightness_temp_struct.brightness_temp,
-            coeval2.brightness_temp_struct.brightness_temp,
-        )
+        np.isclose(lc.lightcones["brightness_temp"], lc2.lightcones["brightness_temp"])
     )
 
 
-def test_coeval_cache(coeval):
-    assert coeval.cache_files is not None
-    out = coeval.get_cached_data(kind="brightness_temp", redshift=25.1, load_data=True)
-
-    assert isinstance(out, BrightnessTemp)
-    assert np.all(out.brightness_temp == coeval.brightness_temp)
-
-    out = coeval.get_cached_data(
-        kind="spin_temp", redshift=coeval.user_params.Z_HEAT_MAX * 1.01, load_data=True
-    )
-
-    assert isinstance(out, TsBox)
-    assert not np.all(out.Ts_box == coeval.Ts_box)
-
-    with pytest.raises(ValueError):
-        coeval.get_cached_data(kind="bad", redshift=100.0)
-
-
-def test_gather(coeval, test_direc):
-    fname = coeval.gather(
-        fname="tmpfile_test_gather.h5",
-        kinds=("perturb_field", "init"),
-        direc=str(test_direc),
-    )
-
-    with h5py.File(fname, "r") as fl:
-        assert "cache" in fl
-        assert "perturb_field" in fl["cache"]
-        assert "z25.00" in fl["cache"]["perturb_field"]
-        assert "density" in fl["cache"]["perturb_field"]["z25.00"]
-        assert (
-            fl["cache"]["perturb_field"]["z25.00"]["density"].shape
-            == (coeval.user_params.HII_DIM,) * 3
-        )
-
-        assert "z0.00" in fl["cache"]["init"]
-
-
-def test_lightcone_cache(lc):
-    assert lc.cache_files is not None
-    out = lc.get_cached_data(kind="brightness_temp", redshift=15.1, load_data=True)
-
-    assert isinstance(out, BrightnessTemp)
-
-    out = lc.get_cached_data(
-        kind="brightness_temp",
-        redshift=lc.user_params.Z_HEAT_MAX * 1.01,
-        load_data=True,
-    )
-
-    assert isinstance(out, BrightnessTemp)
-
-    with pytest.raises(ValueError):
-        lc.get_cached_data(kind="bad", redshift=100.0)
-
-    lc.gather(fname="tmp_lc_gather.h5", clean=["brightness_temp"])
+def test_coeval_roundtrip(test_direc, coeval):
+    fname = test_direc / "a_coeval.h5"
+    coeval.save(fname)
+    coeval2 = Coeval.from_file(fname)
 
-    with pytest.raises(IOError):
-        lc.get_cached_data(kind="brightness_temp", redshift=25.1)
+    assert coeval == coeval2
+    assert np.all(np.isclose(coeval.brightness_temp, coeval2.brightness_temp))
 
 
-def test_ang_lightcone(lc, ang_lightcone):
+def test_ang_lightcone(lc, ang_lightcone: AngularLightcone):
     # we test that the fields are "highly correlated",
     # and moreso in the one corner where the lightcones
     # should be almost exactly the same, and less so in the other
     # corners, and also less so at the highest redshifts.
-    rbt = lc.brightness_temp
-    abt = ang_lightcone.brightness_temp.reshape(rbt.shape)
+    rbt = lc.lightcones["brightness_temp"]
+    abt = ang_lightcone.lightcones["brightness_temp"].reshape(rbt.shape)
 
     fullcorr0 = np.corrcoef(rbt[:, :, 0].flatten(), abt[:, :, 0].flatten())
     fullcorrz = np.corrcoef(rbt[:, :, -1].flatten(), abt[:, :, -1].flatten())
@@ -253,15 +175,12 @@ def test_ang_lightcone(lc, ang_lightcone):
     assert topcorner[0, 1] > bottomcorner[0, 1]
 
 
-def test_write_to_group(ic, test_direc):
-    ic.save(test_direc / "a_new_file.h5", h5_group="new_group")
+def test_write_to_group(ic: InitialConditions, cache: OutputCache):
+    h5.write_output_to_hdf5(ic, path=cache.direc / "a_new_file.h5", group="new_group")
 
-    with h5py.File(test_direc / "a_new_file.h5", "r") as fl:
+    with h5py.File(cache.direc / "a_new_file.h5", "r") as fl:
         assert "new_group" in fl
-        assert "global_params" in fl["new_group"]
 
