From 41970608d25ebed89a5fde5655a5ecdedf092478 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Sun, 28 Jul 2024 20:15:01 -0400
Subject: [PATCH] upd

---
 docs/assets/images/howto/nt2-demo-5.png   | Bin 0 -> 43745 bytes
 docs/code/problem_generators.md           |  95 +++++++++++++++++++-
 docs/getting-started/compile-run.md       |  65 ++++++++++++++
 docs/getting-started/docker.md            |   2 +-
 docs/getting-started/faq.md               |  68 +++-----------
 docs/getting-started/vis.md               | 105 +++++++++++++---------
 extra_sass/elements/_admonitions.css.scss |  22 +++++
 7 files changed, 259 insertions(+), 98 deletions(-)
 create mode 100644 docs/assets/images/howto/nt2-demo-5.png

diff --git a/docs/assets/images/howto/nt2-demo-5.png b/docs/assets/images/howto/nt2-demo-5.png
new file mode 100644
index 0000000000000000000000000000000000000000..9bb35681881a5cf58c38171aaaf018ace7f8f5bb
GIT binary patch
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HcmV?d00001

diff --git a/docs/code/problem_generators.md b/docs/code/problem_generators.md
index 1c9f022..a5917f5 100644
--- a/docs/code/problem_generators.md
+++ b/docs/code/problem_generators.md
@@ -416,6 +416,99 @@ Again, as everything else in the problem generator, the force (rather, the accel
     Note, that among the functions mentioned throughout this section, you may specify only the ones you actually need, and ignore the ones you don't (i.e., there is no need to provide dummy functions that return zero), as the code will automatically determine at compile-time which functions are present.
 
 
+## Custom field output
+
+The code also allows for custom-defined fields to be written together with other field quantities during the output. To enable that, simply define the name of your field in the input file:
+
+```toml
+[output.fields]
+  ...
+  custom = ["my_field"]
+  ...
+```
+
+There can be as many custom fields as one needs. And then in the problem generator, populate the corresponding field by defining the following function:
+
+```c++
+void CustomFieldOutput(const std::string&    name,//(1)!
+                        ndfield_t<M::Dim, 6> buffer,//(2)!
+                        std::size_t          index,//(3)!
+                        const Domain<S, M>&  domain) {//(4)!
+  if (name == "my_field") {
+    // 1D example (can be easily generalized)
+    if constexpr (M::Dim == Dim::_1D) {
+      const auto& EM = domain.fields.em;
+      Kokkos::parallel_for(
+        "MyField",
+        domain.mesh.rangeActiveCells(),
+        Lambda(index_t i1) {
+          const auto      i1_ = COORD(i1);
+          coord_t<M::Dim> x_Ph { ZERO };
+          // convert coordinate to physical basis:
+          metric.template convert<Crd::Cd, Crd::Ph>({ i1_ }, x_Ph);
+          // compute whatever needs to be written
+          // ... may also depend on the EM fields from the `domain`
+          // ... in this example -- output Ex * x^2
+          buffer(i1, index) = SQR(x_Ph[0]) *
+                              metric.template transform<1, Idx::U, Idx::T>(
+                                { i1_ + HALF },
+                                EM(i1, em::ex1));
+          // here we also convert Ex1(i + 1/2) to Tetrad basis
+        });
+    }
+  } else {
+    raise::Error("Custom output not provided", HERE);
+  }
+}
+```
+
+1. the same name that went into the input file
+2. buffer array where the field is going to be written into
+3. an index of the buffer array where the field is written into
+4. reference of the local subdomain
+
+Alternatively, you can precompute the desired quantity in the `CustomPostStep` function and then simply copy to the buffer in the same function:
+
+```c++
+// assuming 2D and that the desired quantity is saved in `cbuff`
+template <SimEngine::type S, class M>
+struct PGen : public arch::ProblemGenerator<S, M> {
+  // ...
+
+  array_t<real_t**> cbuff;
+  
+  // ...
+
+  void CustomPostStep(std::size_t step, long double, Domain<S, M>& domain) {
+    if (step == 0) {
+      // allocate the array at time = 0
+      cbuff = array_t<real_t**>("cbuff",
+                                domain.mesh.n_all(in::x1),
+                                domain.mesh.n_all(in::x2));
+    }
+    // populate the buffer (can be done at specific timesteps)
+    Kokkos::parallel_for(
+      "FillCbuff",
+      domain.mesh.rangeActiveCells(),
+      Lambda(index_t i1, index_t i2) {
+        // ...
+      });
+  }
+
+  void CustomFieldOutput(const std::string&    name,
+                          ndfield_t<M::Dim, 6> buffer,
+                          std::size_t          index,
+                          const Domain<S, M>&) {
+    if (name == "my_field") {
+      Kokkos::deep_copy(Kokkos::subview(buffer, Kokkos::ALL, Kokkos::ALL, index), cbuff);
+    } else {
+      // ...
+    }
+  }
+};
+```
+
+Keep in mind that the custom field output is written as-is, i.e., no additional interpolation or transformation is applied. So make sure the quantity you output is covariant (i.e., does not depend on the resolution or the stretching of coordinates; essentially, always output "physical" covariant/contravariant vectors or transform them to the tetrad basis).
 
