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_sources/notebooks/supplementary_examples/00_nearfield_ptychography.ipynb
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_sources/notebooks/supplementary_examples/01_object_regularisation.ipynb
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_sources/notebooks/supplementary_examples/02_live_visualisation.ipynb
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| {"cells": [{"cell_type": "markdown", "id": "6044e4fe-966e-4008-9545-c33b9727f10d", "metadata": {}, "source": ["# Live visualisation\n", "**Learning how to use the ```PlotClient``` to look at the reconstruction while it is still running.**\n", "\n", "There many use cases where it can be useful to communicate between a remote process running the reconstruction and a local client process that wants to receive live information about the status of the reconstruction. In PtyPy, this is possible via ZMQ socket communication."]}, {"cell_type": "markdown", "id": "196b8869-ac86-48c3-a867-ca92585f9f48", "metadata": {}, "source": ["## Running the reconstruction in Python script\n", "\n", "First, we copy a YAML config file [./config/dls_i08_nanogold_spiral.yaml](./config/dls_i08_nanogold_spiral.yaml) from one of the previous examples making sure that we turn on the interaction server ```p.io.interaction.server``` \n", "\n", "```python\n", "p.io = u.Param()\n", "p.io.home = \"./\"\n", "p.io.rfile = \"recons/%(run)s_%(engine)s_%(iterations)04d.ptyr\"\n", "p.io.autosave = u.Param(active=False)\n", "p.io.autoplot = u.Param(active=False)\n", "p.io.interaction = u.Param(active=True)\n", "p.io.interaction.server = u.Param()\n", "p.io.interaction.server.port = 5580\n", "p.io.interaction.server.address = \"tcp://127.0.0.1\"\n", "```\n", "\n", "sending updates on localhost (127.0.0.1) at port 9001. We then create a Python run script reading from this config file\n", "\n", "```python\n", "with open(\"./ptypy_run_dls_i08_nanogold.py\", \"w\") as f:\n", " f.write(\"\"\"\\\n", "import ptypy\n", "ptypy.load_ptyscan_module(\"hdf5_loader\")\n", "ptypy.load_gpu_engines(\"cupy\") \n", "p = ptypy.utils.param_from_yaml(\"config/dls_i08_nanogold_spiral.yaml\")\n", "P = ptypy.core.Ptycho(p,level=5)\n", "\"\"\")\n", "```\n", "\n", "and execute the run script in a terminal\n", "\n", "```bash\n", "srun -n 4 -c 2 --gpus-per-task=1 --gpu-bin=None python ptypy_run_dls_i08_nanogold.py\n", "\n", "```"]}, {"cell_type": "markdown", "id": "169dd282-8217-4f22-a0c9-1c374e7fd3b8", "metadata": {}, "source": ["---"]}, {"cell_type": "code", "execution_count": null, "id": "b81d242c-deda-4b5d-a577-b55b42e46ac9", "metadata": {}, "outputs": [], "source": ["with open(\"./ptypy_run_dls_i08_nanogold.py\", \"w\") as f:\n", " f.write(\"\"\"\\\n", "import ptypy\n", "ptypy.load_ptyscan_module(\"hdf5_loader\")\n", "ptypy.load_gpu_engines(\"cupy\") \n", "p = ptypy.utils.param_from_yaml(\"config/dls_i08_nanogold_spiral.yaml\")\n", "P = ptypy.core.Ptycho(p,level=5)\n", "\"\"\")"]}, {"cell_type": "markdown", "id": "6a288a19-ea65-41f6-9bb3-0337a00dde6a", "metadata": {}, "source": ["## Running the plot client in this notebook\n", "\n", "While the processing script is running in a separate process, we just need to connect our client to the same address and port\n", "\n", "```python\n", "import ptypy\n", "\n", "client = ptypy.utils.Param()\n", "client.port = 5580\n", "client.address = \"tcp://127.0.0.1\"\n", "```\n", "\n", "and start an instance of the ```PlotClient```\n", "\n", "```python\n", "pc = ptypy.utils.PlotClient(client)\n", "pc.start()\n", "```\n", "\n", "and use its ```get_data()``` function to get updates\n", "\n", "```python\n", "plotter.pr, plotter.ob, runtime = pc.get_data()\n", "```\n", "\n", "on probe, object and runtime information. We can also use the ```MPLplotter``` create \n", "a plot that is updated by repeatedly executing a cell with this code\n", "\n", "```python\n", "from IPython import display\n", "plotter = ptypy.utils.MPLplotter()\n", "initialised = False\n", "if (pc.status == pc.DATA):\n", " plotter.pr, plotter.ob, runtime = pc.get_data()\n", " plotter.runtime.update(runtime)\n", " if not initialised:\n", " plotter.update_plot_layout()\n", " plotter.plot_all()\n", " plotter.draw()\n", " display.clear_output(wait=True)\n", " display.display(plotter.plot_fig)\n", "```"]}, {"cell_type": "markdown", "id": "deaa9e0c-8af9-4af8-b318-5f29f3c289a4", "metadata": {}, "source": ["---"]}, {"cell_type": "code", "execution_count": null, "id": "ca168d91-e5b8-4d41-8b5d-1c37035f906a", "metadata": {}, "outputs": [], "source": ["import ptypy\n", "\n", "# Client parameters\n", "client = ptypy.utils.Param()\n", "client.port = 5580\n", "client.address = \"tcp://127.0.0.1\"\n", "\n", "# Create instance of plotclient\n", "pc = ptypy.utils.PlotClient(client)\n", "\n", "# start listening\n", "pc.start()"]}, {"cell_type": "code", "execution_count": null, "id": "06fe8608-449e-4b66-a512-0f339ea7afe8", "metadata": {}, "outputs": [], "source": ["# Refresh this cell multiple time \n", "# to see updates on the reconstruction\n", "# while the script is running in a separate process\n", "layout = ptypy.utils.plot_client.TEMPLATES[\"jupyter\"]\n", "plotter = ptypy.utils.MPLplotter(pars=layout)\n", "initialised = False\n", "if (pc.status == pc.DATA):\n", " plotter.pr, plotter.ob, runtime = pc.get_data()\n", " plotter.runtime.update(runtime)\n", " if not initialised:\n", " plotter.update_plot_layout()\n", " plotter.plot_all()\n", " plotter.draw()\n", " plotter.plot_fig"]}], "metadata": {"kernelspec": {"display_name": "PtyPy", "language": "python", "name": "ptypy_pycuda"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15"}}, "nbformat": 4, "nbformat_minor": 5} | ||
| {"cells": [{"cell_type": "markdown", "id": "6044e4fe-966e-4008-9545-c33b9727f10d", "metadata": {}, "source": ["# Live visualisation\n", "**Learning how to use the ```PlotClient``` to look at the reconstruction while it is still running.**\n", "\n", "There many use cases where it can be useful to communicate between a remote process running the reconstruction and a local client process that wants to receive live information about the status of the reconstruction. In PtyPy, this is possible via ZMQ socket communication."]}, {"cell_type": "markdown", "id": "196b8869-ac86-48c3-a867-ca92585f9f48", "metadata": {}, "source": ["## Running the reconstruction in Python script\n", "\n", "First, we copy a YAML config file [./config/dls_i08_nanogold_spiral.yaml](./config/dls_i08_nanogold_spiral.yaml) from one of the previous examples making sure that we turn on the interaction server ```p.io.interaction.server``` \n", "\n", "```python\n", "p.io = u.Param()\n", "p.io.home = \"./\"\n", "p.io.rfile = \"recons/%(run)s_%(engine)s_%(iterations)04d.ptyr\"\n", "p.io.autosave = u.Param(active=False)\n", "p.io.autoplot = u.Param(active=False)\n", "p.io.interaction = u.Param(active=True)\n", "p.io.interaction.server = u.Param()\n", "p.io.interaction.server.port = 5580\n", "p.io.interaction.server.address = \"tcp://127.0.0.1\"\n", "```\n", "\n", "sending updates on localhost (127.0.0.1) at port 9001. We then create a Python run script reading from this config file\n", "\n", "```python\n", "with open(\"./ptypy_run_dls_i08_nanogold.py\", \"w\") as f:\n", " f.write(\"\"\"\\\n", "import ptypy\n", "ptypy.load_ptyscan_module(\"hdf5_loader\")\n", "ptypy.load_gpu_engines(\"cupy\") \n", "p = ptypy.utils.param_from_yaml(\"config/dls_i08_nanogold_spiral.yaml\")\n", "P = ptypy.core.Ptycho(p,level=5)\n", "\"\"\")\n", "```\n", "\n", "and execute the run script in a