-    ic2 = InitialConditions.from_file(
-        test_direc / "a_new_file.h5", h5_group="new_group"
-    )
+    ic2 = h5.read_output_struct(cache.direc / "a_new_file.h5", group="new_group")
 
     assert ic2 == ic
diff --git a/tests/test_initial_conditions.py b/tests/test_initial_conditions.py
index 2bdb8845d..2b013d4c9 100644
--- a/tests/test_initial_conditions.py
+++ b/tests/test_initial_conditions.py
@@ -10,7 +10,7 @@
 import py21cmfast as p21c
 
 
-def test_box_shape(ic):
+def test_box_shape(ic: p21c.InitialConditions):
     """Test basic properties of the InitialConditions struct"""
     shape = (35, 35, 35)
     hires_shape = tuple(2 * s for s in shape)
@@ -30,34 +30,37 @@ def test_box_shape(ic):
     assert ic.hires_vy_2LPT.shape == hires_shape
     assert ic.hires_vz_2LPT.shape == hires_shape
 
-    assert not hasattr(ic, "lowres_vcb")
+    assert ic.lowres_vcb is None
 
     assert ic.cosmo_params == p21c.CosmoParams()
 
 
-def test_modified_cosmo(ic, default_input_struct):
+def test_modified_cosmo(
+    ic: p21c.InitialConditions, default_input_struct: p21c.InputParameters, cache
+):
     """Test using a modified cosmology"""
     inputs = default_input_struct.evolve_input_structs(SIGMA_8=0.9)
-    ic2 = p21c.compute_initial_conditions(inputs=inputs)
+    ic2 = p21c.compute_initial_conditions(inputs=inputs, cache=cache)
 
     assert ic2.cosmo_params != ic.cosmo_params
     assert ic2.cosmo_params == inputs.cosmo_params
     assert ic2.cosmo_params.SIGMA_8 == inputs.cosmo_params.SIGMA_8
 
 
-def test_transfer_function(ic, default_input_struct):
+def test_transfer_function(
+    ic: p21c.InitialConditions, default_input_struct: p21c.InputParameters, cache
+):
     """Test using a modified transfer function"""
     inputs = default_input_struct.evolve_input_structs(POWER_SPECTRUM="CLASS")
-    ic2 = p21c.compute_initial_conditions(
-        inputs=inputs,
-    )
-    print(ic2.cosmo_params)
+    ic2 = p21c.compute_initial_conditions(inputs=inputs, cache=cache)
+    hrd2 = ic2.hires_density.value
+    hrd = ic.hires_density.value
 
-    rmsnew = np.sqrt(np.mean(ic2.hires_density**2))
-    rmsdelta = np.sqrt(np.mean((ic2.hires_density - ic.hires_density) ** 2))
+    rmsnew = np.sqrt(np.mean(hrd2**2))
+    rmsdelta = np.sqrt(np.mean((hrd2 - hrd) ** 2))
     assert rmsdelta < rmsnew
     assert rmsnew > 0.0
-    assert not np.allclose(ic2.hires_density, ic.hires_density)
+    assert not np.allclose(hrd2, hrd)
 
 
 def test_relvels():
@@ -72,8 +75,8 @@ def test_relvels():
     )
     ic = p21c.compute_initial_conditions(inputs=inputs)
 
-    vcbrms_lowres = np.sqrt(np.mean(ic.lowres_vcb**2))
-    vcbavg_lowres = np.mean(ic.lowres_vcb)
+    vcbrms_lowres = np.sqrt(np.mean(ic.lowres_vcb.value**2))
+    vcbavg_lowres = np.mean(ic.lowres_vcb.value)
 
     # we test the lowres box
     # rms should be about 30 km/s for LCDM, so we check it is finite and not far off
diff --git a/tests/test_input_structs.py b/tests/test_input_structs.py
index 27af1b28d..30a0d551c 100644
--- a/tests/test_input_structs.py
+++ b/tests/test_input_structs.py
@@ -273,16 +273,16 @@ def test_inputstruct_init(default_seed):
     assert altered_struct.user_params.BOX_LEN == 30
 
 
-def test_inputstruct_outputs(
-    default_input_struct, default_input_struct_ts, perturbed_field
-):
-    # NOTE: node_redshifts are not yet saved in inputstruct, so two OutputStruct
-    # can still be compatible with different node_redshifts
-    example_ib = IonizedBox(inputs=default_input_struct_ts)  # doesn't compute
-    with pytest.raises(ValueError, match="InputParameters not compatible with"):
-        default_input_struct.check_output_compatibility([example_ib])
-
-    default_input_struct.check_output_compatibility([perturbed_field])
+# def test_inputstruct_outputs(
+#     default_input_struct, default_input_struct_ts, perturbed_field
+# ):
+#     # NOTE: node_redshifts are not yet saved in inputstruct, so two OutputStruct
+#     # can still be compatible with different node_redshifts
+#     example_ib = IonizedBox.new(inputs=default_input_struct_ts)  # doesn't compute
+#     with pytest.raises(ValueError, match="InputParameters not compatible with"):
+#         default_input_struct.check_output_compatibility([example_ib])
+
+#     default_input_struct.check_output_compatibility([perturbed_field])
 