 {% include "html/d3js.html" %}
-<script src="../atm-boundaries.js">
\ No newline at end of file
+<script src="../atm-boundaries.js">
diff --git a/docs/getting-started/compile-run.md b/docs/getting-started/compile-run.md
index 9b48b3d..7863bfb 100644
--- a/docs/getting-started/compile-run.md
+++ b/docs/getting-started/compile-run.md
@@ -130,3 +130,68 @@ To run only specific tests, you can use the `-R` flag followed by the regular ex
 ```shell
 ctest --test-dir build/ -R particle
 ```
+
+## Specific architectures
+
+
+### HIP/ROCm @ AMD GPUs
+
+Compiling on AMD GPUs is typically not an issue: 
+
+1. Make sure you have the ROCm library loaded: e.g., run `rocminfo`;
+2. Sometimes the environment variables are not properly set up, so make sure you have the following variables properly defined: 
+
+- `CMAKE_PREFIX_PATH=/opt/rocm` (or wherever ROCm is installed),
+- `CC=hipcc` & `CXX=hipcc`,
+- in rare occasions, you might have to also explicitly pass `-D CMAKE_CXX_COMPILER=hipcc -D CMAKE_C_COMPILER=hipcc` to cmake during the configuration stage;
+
+3. Compile the code with proper Kokkos flags; i.e., for MI250x GPUs you would use: `-D Kokkos_ENABLE_HIP=ON` and `-D Kokkos_ARCH_AMD_GFX90A=ON`.
+
+Now running is a bit trickier and the exact instruction might vary from machine to machine (part of it is because ROCm is much less streamlined than CUDA, but also system administrators on clusters are often more negligent towards AMD GPUs). 
+
+* If you are running this on a cluster -- the first thing to do is to inspect the documentation of the cluster. There you might find the proper `slurm` command for requesting GPU nodes and binding each GPU to respective CPUs. 
+
+* On personal machines figuring this out is a bit easier. First, inspect the output of `rocminfo` and `rocm-smi`. From there, you should be able to find the ID of the GPU you want to use. If you see more than one device -- that means you either have an additional AMD CPU, or an integrated GPU installed as well; ignore them. You will need to override two environment variables:
+
+- `HSA_OVERRIDE_GFX_VERSION` set to GFX version that you used to compile the code (if you used `GFX1100` Kokkos flag, that would be `11.0.0`);
+- `HIP_VISIBLE_DEVICES`, and `ROCR_VISIBLE_DEVICES` both need to be set to your device ID (usually, it's just a number from 0 to the number of devices that support HIP).
+
+For example, the output of `rocminfo | grep -A 5 "Agent "` may look like this:
+```
+Agent 1                  
+*******                  
+  Name:                    AMD Ryzen 9 7940HS w/ Radeon 780M Graphics
+  Uuid:                    CPU-XX                             
+  Marketing Name:          AMD Ryzen 9 7940HS w/ Radeon 780M Graphics
+  Vendor Name:             CPU                                
+--
+Agent 2                  
+*******                  
+  Name:                    gfx1100                            
+  Uuid:                    GPU-XX                             
+  Marketing Name:          AMD Radeon™ RX 7700S             
+  Vendor Name:             AMD                                
+--
+Agent 3                  
+*******                  
+  Name:                    gfx1100                            
+  Uuid:                    GPU-XX                             
+  Marketing Name:          AMD Radeon Graphics                
+  Vendor Name:             AMD
+```
+In this case, the required GPU is the `Agent 2`, which supports GFX1100. `rocm-smi` will look something like this:
+```
+============================================ ROCm System Management Interface ============================================
+====================================================== Concise Info ======================================================
+Device  Node  IDs              Temp    Power    Partitions          SCLK  MCLK     Fan    Perf  PwrCap       VRAM%  GPU%  
+              (DID,     GUID)  (Edge)  (Avg)    (Mem, Compute, ID)                                                        
+==========================================================================================================================
+0       1     0x7480,   19047  35.0°C  0.0W     N/A, N/A, 0         0Mhz  96Mhz    29.8%  auto  100.0W       0%     0%    
+1       2     0x15bf,   17218  48.0°C  19.111W  N/A, N/A, 0         None  1000Mhz  0%     auto  Unsupported  82%    5%    
+==========================================================================================================================
+================================================== End of ROCm SMI Log ===================================================
+```
+so the GPU we need has `Device` ID of `0` (since it's the dedicated GPU, it might automatically turn off when idle to save power on laptops; hence `Power = 0.0W`). Now we can run the code with: 
+```sh
+HSA_OVERRIDE_GFX_VERSION=11.0.0 HIP_VISIBLE_DEVICES=0 ROCR_VISIBLE_DEVICES=0 ./executable ...
+```
diff --git a/docs/getting-started/docker.md b/docs/getting-started/docker.md
index 630d1fe..9cdd0d7 100644
--- a/docs/getting-started/docker.md
+++ b/docs/getting-started/docker.md
@@ -50,7 +50,7 @@ We provide the following configurations (tags) as separate images:
 Images for these containers are all stored on the [Docker hub](https://hub.docker.com/repository/docker/morninbru/entity/general). If you do not wish to use `docker compose` to download the pre-made ones from the Docker hub, you may also build the images yourself from the corresponding `Dockerfile`-s also provided with the source code. You can do that by going to the `dev/` directory in the root of the source code, and running: 
 
 ```sh
-docker build --no-cache -t myentity:<toolkit> -f Dockerfile.<toolkit>
+docker build --no-cache -t myentity:<toolkit> -f Dockerfile.<toolkit> .
 ```
 