terminal\n", "\n", "```bash\n", "srun -n 4 -c 2 --gpus-per-task=1 --gpu-bin=None python ptypy_run_dls_i08_nanogold.py\n", "\n", "```"]}, {"cell_type": "markdown", "id": "169dd282-8217-4f22-a0c9-1c374e7fd3b8", "metadata": {}, "source": ["---"]}, {"cell_type": "code", "execution_count": null, "id": "b81d242c-deda-4b5d-a577-b55b42e46ac9", "metadata": {"tags": []}, "outputs": [], "source": ["with open(\"./ptypy_run_dls_i08_nanogold.py\", \"w\") as f:\n", " f.write(\"\"\"\\\n", "import ptypy\n", "ptypy.load_ptyscan_module(\"hdf5_loader\")\n", "ptypy.load_gpu_engines(\"cupy\") \n", "p = ptypy.utils.param_from_yaml(\"config/dls_i08_nanogold_spiral.yaml\")\n", "P = ptypy.core.Ptycho(p,level=5)\n", "\"\"\")"]}, {"cell_type": "raw", "id": "75d00b06-5dc5-46da-93a8-5b160285845b", "metadata": {"tags": []}, "source": ["# commands to copy into terminal\n", "cd $HOME/tutorials/notebooks/supplementary_examples\n", "srun -n 4 -c 2 --gpus-per-task=1 --gpu-bin=None python ptypy_run_dls_i08_nanogold.py"]}, {"cell_type": "markdown", "id": "6a288a19-ea65-41f6-9bb3-0337a00dde6a", "metadata": {}, "source": ["## Running the plot client in this notebook\n", "\n", "While the processing script is running in a separate process, we just need to connect our client to the same address and port\n", "\n", "```python\n", "import ptypy\n", "\n", "client = ptypy.utils.Param()\n", "client.port = 5580\n", "client.address = \"tcp://127.0.0.1\"\n", "```\n", "\n", "and start an instance of the ```PlotClient```\n", "\n", "```python\n", "pc = ptypy.utils.PlotClient(client)\n", "pc.start()\n", "```\n", "\n", "and use its ```get_data()``` function to get updates\n", "\n", "```python\n", "plotter.pr, plotter.ob, runtime = pc.get_data()\n", "```\n", "\n", "on probe, object and runtime information. We can also use the ```MPLplotter``` create \n", "a plot that is updated by repeatedly executing a cell with this code\n", "\n", "```python\n", "from IPython import display\n", "plotter = ptypy.utils.MPLplotter()\n", "initialised = False\n", "if (pc.status == pc.DATA):\n", " plotter.pr, plotter.ob, runtime = pc.get_data()\n", " plotter.runtime.update(runtime)\n", " if not initialised:\n", " plotter.update_plot_layout()\n", " plotter.plot_all()\n", " plotter.draw()\n", " display.clear_output(wait=True)\n", " display.display(plotter.plot_fig)\n", "```"]}, {"cell_type": "markdown", "id": "deaa9e0c-8af9-4af8-b318-5f29f3c289a4", "metadata": {}, "source": ["---"]}, {"cell_type": "code", "execution_count": null, "id": "ca168d91-e5b8-4d41-8b5d-1c37035f906a", "metadata": {"tags": []}, "outputs": [], "source": ["import ptypy\n", "\n", "# Client parameters\n", "client = ptypy.utils.Param()\n", "client.port = 5580\n", "client.address = \"tcp://127.0.0.1\"\n", "\n", "# Create instance of plotclient\n", "pc = ptypy.utils.PlotClient(client)\n", "\n", "# start listening\n", "pc.start()"]}, {"cell_type": "code", "execution_count": null, "id": "06fe8608-449e-4b66-a512-0f339ea7afe8", "metadata": {"tags": []}, "outputs": [], "source": ["# Refresh this cell multiple time \n", "# to see updates on the reconstruction\n", "# while the script is running in a separate process\n", "layout = ptypy.utils.plot_client.TEMPLATES[\"jupyter\"]\n", "plotter = ptypy.utils.MPLplotter(pars=layout)\n", "initialised = False\n", "if (pc.status == pc.DATA):\n", " plotter.pr, plotter.ob, runtime = pc.get_data()\n", " plotter.runtime.update(runtime)\n", " if not initialised:\n", " plotter.update_plot_layout()\n", " plotter.plot_all()\n", " plotter.draw()\n", " plotter.plot_fig"]}], "metadata": {"kernelspec": {"display_name": "PtyPy (cupy)", "language": "python", "name": "ptypy_cupy"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8"}}, "nbformat": 4, "nbformat_minor": 5} |
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