 
 def test_native_template_loading(default_seed):
diff --git a/tests/test_output_structs.py b/tests/test_output_structs.py
index bf3181076..e4cfb6287 100644
--- a/tests/test_output_structs.py
+++ b/tests/test_output_structs.py
@@ -4,125 +4,75 @@
 
 import pytest
 
+import attrs
 import copy
 import numpy as np
 import pickle
 
 from py21cmfast import InitialConditions  # An example of an output struct
-from py21cmfast import IonizedBox, PerturbedField, TsBox, global_params
+from py21cmfast import (
+    InputParameters,
+    IonizedBox,
+    OutputCache,
+    PerturbedField,
+    TsBox,
+    global_params,
+)
+from py21cmfast.io import h5
+from py21cmfast.wrapper import outputs as ox
 
 
 @pytest.fixture(scope="function")
-def init(default_input_struct):
-    return InitialConditions(inputs=default_input_struct)
+def init(default_input_struct: InputParameters):
+    return InitialConditions.new(inputs=default_input_struct)
 
 
-@pytest.mark.parametrize("cls", [InitialConditions, PerturbedField, IonizedBox, TsBox])
-def test_pointer_fields(cls, default_input_struct):
-    with pytest.raises(KeyError):
-        cls()
+def test_readability(
+    ic: InitialConditions, cache: OutputCache, default_input_struct: InputParameters
+):
+    ic2 = InitialConditions.new(inputs=default_input_struct)
+    existing = cache.find_existing(ic2)
 
-    inst = cls(inputs=default_input_struct)
+    assert existing is not None
+    assert existing.exists()
 
-    # Get list of fields before and after array initialisation
-    d = copy.copy(list(inst.__dict__.keys()))
-    inst._init_arrays()
-    new_names = [name for name in inst.__dict__ if name not in d]
+    ic2 = cache.load(ic2)
 
-    assert new_names
-    assert all(n in inst.struct.pointer_fields for n in new_names)
-
-
-def test_non_existence(init, test_direc):
-    assert not init.exists(direc=test_direc)
-
-
-def test_writeability(init):
-    """init is not initialized and therefore can't write yet."""
-    with pytest.raises(IOError):
-        init.write()
-
-
-def test_readability(ic, tmpdirec, default_input_struct):
-    ic2 = InitialConditions(inputs=default_input_struct)
-
-    # without seeds, they are obviously exactly the same.
-    assert ic._seedless_repr() == ic2._seedless_repr()
-
-    assert ic2.exists(direc=tmpdirec)
-
-    ic2.read(direc=tmpdirec)
-
-    assert repr(ic) == repr(ic2)  # they should be exactly the same.
-    assert str(ic) == str(ic2)  # their str is the same.
-    assert hash(ic) == hash(ic2)
     assert ic == ic2
     assert ic is not ic2
 
 
-def test_different_seeds(init, default_input_struct, default_seed):
-    ic2 = InitialConditions(inputs=default_input_struct.clone(random_seed=2))
+def test_different_seeds(
+    init: InitialConditions,
+    default_input_struct: InputParameters,
+):
+    ic2 = InitialConditions.new(
+        inputs=default_input_struct.clone(
+            random_seed=default_input_struct.random_seed + 1
+        )
+    )
 
     assert init is not ic2
     assert init != ic2
-    assert repr(init) != repr(ic2)
-    assert init._seedless_repr() == ic2._seedless_repr()
-
-    assert init._md5 == ic2._md5
 
     # make sure we didn't inadvertantly set the random seed while doing any of this
-    assert init.random_seed == default_seed
+    assert init.random_seed == default_input_struct.random_seed
 
 
-def test_pickleability(default_input_struct):
-    ic_ = InitialConditions(inputs=default_input_struct)
-    ic_.filled = True
-
+def test_pickleability(default_input_struct: InputParameters):
+    ic_ = InitialConditions.new(inputs=default_input_struct)
     s = pickle.dumps(ic_)
 
     ic2 = pickle.loads(s)
     assert repr(ic_) == repr(ic2)
 
 
-def test_fname(default_input_struct):
-    ic1 = InitialConditions(inputs=default_input_struct)
-    ic2 = InitialConditions(inputs=default_input_struct.clone(random_seed=2))
-
-    assert ic1.filename != ic2.filename  # random seeds should now be different
-    assert ic1._fname_skeleton == ic2._fname_skeleton
-
-
-def test_match_seed(tmpdirec, default_input_struct):
-    ic2 = InitialConditions(inputs=default_input_struct.clone(random_seed=3))
+def test_match_seed(cache: OutputCache, default_input_struct: InputParameters):
+    ic2 = InitialConditions.new(inputs=default_input_struct.clone(random_seed=3))
 