 substituting one of the values for the `<toolkit>` mentioned above. You may then launch a container using the built image by running the following from the code source directory (or any other directory you wish to mount inside the container):
diff --git a/docs/getting-started/faq.md b/docs/getting-started/faq.md
index ebc6330..45199ae 100644
--- a/docs/getting-started/faq.md
+++ b/docs/getting-started/faq.md
@@ -7,69 +7,25 @@ hide:
 
     Here we collect the most frequent questions that might occur. Please, make sure to inspect this section before filing a GitHub issue.
 
+## Code usage
 
-??? faq "Compiling & running on AMD GPUs"
+??? faq "I want to have a custom ..."
     
-    Compiling on AMD GPUs is typically not an issue: 
+    Here, "..." can be "boundary/injection conditions," "driving," "injection," "distribution function," "output." All of that *can* be done via the tools provided by the problem generator. Please inspect carefully the [section dedicated to that](../code/problem_generators.md).
 
-    1. Make sure you have the ROCm library loaded: e.g., run `rocminfo`;
-    2. Sometimes the environment variables are not properly set up, so make sure you have the following variables properly defined: 
 
-        - `CMAKE_PREFIX_PATH=/opt/rocm` (or wherever ROCm is installed),
-        - `CC=hipcc` & `CXX=hipcc`
-    
-    3. Compile the code with proper Kokkos flags; i.e., for MI250x GPUs you would use: `-D Kokkos_ENABLE_HIP=ON` and `-D Kokkos_ARCH_AMD_GFX1100=ON`.
+## Technical
 
-    Now running is a bit trickier and the exact instruction might vary from machine to machine (part of it is because ROCm is much less streamlined than CUDA, but also system administrators on clusters are often more negligent towards AMD GPUs). 
-    
-    * If you are running this on a cluster -- the first thing to do is to inspect the documentation of the cluster. There you might find the proper `slurm` command for requesting GPU nodes and binding each GPU to respective CPUs. 
 
