diff --git a/examples/Pyvista_drive_demo.ipynb b/examples/Pyvista_drive_demo.ipynb
new file mode 100644
index 0000000..eccaa50
--- /dev/null
+++ b/examples/Pyvista_drive_demo.ipynb
@@ -0,0 +1,688 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a3aa125a",
+ "metadata": {},
+ "source": [
+ "## ***README***\n",
+ "\n",
+ "There is a bug where the pyvista grid is removed when moving between time steps with the slider. A temporary fix is possible if you are using this notebook locally and have the developer version of Ubermag.\n",
+ "\n",
+ "A few lines of code from the [pyvsita_field.py](https://github.com/ubermag/discretisedfield/blob/master/discretisedfield/plotting/pyvista_field.py) file will have to be commented out. These are:\n",
+ "- **Line 172**\n",
+ "- **Line 354**\n",
+ "- **Line 573**\n",
+ "- **Line 731**\n",
+ "\n",
+ "These lines relate to the region bounds created with the pyvista plots.\n",
+ "\n",
+ "When running the functions in the notebook click on the \"toggle ruler\" icon to show the grid.\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc3d9a44",
+ "metadata": {},
+ "source": [
+ "# Pyvista Drive Demo\n",
+ "\n",
+ "In Ubermag we can visualise micromagnetic (mm) systems with Holoviews using the function `drive.hv()`. `Drive.hv()` allows us to view the change in time of a mm system when a Time Driver simulation is run. \n",
+ "\n",
+ "We can also use Pyvista which is a 3D visualisation tool to also visual mm systems, however we currently do not have the ability to visualise all the time steps at once. Instead the user has to specify which time step they want to see by indexing the drive object as shown below: \n",
+ "\n",
+ "`Drive[-1].pyvista.vector()`\n",
+ "\n",
+ "This notebook is a demo on how we can replicate the functionality of `drive.hv()` to Pyvista so that we can see all the timesteps of the mm system."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1457535",
+ "metadata": {},
+ "source": [
+ "## System Setup\n",
+ "\n",
+ "First we import our modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5f1ff354",
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+ },
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+ "\n",
+ ""
+ ]
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+ "source": [
+ "import pyvista as pv\n",
+ "import discretisedfield as df\n",
+ "import micromagneticmodel as mm\n",
+ "import micromagneticdata as mdata\n",
+ "import oommfc as oc\n",
+ "import panel as pn\n",
+ "\n",
+ "pn.extension()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b60581e3",
+ "metadata": {},
+ "source": [
+ "Below we initialise our system"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d2819758",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Geometry\n",
+ "r = 50e-9 # Radius of the thin nano magnetic disk (m)\n",
+ "thickness = 10e-9 # sample thickness (m)\n",
+ "\n",
+ "# Material (Permalloy) parameters\n",
+ "Ms = 1e5 # saturation magnetisation (A/m)\n",
+ "A = 13e-12 # exchange energy constant (J/m)\n",
+ "\n",
+ "# Dynamics (LLG equation) parameters\n",
+ "gamma0 = mm.consts.gamma0 # gyromagnetic ratio (m/As)\n",
+ "alpha = 0.05 # Gilbert damping\n",
+ "\n",
+ "system = mm.System(name=\"test_1\")\n",
+ "\n",
+ "# Energy equation. We omit Zeeman energy term, because H=0.\n",
+ "system.energy = mm.Exchange(A=A) + mm.Demag()\n",
+ "\n",
+ "# Dynamics equation\n",
+ "system.dynamics = mm.Precession(gamma0=gamma0) + mm.Damping(alpha=alpha)\n",
+ "\n",
+ "\n",
+ "# initial magnetisation state\n",
+ "def m_init(point):\n",
+ " x, y, _ = point\n",
+ " c = 1e9 # (1/m)\n",
+ " return (-c * y, c * x, 0.1)\n",
+ "\n",
+ "\n",
+ "# Defining the geometry of the material as a circular disk\n",
+ "def Ms_func(point):\n",
+ " x, y, _ = point\n",
+ " if x**2 + y**2 <= r**2:\n",
+ " return Ms\n",
+ " else:\n",
+ " return 0\n",
+ "\n",
+ "\n",