     # This fails because we've set the seed and it's different to the existing one.
-    with pytest.raises(IOError):
-        ic2.read(direc=tmpdirec)
-
-
-def test_bad_class_definition(default_input_struct):
-    class CustomInitialConditions(InitialConditions):
-        """
-        A class containing all initial conditions boxes.
-        """
-
-        def __init__(self):
-            super.__init__(self)
-            self._name = "InitialConditions"
-
-        def _get_box_structures(self):
-            out = super()._get_box_structures()
-            out["unknown_key"] = (1, 1, 1)
-            return out
-
-    with pytest.raises(TypeError):
-        CustomInitialConditions(inputs=default_input_struct)
-
-
-def test_bad_write(init):
-    # no random seed yet so shouldn't be able to write.
-    with pytest.raises(IOError):
-        init.write()
+    with pytest.raises(IOError, match="No cache exists for"):
+        cache.load(ic2)
 
 
 def test_global_params_keys():
@@ -130,21 +80,35 @@ def test_global_params_keys():
 
 
 def test_reading_purged(ic: InitialConditions):
-    lowres_density = ic.lowres_density
+    lowres_density = ic.get(ic.lowres_density)
 
     # Remove it from memory
     ic.purge()
 
-    assert "lowres_density" not in ic.__dict__
-    assert ic._array_state["lowres_density"].on_disk
-    assert not ic._array_state["lowres_density"].computed_in_mem
+    assert not ic.lowres_density.state.computed_in_mem
+    assert ic.lowres_density.state.on_disk
 
     # But we can still get it.
-    lowres_density_2 = ic.lowres_density
+    lowres_density_2 = ic.get(ic.lowres_density)
 
-    assert ic._array_state["lowres_density"].on_disk
-    assert ic._array_state["lowres_density"].computed_in_mem
+    assert ic.lowres_density.state.on_disk
+    assert ic.lowres_density.state.computed_in_mem
 
     assert np.allclose(lowres_density_2, lowres_density)
 
     ic.load_all()
+
+
+@pytest.mark.parametrize("struct", list(ox._ALL_OUTPUT_STRUCTS.values()))
+def test_all_fields_exist(struct: ox.OutputStruct):
+    cstruct = ox.StructWrapper(struct.__name__)
+
+    this = attrs.fields_dict(struct)
+
+    # Ensure that all fields in the cstruct are also defined on this class.
+    for name in cstruct.pointer_fields:
+        assert name in this
+        assert this[name].type == ox.Array
+
+    for name in cstruct.primitive_fields:
+        assert name in this
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index 870106a29..bf5c13ba3 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -4,10 +4,11 @@
 
 import pytest
 
+import py21cmfast as p21c
 from py21cmfast import plotting
 
 
-def test_coeval_sliceplot(ic):
+def test_coeval_sliceplot(ic: p21c.InitialConditions):
     fig, ax = plotting.coeval_sliceplot(ic)
 
     assert ax.xaxis.get_label().get_text() == "x-axis [Mpc]"
@@ -41,28 +42,28 @@ def test_coeval_sliceplot(ic):
     assert ax.yaxis.get_label().get_text() == "z-axis [Mpc]"
 
 
-def test_lightcone_sliceplot_default(lc):
+def test_lightcone_sliceplot_default(lc: p21c.LightCone):
     fig, ax = plotting.lightcone_sliceplot(lc)
 
     assert ax.yaxis.get_label().get_text() == "y-axis [Mpc]"
     assert ax.xaxis.get_label().get_text() == "Redshift"
 
 
-def test_lightcone_sliceplot_vertical(lc):
+def test_lightcone_sliceplot_vertical(lc: p21c.LightCone):
     fig, ax = plotting.lightcone_sliceplot(lc, vertical=True)
 
     assert ax.yaxis.get_label().get_text() == "Redshift"
     assert ax.xaxis.get_label().get_text() == "y-axis [Mpc]"
 
 
-def test_lc_sliceplot_freq(lc):
+def test_lc_sliceplot_freq(lc: p21c.LightCone):
     fig, ax = plotting.lightcone_sliceplot(lc, zticks="frequency")
 
     assert ax.yaxis.get_label().get_text() == "y-axis [Mpc]"
     assert ax.xaxis.get_label().get_text() == "Frequency [MHz]"
 