-    * On personal machines figuring this out is a bit easier. First, inspect the output of `rocminfo` and `rocm-smi`. From there, you should be able to find the ID of the GPU you want to use. If you see more than one device -- that means you either have an additional AMD CPU, or an integrated GPU installed as well; ignore them. You will need to override two environment variables:
-    
-        - `HSA_OVERRIDE_GFX_VERSION` set to GFX version that you used to compile the code (for most recent AMD cards that would be `11.0.0` (if you used `GFX1100` Kokkos flag);
-        - `HIP_VISIBLE_DEVICES`, and `ROCR_VISIBLE_DEVICES` both need to be set to your device ID (usually, it's just a number from 0 to the number of devices that support HIP).
+??? faq "Running in a `docker` container with an AMD card"
 
-        For example, the output of `rocminfo | grep -A 5 "Agent "` may look like this:
-        ```
-        Agent 1                  
-        *******                  
-          Name:                    AMD Ryzen 9 7940HS w/ Radeon 780M Graphics
-          Uuid:                    CPU-XX                             
-          Marketing Name:          AMD Ryzen 9 7940HS w/ Radeon 780M Graphics
-          Vendor Name:             CPU                                
-        --
-        Agent 2                  
-        *******                  
-          Name:                    gfx1100                            
-          Uuid:                    GPU-XX                             
-          Marketing Name:          AMD Radeon™ RX 7700S             
-          Vendor Name:             AMD                                
-        --
-        Agent 3                  
-        *******                  
-          Name:                    gfx1100                            
-          Uuid:                    GPU-XX                             
-          Marketing Name:          AMD Radeon Graphics                
-          Vendor Name:             AMD
-        ```
-        In this case, the required GPU is the `Agent 2`, which supports GFX1100. `rocm-smi` will look something like this:
-        ```
-        ============================================ ROCm System Management Interface ============================================
-        ====================================================== Concise Info ======================================================
-        Device  Node  IDs              Temp    Power    Partitions          SCLK  MCLK     Fan    Perf  PwrCap       VRAM%  GPU%  
-                      (DID,     GUID)  (Edge)  (Avg)    (Mem, Compute, ID)                                                        
-        ==========================================================================================================================
-        0       1     0x7480,   19047  35.0°C  0.0W     N/A, N/A, 0         0Mhz  96Mhz    29.8%  auto  100.0W       0%     0%    
-        1       2     0x15bf,   17218  48.0°C  19.111W  N/A, N/A, 0         None  1000Mhz  0%     auto  Unsupported  82%    5%    
-        ==========================================================================================================================
-        ================================================== End of ROCm SMI Log ===================================================
-        ```
-        so the GPU we need has `Device` ID of `0` (since it's the dedicated GPU, it might automatically turn off when idle to save power on laptops; hence `Power = 0.0W`). Now we can run the code with: 
-        ```sh
-        HSA_OVERRIDE_GFX_VERSION=11.0.0 HIP_VISIBLE_DEVICES=0 ROCR_VISIBLE_DEVICES=0 ./executable ...
-        ```
+    AMD has a vary [brief documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html) on the topic. In theory the `docker` containers that come with the code should work. Just make sure you have the proper groups (`render` and `video`) defined and added to the current user. If it complains about access to `/dev/kfd`, You might have to run docker as a root.
 
 
-??? faq "Running in a `docker` container with an AMD card"
+??? faq "Compilation errors"
+    
+    Before merging with the released stable version, the code is tested on CUDA and HIP GPU compilers, as well as few version of CPU compilers (GCC 9...11, and LLVM 13...17). If you are encountering compiler errors on GPUs, first thing to check is whether the compilers are set up properly (i.e., whether CMake indeed captures the right compilers). Here are a few tips:
 