+ "# Sample's centre is placed at origin\n",
+ "region = df.Region(p1=(-r, -r, -thickness / 2), p2=(r, r, thickness / 2))\n",
+ "mesh = df.Mesh(\n",
+ " region=region, cell=(5e-9, 5e-9, 10e-9)\n",
+ ") # default cell = (5e-9, 5e-9, 10e-9)\n",
+ "\n",
+ "system.m = df.Field(mesh, nvdim=3, value=m_init, norm=Ms_func, valid=\"norm\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25e93571",
+ "metadata": {},
+ "source": [
+ "We run our `TimeDriver` simulation below for **5** ns and save our magnetisation in **50** steps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ef868e42",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4137a904939141c590292a4bde1e808b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Running OOMMF (ExeOOMMFRunner): 0%| | 0/100 files written [00:00]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running OOMMF (ExeOOMMFRunner)[2025/04/05 21:08] took 33.0 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "H = (3.4e4, 0, 0) # external magnetic field A/m\n",
+ "\n",
+ "td = oc.TimeDriver()\n",
+ "td.drive(system, t=5e-9, n=50, verbose=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf7b038a",
+ "metadata": {},
+ "source": [
+ "Our example drive to be used for analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "84d6af40",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "drive = mdata.Data(system.name)[-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0d705ca",
+ "metadata": {},
+ "source": [
+ "## Functions\n",
+ "\n",
+ "Below we have functions for four pyvista plotting functions. These are:\n",
+ "\n",
+ "1. `drive.pyvista.vector()`\n",
+ "2. `drive.pyvista.volume()`\n",
+ "3. `drive.pyvista.contour()`\n",
+ "4. `drive.pyvista.streamlines()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "407a9796",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def drive_pv_vector(drive):\n",
+ " \"\"\"\n",
+ " pyvista vector plot.\n",
+ "\n",
+ " This function visualises a vector field. We use a panel slider change the time step\n",
+ " and a panel number indicator to label the corresponding index for the time step we are on.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " drive:\n",
+ "\n",
+ " Drive object that is used for analysis\n",
+ " \"\"\"\n",
+ " # Create a PyVista plotter\n",
+ " plot = pv.Plotter()\n",
+ "\n",
+ " def time_pv(value):\n",
+ " drive[value].pyvista.vector(name=\"x\", plotter=plot)\n",
+ " time_step.value = (drive.info[\"t\"] / drive.n) * (value + 1)\n",
+ "\n",
+ " # Create a Panel slider\n",
+ " slider = pn.widgets.IntSlider(\n",
+ " name=\"Index\", value=0, start=0, end=drive.n - 1, step=1\n",
+ " )\n",
+ "\n",
+ " # Create a Panel FloatInput for the time step\n",
+ " time_step = pn.indicators.Number(\n",
+ " name=\"t:\",\n",
+ " format=\"{value:.2g}\",\n",
+ " title_size=\"10pt\",\n",
+ " font_size=\"10pt\",\n",
+ " default_color=\"white\",\n",
+ " )\n",
+ "\n",
+ " # Bind the slider to update function\n",
+ " slider.param.watch(\n",
+ " lambda event: time_pv(event.new), \"value\"\n",
+ " ) # callback function where event.new is the newest slider value\n",
+ "\n",
+ " # Initialize with first value\n",
+ " time_pv(slider.value)\n",
+ "\n",
+ " # Panel layout\n",
+ " layout = pn.Row(\n",
+ " slider,\n",
+ " time_step,\n",
+ " styles={\n",
+ " \"background\": \"black\",\n",
+ " \"padding\": \"10px\",\n",
+ " \"border-radius\": \"10px\",\n",
+ " \"border\": \"1px solid white\",\n",
+ " },\n",
+ " )\n",
+ "\n",
+ " display(layout)\n",
+ " plot.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da3add81",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def drive_pv_volume(drive):\n",
+ " \"\"\"\n",
+ " pyvista volume plot.\n",
+ "\n",
+ " This function visualises the scalar field within a three-dimensional region by rendering a volume.\n",
+ " We use a panel slider change the time step and a panel number indicator to label the corresponding\n",