 
-def test_lc_sliceplot_cdist(lc):
+def test_lc_sliceplot_cdist(lc: p21c.LightCone):
     fig, ax = plotting.lightcone_sliceplot(lc, zticks="comoving_distance")
 
     assert ax.yaxis.get_label().get_text() == "y-axis [Mpc]"
@@ -73,7 +74,7 @@ def test_lc_sliceplot_cdist(lc):
     assert xlim.max() <= lc.lightcone_dimensions[-1]
 
 
-def test_lc_sliceplot_sliceax(lc):
+def test_lc_sliceplot_sliceax(lc: p21c.LightCone):
     fig, ax = plotting.lightcone_sliceplot(lc, slice_axis=2)
 
     assert ax.yaxis.get_label().get_text() == "y-axis [Mpc]"
diff --git a/tests/test_segfaults.py b/tests/test_segfaults.py
index fb71b22d7..11faebc75 100644
--- a/tests/test_segfaults.py
+++ b/tests/test_segfaults.py
@@ -120,7 +120,7 @@
 
 
 @pytest.mark.parametrize("name", list(OPTIONS_CTEST.keys()))
-def test_lc_runs(name, max_redshift):
+def test_lc_runs(name, max_redshift, cache):
     redshift, kwargs = OPTIONS_CTEST[name]
     options = prd.get_all_options_struct(redshift, **kwargs)
 
@@ -130,6 +130,7 @@ def test_lc_runs(name, max_redshift):
         or options["inputs"].flag_options.INHOMO_RECO
     ):
         node_maxz = options["inputs"].user_params.Z_HEAT_MAX
+
     options["inputs"] = options["inputs"].clone(
         user_params=p21c.UserParams.new(DEFAULT_USER_PARAMS_CTEST),
         node_redshifts=p21c.get_logspaced_redshifts(
@@ -138,6 +139,9 @@ def test_lc_runs(name, max_redshift):
             z_step_factor=options["inputs"].user_params.ZPRIME_STEP_FACTOR,
         ),
     )
+    print(
+        options["inputs"].user_params.Z_HEAT_MAX, max(options["inputs"].node_redshifts)
+    )
 
     lcn = p21c.RectilinearLightconer.with_equal_cdist_slices(
         min_redshift=redshift,
@@ -147,15 +151,20 @@ def test_lc_runs(name, max_redshift):
         ],
         resolution=options["inputs"].user_params.cell_size,
     )
+
+    # TODO: can this be removed from the get_all_options_struct function?
+    del options["out_redshifts"]
+
     with p21c.config.use(ignore_R_BUBBLE_MAX_error=True):
-        _, _, _, lightcone = p21c.exhaust_lightcone(
+        _, _, _, lightcone = p21c.run_lightcone(
             lightconer=lcn,
             write=False,
+            cache=cache,
             **options,
         )
 
     assert isinstance(lightcone, p21c.LightCone)
-    assert np.all(np.isfinite(lightcone.brightness_temp))
+    assert np.all(np.isfinite(lightcone.lightcones["brightness_temp"]))
     assert lightcone.user_params == options["inputs"].user_params
     assert lightcone.cosmo_params == options["inputs"].cosmo_params
     assert lightcone.astro_params == options["inputs"].astro_params
@@ -163,7 +172,7 @@ def test_lc_runs(name, max_redshift):
 
 
 @pytest.mark.parametrize("name", list(OPTIONS_CTEST.keys()))
-def test_cv_runs(name, default_seed):
+def test_cv_runs(name, cache):
     redshift, kwargs = OPTIONS_CTEST[name]
     options = prd.get_all_options_struct(redshift, **kwargs)
 
@@ -174,6 +183,7 @@ def test_cv_runs(name, default_seed):
     with p21c.config.use(ignore_R_BUBBLE_MAX_error=True):
         cv = p21c.run_coeval(
             write=False,
+            cache=cache,
             **options,
         )
 
diff --git a/tests/test_singlefield.py b/tests/test_singlefield.py
index c81c49575..b5351e665 100644
--- a/tests/test_singlefield.py
+++ b/tests/test_singlefield.py
@@ -11,69 +11,76 @@
 from astropy import units as un
 
 import py21cmfast as p21c
-from py21cmfast.drivers.coeval import get_logspaced_redshifts
+from py21cmfast import InitialConditions, OutputCache, TsBox
 
 
 @pytest.fixture(scope="module")
-def ic_newseed(default_input_struct, tmpdirec):
+def ic_newseed(default_input_struct, cache: p21c.OutputCache):
     return p21c.compute_initial_conditions(
-        inputs=default_input_struct.clone(random_seed=33),
-        write=True,
-        direc=tmpdirec,
+        inputs=default_input_struct.clone(random_seed=33), write=True, cache=cache
     )
 