-    AMD has a vary [brief documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html) on the topic. In theory the `docker` containers that come with the code should work. Just make sure you have the proper groups (`render` and `video`) defined and added to the current user. If it complains about access to `/dev/kfd`, You might have to run docker as a root.
+    - CUDA @ NVIDIA GPUs: make sure you have a version of `gcc` which is supported by the version of CUDA you are using; check out [this unofficial compatibility matrix](https://gist.github.com/ax3l/9489132#nvcc). In particular, Intel compilers are not very compatible with CUDA, and it is recommended to use `gcc` instead (you won't gain much by using Intel anyway, since CUDA will be doing the heavy-lifting).
+  
+    - HIP/ROCm @ AMD GPUs: ROCm library is a headache. The documentation is even more so. We have a [dedicated section](./compile-run.md#hiprocm-amd-gpus) specifically discussing compilation with HIP. Make sure to check it before opening an issue.
diff --git a/docs/getting-started/vis.md b/docs/getting-started/vis.md
index f045ec5..98c4113 100644
--- a/docs/getting-started/vis.md
+++ b/docs/getting-started/vis.md
@@ -52,7 +52,7 @@ The output is configured using the following configurations in the `input` file:
 12. Min/max energies for binning the energy distribution [default to 1e-3 -> 1e3]
 13. whether to use logarithmic energy bins or linear
 
-Output is written in the run directory in a single `hdf5` file: `MySimulation.h5`. All the steps are written in the same file, and the time step is stored as an attribute of the dataset: `Step0`, `Step1`, `Step2`, etc. Thus to access, say, the `Ez` field at the 10th output step (not the same as the simulation timestep), one has to access the dataset `/Step9/Ez` in the `hdf5` file. If one needs the `X1` coordinates of particles of species 2 at the 5th output step, one has to access the dataset `/Step4/X1_2` in the `hdf5` file, etc.
+Output is written in the run directory in a single `hdf5` file: `MySimulation.h5`. All the steps are written in the same file, and the time step is stored as an attribute of the dataset: `Step0`, `Step1`, `Step2`, etc. Thus to access, say, the `Ez` field at the 10th output step (not the same as the simulation timestep), one has to access the dataset `/Step9/fE3` in the `hdf5` file. If one needs the `X1` coordinates of particles of species 2 at the 5th output step, one has to access the dataset `/Step4/pX1_2` in the `hdf5` file, etc.
 
 Following is the list of the supported fields
 
@@ -84,15 +84,16 @@ and particle quantities
 !!! note "Refining fields and particle quantities for the output"
 
     One can specify particular components to output for the `Tij` fields: `T0i` will output the `T00`, `T01`, and `T02` components, while `Tii` will output only the diagonal components: `T11`, `T22`, and `T33`, and `Tij` will output all the 6 components. One can also specify the particle species which will be used to compute the moments or output particle quantities: `Rho_1` (density of species 1), `N_2_3` (number density of species 2 and 3), `Tij_1_3` (energy-momentum tensor for species 1 and 3), etc. 
-    <!-- Or for the particle quantities: `X_1_2` will write the coordinates of particles of species 1 and 2 only. If no species are specified, the moments will be computed for all the species with $m_s \ne 0$. -->
 
 All of the vector fields are interpolated to cell centers before the output, and converted to orthonormal basis. The particle-based moments are smoothed with a stencil (specified in the input file; `mom_smooth`) for each particle.
 
+In addition, one can write custom user-defined field quantities to the output with the fields. Refer to [the following section](../code/problem_generators.md#custom-field-output) for more details.
+
 !!! warning "Can one track particles at different times?"
 
     Particle tracking (outputting the same batch of particles at every timestep) is unfortunately not yet implemented, and will unlikely be available due to limitations imposed by the nature of GPU computations.
 
-## `nt2py`
+## [`nt2py`](https://pypi.org/project/nt2py/)
 
 We provide the `nt2py` python package to help easily access and manipulate the simulation data. `nt2py` package uses the [`dask`](https://docs.dask.org/en/stable/) and [`xarray`](https://docs.xarray.dev/en/stable/) libraries together with [`h5py`](https://pypi.org/project/h5py/) and [`h5pickle`](https://github.com/DaanVanVugt/h5pickle) to [lazily load](https://en.wikipedia.org/wiki/Lazy_loading) the output data and provide a convenient interface for the data analysis and quick visualization. 
 