+ " index for the time step we are on.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " drive:\n",
+ "\n",
+ " Drive object that is used for analysis\n",
+ " \"\"\"\n",
+ " # Create a PyVista plotter\n",
+ " plot = pv.Plotter()\n",
+ "\n",
+ " def time_pv(value):\n",
+ " drive[value].pyvista.volume(name=\"x\", plotter=plot)\n",
+ " time_step.value = (drive.info[\"t\"] / drive.n) * (value + 1)\n",
+ "\n",
+ " # Create a Panel slider\n",
+ " slider = pn.widgets.IntSlider(\n",
+ " name=\"Index\", value=0, start=0, end=drive.n - 1, step=1\n",
+ " )\n",
+ "\n",
+ " # Create a Panel FloatInput for the time step\n",
+ " time_step = pn.indicators.Number(\n",
+ " name=\"t:\",\n",
+ " format=\"{value:.2g}\",\n",
+ " title_size=\"10pt\",\n",
+ " font_size=\"10pt\",\n",
+ " default_color=\"white\",\n",
+ " )\n",
+ "\n",
+ " # Bind the slider to update function\n",
+ " slider.param.watch(\n",
+ " lambda event: time_pv(event.new), \"value\"\n",
+ " ) # callback function where event.new is the newest slider value\n",
+ "\n",
+ " # Initialize with first value\n",
+ " time_pv(slider.value)\n",
+ "\n",
+ " # Panel layout\n",
+ " layout = pn.Row(\n",
+ " slider,\n",
+ " time_step,\n",
+ " styles={\n",
+ " \"background\": \"black\",\n",
+ " \"padding\": \"10px\",\n",
+ " \"border-radius\": \"10px\",\n",
+ " \"border\": \"1px solid white\",\n",
+ " },\n",
+ " )\n",
+ "\n",
+ " display(layout)\n",
+ " plot.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7a9a4c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def drive_pv_contour(drive, isosurfaces=10):\n",
+ " \"\"\"\n",
+ " pyvista contour plot.\n",
+ "\n",
+ " This function computes isosurfaces of the field. We use a panel slider\n",
+ " change the time step and a panel number indicator to label the corresponding\n",
+ " index for the time step we are on.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " drive:\n",
+ "\n",
+ " Drive object that is used for analysis\n",
+ "\n",
+ " isosurfaces:\n",
+ "\n",
+ " Number of isosurfaces. Defaults to 10\n",
+ " \"\"\"\n",
+ " # Create a PyVista plotter\n",
+ " plot = pv.Plotter()\n",
+ "\n",
+ " def time_pv(value):\n",
+ " drive[value].pyvista.contour(name=\"x\", plotter=plot, isosurfaces=isosurfaces)\n",
+ " time_step.value = (drive.info[\"t\"] / drive.n) * (value + 1)\n",
+ "\n",
+ " # Create a Panel slider\n",
+ " slider = pn.widgets.IntSlider(\n",
+ " name=\"Index\", value=0, start=0, end=drive.n - 1, step=1\n",
+ " )\n",
+ "\n",
+ " # Create a Panel FloatInput for the time step\n",
+ " time_step = pn.indicators.Number(\n",
+ " name=\"t:\",\n",
+ " format=\"{value:.2g}\",\n",
+ " title_size=\"10pt\",\n",
+ " font_size=\"10pt\",\n",
+ " default_color=\"white\",\n",
+ " )\n",
+ "\n",
+ " # Bind the slider to update function\n",
+ " slider.param.watch(\n",
+ " lambda event: time_pv(event.new), \"value\"\n",
+ " ) # callback function where event.new is the newest slider value\n",
+ "\n",
+ " # Initialize with first value\n",
+ " time_pv(slider.value)\n",
+ "\n",
+ " # Panel layout\n",
+ " layout = pn.Row(\n",
+ " slider,\n",
+ " time_step,\n",
+ " styles={\n",
+ " \"background\": \"black\",\n",
+ " \"padding\": \"10px\",\n",
+ " \"border-radius\": \"10px\",\n",
+ " \"border\": \"1px solid white\",\n",
+ " },\n",
+ " )\n",
+ "\n",
+ " display(layout)\n",
+ " plot.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62b40d4f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def drive_pv_streamlines(drive):\n",
+ " \"\"\"\n",
+ " pyvista streamline plot.\n",
+ "\n",
+ " This function generates a plot of streamines. We use a\n",
+ " panel slider change the time step and a panel number\n",
+ " indicator to label the correspondingindex for the time\n",
+ " step we are on.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " drive:\n",
+ "\n",
+ " Drive object that is used for analysis\n",