 
 @pytest.fixture(scope="module")
-def perturb_field_lowz(ic, default_input_struct, low_redshift):
+def perturb_field_lowz(ic: InitialConditions, low_redshift: float, cache: OutputCache):
     """A default perturb_field"""
     return p21c.perturb_field(
         redshift=low_redshift,
-        inputs=default_input_struct,
         initial_conditions=ic,
         write=True,
+        cache=cache,
     )
 
 
 @pytest.fixture(scope="module")
-def ionize_box(ic, perturbed_field, default_input_struct):
+def ionize_box(
+    ic: InitialConditions,
+    perturbed_field: p21c.PerturbedField,
+    cache: OutputCache,
+):
     """A default ionize_box"""
     return p21c.compute_ionization_field(
         initial_conditions=ic,
-        inputs=default_input_struct,
         perturbed_field=perturbed_field,
         write=True,
+        cache=cache,
     )
 
 
 @pytest.fixture(scope="module")
-def ionize_box_lowz(ic, perturb_field_lowz, default_input_struct):
+def ionize_box_lowz(
+    ic: InitialConditions,
+    perturb_field_lowz: p21c.PerturbedField,
+    cache: OutputCache,
+):
     """A default ionize_box at lower redshift."""
     return p21c.compute_ionization_field(
         initial_conditions=ic,
-        inputs=default_input_struct,
         perturbed_field=perturb_field_lowz,
         write=True,
+        cache=cache,
     )
 
 
 @pytest.fixture(scope="module")
-def spin_temp_evolution(ic, redshift, default_input_struct_ts):
+def spin_temp_evolution(ic: InitialConditions, default_input_struct_ts: TsBox, cache):
     """An example spin temperature evolution"""
     scrollz = default_input_struct_ts.node_redshifts
     st_prev = None
     outputs = []
-    for iz, z in enumerate(scrollz):
+    for z in scrollz:
         pt = p21c.perturb_field(
             redshift=z,
-            inputs=default_input_struct_ts,
             initial_conditions=ic,
+            inputs=default_input_struct_ts,
+            cache=cache,
         )
-        st = p21c.spin_temperature(
-            redshift=z,
+        st = p21c.compute_spin_temperature(
             initial_conditions=ic,
             perturbed_field=pt,
             previous_spin_temp=st_prev,
             inputs=default_input_struct_ts,
+            cache=cache,
         )
         outputs.append(
             {
@@ -87,36 +94,21 @@ def spin_temp_evolution(ic, redshift, default_input_struct_ts):
     return outputs
 
 
-def test_perturb_field_no_ic(default_input_struct, redshift, perturbed_field):
-    """Run a perturb field without passing an init box"""
-    with pytest.raises(TypeError):
-        p21c.perturb_field(redshift=redshift, user_params=default_input_struct)
-
-
-def test_ib_no_z(ic, default_input_struct):
-    with pytest.raises(TypeError):
-        p21c.compute_ionization_field(
-            initial_conditions=ic, inputs=default_input_struct
-        )
-
-
 def test_pf_unnamed_param():
     """Try using an un-named parameter."""
     with pytest.raises(TypeError):
         p21c.perturb_field(7)
 
 
-def test_perturb_field_ic(perturbed_field, default_input_struct, ic):
+def test_perturb_field_ic(perturbed_field, default_input_struct, ic, cache):
     # this will run perturb_field again, since by default regenerate=True for tests.
     # BUT it should produce exactly the same as the default perturb_field since it has
     # the same seed.
     pf = p21c.perturb_field(
-        redshift=perturbed_field.redshift,
-        initial_conditions=ic,
-        inputs=default_input_struct,
+        redshift=perturbed_field.redshift, initial_conditions=ic, cache=cache
     )
 
-    assert len(pf.density) == len(ic.lowres_density)
+    assert pf.density.shape == ic.lowres_density.shape
     assert pf.cosmo_params == ic.cosmo_params
     assert pf.user_params == ic.user_params
     assert not np.all(pf.density == 0)
@@ -127,16 +119,17 @@ def test_perturb_field_ic(perturbed_field, default_input_struct, ic):
     assert pf == perturbed_field
 
 
-def test_cache_exists(default_input_struct, perturbed_field, tmpdirec):
-    pf = p21c.PerturbedField(
+def test_cache_exists(default_input_struct, perturbed_field, cache):
+    pf = p21c.PerturbedField.new(
         redshift=perturbed_field.redshift,
         inputs=default_input_struct,
     )
 
-    assert pf.exists(tmpdirec)
+    assert cache.find_existing(pf) is not None
 
-    pf.read(tmpdirec)
-    np.testing.assert_allclose(pf.density, perturbed_field.density)
+    pf = cache.load(pf)
+    pf.load_all()
+    np.testing.assert_allclose(pf.density.value, perturbed_field.density.value)
     assert pf == perturbed_field
 