@@ -190,38 +191,6 @@ Particles and spectra can, in turn, be accessed via `data.particles[s]`, where `
 
     You can access the documentation of the `nt2py` functions and methods of the `Data` object by calling `nt2r.<function>?` in the jupyter notebook or `help(nt2r.<function>)` in the python console.
 
-### Exporting movies
-
-To produce animations, `nt2py` provides a shortcut helper function which saves the frames using multiple threads, and then calls `ffmpeg` to merge them into a video file. 
-
-```python
-def plot_frame(ti, data):
-    # function must take two parameters:
-    # - ti: output index
-    # - data: the dataset loaded with nt2.read
-    #
-    # any type data manipulation & plotting routine goes here
-    # e.g.
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    (data.N_1 + data.N_2).isel(t=ti).plot(ax=ax, cmap="viridis")
-    #                      ^
-    #                 selecting timestep by index
-
-# then simply pass this function to the routine:
-data.makeMovie(plot_frame, num_cpus=8, framerate="10", ...)
-#                                   ^
-#                 (optional) by default all available threads are used
-```
-
-`makeMovie` also accepts a number of arguments used by `ffmpeg`, such as the framerate, the compression rate, etc. Run the following to see all the arguments:
-
-```python
-import nt2.export as nt2e
-nt2e.makeMovie?
-```
-
-<!-- 
 ### Accessing particles
 
 Particles are stored in the same `data` object and are lazily preloaded when one calls `nt2.Data(...)`, as we did above. To access the particle data, use `data.particles`, which returns a python dictionary where the key is particles species index, and the value is an `xarray` Dataset with the particle data. For example, to access the `x` and `y` coordinates of the first species, one can do:
@@ -237,8 +206,6 @@ The shape of the returned dataset is number of particles times the number of tim
 data.particles[3].isel(t=10).x
 ```
 
-![nt2demo3](../assets/images/howto/nt2-demo-3.png){width=50%, align=right} 
-
 Scatter plotting the particles on a 2D plane is quite easy, since `xarray` has a built-in `plot.scatter` method:
 
 ```python
@@ -253,12 +220,14 @@ species_4.isel(t=-1)\
                 label=species_4.attrs["label"])
 ```
 
+??? showplot "scatter plot $\{x,~y\}$"
+
+    ![nt2demo3](../assets/images/howto/nt2-demo-3.png)
+
 !!! code "`isel` indexing"
 
     `isel(t=-1)` selects the last time step.
 
-![nt2demo4](../assets/images/howto/nt2-demo-4.png){width=50%, align=right} 
-
 Or one can plot the same in phase space:
 