+ " \"\"\"\n",
+ " # Create a PyVista plotter\n",
+ " plot = pv.Plotter()\n",
+ "\n",
+ " def time_pv(value):\n",
+ " drive[value].pyvista.streamlines(name=\"x\", plotter=plot)\n",
+ " time_step.value = (drive.info[\"t\"] / drive.n) * (value + 1)\n",
+ "\n",
+ " # Create a Panel slider\n",
+ " slider = pn.widgets.IntSlider(\n",
+ " name=\"Index\", value=0, start=0, end=drive.n - 1, step=1\n",
+ " )\n",
+ "\n",
+ " # Create a Panel FloatInput for the time step\n",
+ " time_step = pn.indicators.Number(\n",
+ " name=\"t:\",\n",
+ " format=\"{value:.2g}\",\n",
+ " title_size=\"10pt\",\n",
+ " font_size=\"10pt\",\n",
+ " default_color=\"white\",\n",
+ " )\n",
+ "\n",
+ " # Bind the slider to update function\n",
+ " slider.param.watch(\n",
+ " lambda event: time_pv(event.new), \"value\"\n",
+ " ) # callback function where event.new is the newest slider value\n",
+ "\n",
+ " # Initialize with first value\n",
+ " time_pv(slider.value)\n",
+ "\n",
+ " # Panel layout\n",
+ " layout = pn.Row(\n",
+ " slider,\n",
+ " time_step,\n",
+ " styles={\n",
+ " \"background\": \"black\",\n",
+ " \"padding\": \"10px\",\n",
+ " \"border-radius\": \"10px\",\n",
+ " \"border\": \"1px solid white\",\n",
+ " },\n",
+ " )\n",
+ "\n",
+ " display(layout)\n",
+ " plot.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07c56ab6",
+ "metadata": {},
+ "source": [
+ "## Code examples\n",
+ "\n",
+ "In the cells below we have an example of our `drive_pv_vector()` function and the holoviews equivalent, `drive.hv()`, to compare it with."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6bf2d1be",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "drive_pv_vector(drive)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0060daf6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "drive.hv(kdims=[\"x\", \"y\"])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Watchdog_live_view_demo.ipynb b/examples/Watchdog_live_view_demo.ipynb
new file mode 100644
index 0000000..01fb60b
--- /dev/null
+++ b/examples/Watchdog_live_view_demo.ipynb
@@ -0,0 +1,489 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Holoviews live demo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook is a demo on how we can concurrently visualise and run OC Time Driver simulations. \n",
+ "\n",
+ "This function is designed to work in parallel with a second notebook open. This second notebook should have an ubermag system initialised and it is where the Time Driver simulation is run. \n",
+ "\n",
+ "The system this function was built and tested on was a similar system to the Permalloy disk sample defined in the Ubermag demo. Please refer to the Ubermag demo for detail on how to initialise the system."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Module Info"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To concurrently visualise and run a Time Driver simulation we use common python modules but for the specialised tasks of monitoring directory events and handling a data pipeline we use the Watchdog and holoviews.streams.Pipe .\n",
+ "\n",
+ "\n",
+ "With Watchdog we can assign a directory to monitor. This directory will be where we have our simulation files produced and stored. Once we have assigned a directory to monitor we can create a visualisation pipeline with holoviews.streams.Pipe.\n",
+ "\n",
+ "\n",
+ "With holoviews.streams.Pipe we can pass formatted data from .omf files through a holoviews pipe where they are displayed onto an updating holoviews DynamicMap."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simulation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Firstly, we import the necessary modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "application/vnd.holoviews_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import discretisedfield as df\n",
+ "import xarray as xr\n",