 
@@ -145,190 +138,146 @@ def test_new_seeds(
     perturb_field_lowz,
     ionize_box_lowz,
     default_input_struct,
-    tmpdirec,
+    cache,
 ):
-    inputs_newseed = default_input_struct.clone(random_seed=ic_newseed.random_seed)
     # Perturbed Field
     pf = p21c.perturb_field(
-        redshift=perturb_field_lowz.redshift,
-        initial_conditions=ic_newseed,
-        inputs=inputs_newseed,
+        redshift=perturb_field_lowz.redshift, initial_conditions=ic_newseed, cache=cache
     )
 
     # we didn't write it, and this has a different seed
-    assert not pf.exists(direc=tmpdirec)
+    assert cache.find_existing(pf) is None
     assert pf.random_seed != perturb_field_lowz.random_seed
-    assert not np.all(pf.density == perturb_field_lowz.density)
+    assert not np.all(pf.density.value == perturb_field_lowz.density.value)
 
     # Ionization Box
     with pytest.raises(ValueError):
         p21c.compute_ionization_field(
             initial_conditions=ic_newseed,
             perturbed_field=perturb_field_lowz,
-            inputs=default_input_struct,
+            cache=cache,
         )
 
     ib = p21c.compute_ionization_field(
-        initial_conditions=ic_newseed,
-        perturbed_field=pf,
-        inputs=inputs_newseed,
+        initial_conditions=ic_newseed, perturbed_field=pf, cache=cache
     )
 
     # we didn't write it, and this has a different seed
-    assert not ib.exists(direc=tmpdirec)
+    assert cache.find_existing(ib) is None
     assert ib.random_seed != ionize_box_lowz.random_seed
-    assert not np.all(ib.xH_box == ionize_box_lowz.xH_box)
+    assert not np.all(ib.xH_box.value == ionize_box_lowz.xH_box.value)
 
 
-def test_ib_from_pf(perturbed_field, ic, default_input_struct):
+def test_ib_from_pf(perturbed_field, ic, cache):
     ib = p21c.compute_ionization_field(
-        initial_conditions=ic,
-        perturbed_field=perturbed_field,
-        inputs=default_input_struct,
+        initial_conditions=ic, perturbed_field=perturbed_field, cache=cache
     )
     assert ib.redshift == perturbed_field.redshift
-    assert ib.user_params == perturbed_field.user_params
-    assert ib.cosmo_params == perturbed_field.cosmo_params
+    assert ib.inputs == perturbed_field.inputs
 
 
-def test_ib_override_global(ic, perturbed_field, default_input_struct):
-    # save previous z_heat_max
-    saved_val = p21c.global_params.Pop2_ion
+# def test_ib_override_global(ic, perturbed_field, default_input_struct):
+#     # save previous z_heat_max
+#     saved_val = p21c.global_params.Pop2_ion
 
-    p21c.compute_ionization_field(
-        initial_conditions=ic,
-        perturbed_field=perturbed_field,
-        inputs=default_input_struct,
-        pop2_ion=3500,
-    )
+#     p21c.compute_ionization_field(
+#         initial_conditions=ic,
+#         perturbed_field=perturbed_field,
+#         inputs=default_input_struct,
+#         pop2_ion=3500,
+#     )
 
-    assert p21c.global_params.Pop2_ion == saved_val
+#     assert p21c.global_params.Pop2_ion == saved_val
 
 
-def test_ib_bad_st(ic, default_input_struct, perturbed_field, redshift):
-    with pytest.raises((ValueError, AttributeError)):
+def test_ib_bad_st(ic, default_input_struct, perturbed_field, redshift, cache):
+    with pytest.raises(TypeError, match="spin_temp should be of type TsBox"):
         p21c.compute_ionization_field(
             inputs=default_input_struct,
             initial_conditions=ic,
             perturbed_field=perturbed_field,
             spin_temp=ic,
+            cache=cache,
         )
 
 
-def test_bt(ionize_box, default_input_struct, spin_temp_evolution, perturbed_field):
+def test_bt(
+    ionize_box, default_input_struct, spin_temp_evolution, perturbed_field, cache
+):
     curr_st = spin_temp_evolution[-1]["spin_temp"]
-    with pytest.raises(TypeError):  # have to specify param names
-        p21c.brightness_temperature(
-            default_input_struct, ionize_box, curr_st, perturbed_field
-        )
+    # with pytest.raises(TypeError):  # have to specify param names
+    #     p21c.brightness_temperature(
+    #         default_input_struct, ionize_box, curr_st, perturbed_field
+    #     )
 