 ```python
@@ -270,4 +239,60 @@ species_4.isel(t=-1)\
                 label=species_4.attrs["label"])
 ```
 
--->
\ No newline at end of file
+??? showplot "scatter plot $\{u_x,~u_y\}$"
+
+    ![nt2demo4](../assets/images/howto/nt2-demo-4.png)
+
+
+### Accessing runtime spectra
+
+Distribution functions for all particle species in the box are also written with the data at specified timesteps. These can be accessed via `data.spectra`, which has several different fields. As in particles & fields, you can access the data at different times using `data.spectra.isel(t=...)` or `data.spectra.sel(t=...)`. The energy bins are written into `data.spectra.e`; by default, the binning is done logarithmically in $\gamma - 1$ for massive particles and energy, $E$, for the photons. Below is an example script to build a distribution function of electron-positron pairs at output step `t=450`:
+
+```python
+sp = data.spectra.isel(t=450)
+
+plt.figure(figsize=(6, 3))
+plt.xscale("log")
+plt.yscale("log")
+plt.plot(sp.e, sp.n_1 + sp.n_2, c="r")
+plt.ylim(10, 3e5)
+plt.xlabel(r"$\gamma - 1$")
+plt.xlim(sp.e.min(), 1)
+```
+
+???+ showplot "particle spectra"
+
+    ![nt2demo5](../assets/images/howto/nt2-demo-5.png)
+
+---
+
+### Exporting movies
+
+To produce animations, `nt2py` provides a shortcut helper function which saves the frames using multiple threads, and then calls `ffmpeg` to merge them into a video file. 
+
+```python
+def plot_frame(ti, data):
+    # function must take two parameters:
+    # - ti: output index
+    # - data: the dataset loaded with nt2.read
+    #
+    # any type data manipulation & plotting routine goes here
+    # e.g.
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    (data.N_1 + data.N_2).isel(t=ti).plot(ax=ax, cmap="viridis")
+    #                      ^
+    #                 selecting timestep by index
+
+# then simply pass this function to the routine:
+data.makeMovie(plot_frame, num_cpus=8, framerate="10", ...)
+#                                   ^
+#                 (optional) by default all available threads are used
+```
+
+`makeMovie` also accepts a number of arguments used by `ffmpeg`, such as the framerate, the compression rate, etc. Run the following to see all the arguments:
+
+```python
+import nt2.export as nt2e
+nt2e.makeMovie?
+```
diff --git a/extra_sass/elements/_admonitions.css.scss b/extra_sass/elements/_admonitions.css.scss
index cdcdef1..24b201f 100644
--- a/extra_sass/elements/_admonitions.css.scss
+++ b/extra_sass/elements/_admonitions.css.scss
@@ -3,6 +3,7 @@
 :root {
   --md-admonition-icon--code: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M5 3h2v2H5v5a2 2 0 0 1-2 2 2 2 0 0 1 2 2v5h2v2H5c-1.07-.27-2-.9-2-2v-4a2 2 0 0 0-2-2H0v-2h1a2 2 0 0 0 2-2V5a2 2 0 0 1 2-2m14 0a2 2 0 0 1 2 2v4a2 2 0 0 0 2 2h1v2h-1a2 2 0 0 0-2 2v4a2 2 0 0 1-2 2h-2v-2h2v-5a2 2 0 0 1 2-2 2 2 0 0 1-2-2V5h-2V3h2m-7 12a1 1 0 0 1 1 1 1 1 0 0 1-1 1 1 1 0 0 1-1-1 1 1 0 0 1 1-1m-4 0a1 1 0 0 1 1 1 1 1 0 0 1-1 1 1 1 0 0 1-1-1 1 1 0 0 1 1-1m8 0a1 1 0 0 1 1 1 1 1 0 0 1-1 1 1 1 0 0 1-1-1 1 1 0 0 1 1-1Z" /></svg>');
   --md-admonition-icon--faq: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10.97 8.265a1.45 1.45 0 0 0-.487.57.75.75 0 0 1-1.341-.67c.2-.402.513-.826.997-1.148C10.627 6.69 11.244 6.5 12 6.5c.658 0 1.369.195 1.934.619a2.45 2.45 0 0 1 1.004 2.006c0 1.033-.513 1.72-1.027 2.215-.19.183-.399.358-.579.508l-.147.123a4.329 4.329 0 0 0-.435.409v1.37a.75.75 0 1 1-1.5 0v-1.473c0-.237.067-.504.247-.736.22-.28.486-.517.718-.714l.183-.153.001-.001c.172-.143.324-.27.47-.412.368-.355.569-.676.569-1.136a.953.953 0 0 0-.404-.806C12.766 8.118 12.384 8 12 8c-.494 0-.814.121-1.03.265ZM13 17a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"/><path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Z"/></svg>');
+  --md-admonition-icon--showplot: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" width="24" height="24"><path d="M10.25 2a8.25 8.25 0 0 1 6.34 13.53l5.69 5.69a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215l-5.69-5.69A8.25 8.25 0 1 1 10.25 2ZM3.5 10.25a6.75 6.75 0 1 0 13.5 0 6.75 6.75 0 0 0-13.5 0Z"></path></svg>');
 }
 
 // code
@@ -44,3 +45,24 @@
     }
   }
 }
+
+// result
+.md-typeset .admonition.showplot,
+.md-typeset details.showplot {
+  border-color: var(--md-default-fg-color);
+  font-size: 1.0em;
+}
+
+.md-typeset .showplot {
+  &>.admonition-title,
+  &>summary {
+    font-size: 1.1em;
+    background-color: var(--md-code-bg-color);
+
+    &::before {
+      -webkit-mask-image: var(--md-admonition-icon--showplot);
+      mask-image: var(--md-admonition-icon--showplot);
+      background-color: var(--md-default-fg-color);
+    }
+  }
+}