+ "import holoviews as hv\n",
+ "import numpy as np\n",
+ "import time\n",
+ "import os\n",
+ "import panel as pn\n",
+ "import json\n",
+ "\n",
+ "from holoviews.streams import Pipe\n",
+ "from watchdog.observers import Observer\n",
+ "from watchdog.events import FileSystemEventHandler\n",
+ "\n",
+ "hv.extension(\"bokeh\", logo=False)\n",
+ "pn.extension()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is the class that contains the functionality for the live visualisation. We overwrite methods from the watchdog abstract class FileSystemEventHander."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class DirectoryMonitorHandler(FileSystemEventHandler):\n",
+ " def __init__(self, observer, stop_flag):\n",
+ " self.observer = observer\n",
+ " self.file_count = 0\n",
+ " self.t = 0 # init with value = 0\n",
+ " self.n = (\n",
+ " 1 # init with value = 1 to prevent division by 0 in process_file method\n",
+ " )\n",
+ " self.stop_flag = stop_flag\n",
+ " self.monitored_directories = self.get_existing_subdirectories()\n",
+ " self.pipe1 = Pipe(data=[])\n",
+ " self.pipe2 = Pipe(data=[])\n",
+ " self.vector_dmap = hv.DynamicMap(hv.VectorField, streams=[self.pipe1]).opts(\n",
+ " magnitude=\"mag\", aspect=1\n",
+ " )\n",
+ " self.Image = hv.DynamicMap(hv.Image, streams=[self.pipe2]).opts(\n",
+ " cmap=\"coolwarm\", colorbar=True\n",
+ " )\n",
+ " self.plot = self.Image * self.vector_dmap\n",
+ " self.widget()\n",
+ "\n",
+ " def get_existing_subdirectories(self):\n",
+ " cwd = os.getcwd()\n",
+ " list_dir = os.listdir(cwd)\n",
+ " monitored_directories = {\n",
+ " os.path.join(cwd, i)\n",
+ " for i in list_dir\n",
+ " if os.path.isdir(os.path.join(cwd, i))\n",
+ " }\n",
+ " return monitored_directories\n",
+ "\n",
+ " def on_created(self, event):\n",
+ " if event.is_directory:\n",
+ " print(f\"New directory created: {event.src_path}\")\n",
+ " self.monitor_new_directory(event.src_path)\n",
+ " else:\n",
+ " print(f\"New file detected: {event.src_path}\")\n",
+ " self.process_file(event.src_path)\n",
+ "\n",
+ " def monitor_new_directory(self, directory_path):\n",
+ " if directory_path not in self.monitored_directories:\n",
+ " self.monitored_directories.add(directory_path)\n",
+ " print(f\"Now monitoring: {directory_path}\")\n",
+ "\n",
+ " def process_file(self, file_path):\n",
+ " # Only process files with specific extensions\n",
+ " if file_path.endswith(r\".omf\"):\n",
+ " field = df.Field.from_file(rf\"{file_path}\")\n",
+ " field_xr = field.to_xarray()\n",
+ " # x_array for each vdim component\n",
+ " xxa = field_xr.sel(vdims=\"x\") # arrow_x at each coordinate\n",
+ " yxa = field_xr.sel(vdims=\"y\") # arrow_y\n",
+ " zxa = field_xr.sel(vdims=\"z\") # arrow_z\n",
+ "\n",
+ " angle = np.arctan2(yxa, xxa) # angle of vector line\n",
+ " mag = np.sqrt(xxa**2 + yxa**2) # magnitude of vector\n",
+ "\n",
+ " table = xr.Dataset(\n",
+ " dict(angle=angle[:, :, 0], mag=mag[:, :, 0], W=zxa[:, :, 0])\n",
+ " ) # xarray of processed data needed for hv visualisation the first z-splice is used\n",
+ "\n",
+ " self.pipe1.send(table) # pipe for vectorfield\n",
+ " self.pipe2.send(table[\"W\"]) # pipe for scalar field\n",
+ "\n",
+ " self.file_count += 1\n",
+ " self.check_observer()\n",
+ "\n",
+ " self.time_step.value = (self.t / self.n) * (self.file_count - 1)\n",
+ "\n",
+ " if file_path.endswith(r\"m0.omf\"):\n",
+ " layout = pn.Row(self.plot, self.time_step)\n",
+ " # display one updating dmap that initially gets created with m0 file\n",
+ " display(layout)\n",
+ "\n",
+ " if file_path.endswith(r\".json\"):\n",
+ " # read info.json to have the simulation time and number of files\n",
+ " with open(file_path, \"r\") as info:\n",
+ " self.data = json.load(info)\n",
+ " self.t = self.data[\"t\"]\n",
+ " self.n = self.data[\"n\"]\n",
+ "\n",
+ " def widget(self):\n",
+ " time_step = pn.indicators.Number(\n",