     # this will fail because ionized_box was not created with spin temperature.
     with pytest.raises(ValueError):
         p21c.brightness_temperature(
-            inputs=default_input_struct,
+            #            inputs=default_input_struct,
             ionized_box=ionize_box,
             perturbed_field=perturbed_field,
             spin_temp=curr_st,
+            cache=cache,
         )
 
     bt = p21c.brightness_temperature(
-        ionized_box=ionize_box,
-        perturbed_field=perturbed_field,
-        inputs=default_input_struct,
+        ionized_box=ionize_box, perturbed_field=perturbed_field, cache=cache
     )
 
-    assert bt.cosmo_params == perturbed_field.cosmo_params
-    assert bt.user_params == perturbed_field.user_params
-    assert bt.flag_options == ionize_box.flag_options
-    assert bt.astro_params == ionize_box.astro_params
+    assert bt.inputs == perturbed_field.inputs
 
 
-def test_coeval_against_direct(ic, perturbed_field, ionize_box, default_input_struct):
+def test_coeval_against_direct(
+    ic: p21c.InitialConditions,
+    perturbed_field: p21c.PerturbedField,
+    ionize_box: p21c.IonizedBox,
+    cache,
+):
     coeval = p21c.run_coeval(
-        perturbed_field=perturbed_field,
-        initial_conditions=ic,
-        inputs=default_input_struct,
+        perturbed_field=perturbed_field, initial_conditions=ic, cache=cache
     )
 
-    assert coeval.init_struct == ic
-    assert coeval.perturb_struct == perturbed_field
-    assert coeval.ionization_struct == ionize_box
+    assert coeval.initial_conditions == ic
+    assert coeval.perturbed_field == perturbed_field
+    assert coeval.ionized_box == ionize_box
 
 
-def test_using_cached_halo_field(ic, test_direc, default_input_struct):
+def test_using_cached_halo_field(ic, test_direc):
     """Test whether the C-based memory in halo fields is cached correctly.
 
     Prior to v3.1 this was segfaulting, so this test ensure that this behaviour does
     not regress.
     """
-    inputs = default_input_struct.evolve_input_structs(USE_HALO_FIELD=True)
+    cache = OutputCache(test_direc)
     halo_field = p21c.determine_halo_list(
-        redshift=10.0,
-        initial_conditions=ic,
-        inputs=inputs,
-        write=True,
-        direc=test_direc,
+        redshift=10.0, initial_conditions=ic, write=True, cache=cache
     )
 
     pt_halos = p21c.perturb_halo_list(
         initial_conditions=ic,
-        inputs=inputs,
         halo_field=halo_field,
         write=True,
-        direc=test_direc,
+        cache=cache,
     )
 
-    print("DONE WITH FIRST BOXES!")
     # Now get the halo field again at the same redshift -- should be cached
     new_halo_field = p21c.determine_halo_list(
         redshift=10.0,
         initial_conditions=ic,
-        inputs=inputs,
         write=False,
         regenerate=False,
     )
 
     new_pt_halos = p21c.perturb_halo_list(
-        redshift=10.0,
         initial_conditions=ic,
-        inputs=inputs,
         halo_field=new_halo_field,
         write=False,
         regenerate=False,
     )
 
-    np.testing.assert_allclose(new_halo_field.halo_masses, halo_field.halo_masses)
-    np.testing.assert_allclose(pt_halos.halo_coords, new_pt_halos.halo_coords)
-
-
-def test_first_box(default_input_struct_ts):
-    """Tests whether the first_box idea works for spin_temp.
-    This test was breaking before we set the z_heat_max box to actually get
-    the correct dimensions (before it was treated as a dummy).
-    """
-    inputs = default_input_struct_ts.evolve_input_structs(
-        HII_DIM=default_input_struct_ts.user_params.HII_DIM + 1
+    np.testing.assert_allclose(
+        new_halo_field.halo_masses.value, halo_field.halo_masses.value
     )
-    initial_conditions = p21c.compute_initial_conditions(
-        inputs=inputs,
-        random_seed=1,
+    np.testing.assert_allclose(
+        pt_halos.halo_coords.value, new_pt_halos.halo_coords.value
     )
-
-    prevst = None
-    for z in [default_input_struct_ts.user_params.Z_HEAT_MAX + 1e-2, 29.0]:
-        print(f"z={z}")
-        perturbed_field = p21c.perturb_field(
-            inputs=inputs,
-            redshift=z,
-            initial_conditions=initial_conditions,
-        )
-
-        spin_temp = p21c.spin_temperature(
-            initial_conditions=initial_conditions,
-            perturbed_field=perturbed_field,
-            inputs=inputs,
-            previous_spin_temp=prevst,
-        )
-        prevst = spin_temp
-
-    assert spin_temp.redshift == 29.0