+ " name=\"t:\",\n",
+ " format=\"{value:.2g}\",\n",
+ " title_size=\"10pt\",\n",
+ " font_size=\"10pt\",\n",
+ " default_color=\"white\",\n",
+ " )\n",
+ " self.time_step = time_step\n",
+ "\n",
+ " def check_observer(self):\n",
+ " if self.file_count >= self.n + 1:\n",
+ " print(\"All files produced, stopping monitoring...\")\n",
+ " self.observer.stop()\n",
+ " self.stop_flag[0] = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function below initialises the Watchdog Observer to monitor the desired directory. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def main(working_directory=os.getcwd()):\n",
+ " \"\"\"\n",
+ " Function to plot hv plots whilst simultaneously running a Time Driver simulation\n",
+ "\n",
+ " Parameters:\n",
+ " working_directory: The directory we want Watchdog to monitor. Defaulted to the current working directory\n",
+ " \"\"\"\n",
+ "\n",
+ " # stop flag used to terminate thread when all files are produced.\n",
+ " stop_flag = [False]\n",
+ "\n",
+ " # Set up the observer\n",
+ " observer = Observer()\n",
+ " handler = DirectoryMonitorHandler(observer, stop_flag)\n",
+ "\n",
+ " # Schedule the observer to monitor the working directory\n",
+ " observer.schedule(handler, working_directory, recursive=True)\n",
+ " print(f\"Monitoring: {working_directory}\")\n",
+ "\n",
+ " # Start the observer\n",
+ " observer.start()\n",
+ "\n",
+ " try:\n",
+ " while not stop_flag[0]:\n",
+ " time.sleep(5) # Keeps the script running\n",
+ " except KeyboardInterrupt:\n",
+ " print(\"Stopping monitoring...\")\n",
+ " finally:\n",
+ " observer.stop()\n",
+ "\n",
+ " observer.join()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the cell below first to begin directory monitoring and then run the Time Driver simulation in your other notebook.\n",
+ "\n",
+ "\n",
+ "**Remember** to specify path of the directory you want monitored if your simulation files aren't being produced in the current working directory."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Monitoring: C:\\Users\\Noahd\\IP code\n",
+ "New directory created: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\n",
+ "Now monitoring: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\m0.omf\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8437b4eb022c41d39e9370ca406c299b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "BokehModel(combine_events=True, render_bundle={'docs_json': {'395b53fb-3f0c-4419-a8ef-eb0a553b443d': {'version…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1.mif\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\info.json\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test-noahs-laptop-18416\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1.restart-noahs-laptop-18416-oxs-checkpoint.tmp\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test-noahs-laptop-18416\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1.restart-noahs-laptop-18416-oxs-checkpoint.backup\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1.odt\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-00-0003274.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-01-0006548.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-02-0009822.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-03-0013096.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-04-0016370.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-05-0019644.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-06-0022918.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-07-0026192.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-08-0029466.omf\n",
+ "New file detected: C:\\Users\\Noahd\\IP code\\test_1\\drive-104\\test_1-Oxs_TimeDriver-Magnetization-09-0032740.omf\n",
+ "All files produced, stopping monitoring...\n"
+ ]
+ }
+ ],
+ "source": [
+ "main()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}