-
Notifications
You must be signed in to change notification settings - Fork 5
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Co-authored-by: natl <[email protected]>
- Loading branch information
Showing
9 changed files
with
494 additions
and
267 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
222 changes: 222 additions & 0 deletions
222
docs/source/examples/generating-structures-for-geant.ipynb
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,222 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Making large scale structures in Geant4\n", | ||
| "\n", | ||
| "Geant4 simulations use a large scale structure " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import sys\n", | ||
| "from pathlib import Path\n", | ||
| "\n", | ||
| "try:\n", | ||
| " # The voxelisation library produces the cubic voxelisation that\n", | ||
| " # can be used to build DNA\n", | ||
| " from fractaldna.structure_models import voxelisation as v\n", | ||
| "\n", | ||
| " # The hilbert module produces and handles L-Strings\n", | ||
| " from fractaldna.structure_models import hilbert as h\n", | ||
| "except (ImportError, ModuleNotFoundError):\n", | ||
| " sys.path.append(str(Path.cwd().parent.parent.parent))\n", | ||
| " from fractaldna.structure_models import voxelisation as v\n", | ||
| " from fractaldna.structure_models import hilbert as h\n", | ||
| "\n", | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "from mpl_toolkits.mplot3d import Axes3D" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Fractal Cell Shapes\n", | ||
| "\n", | ||
| "### Generating a square shape" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Start with the initial L-String X for a Hilbert Curve\n", | ||
| "initial_string = \"X\"\n", | ||
| "# Iterate it as required (here, nn=3)\n", | ||
| "# for nn = 8, this takes about two hours on my 16GB RAM Mac\n", | ||
| "nn = 3\n", | ||
| "iterated_lstring = h.iterate_lstring(initial_string)\n", | ||
| "for _ in range(nn - 1):\n", | ||
| " iterated_lstring = h.iterate_lstring(iterated_lstring)\n", | ||
| "\n", | ||
| "vf = v.VoxelisedFractal.fromLString(iterated_lstring, pbar=True)\n", | ||
| "vf.center_fractal()\n", | ||
| "# fig = vf.to_plot()\n", | ||
| "# fig.savefig('results/fractal-X-3-centred.svg')\n", | ||
| "\n", | ||
| "# If you are saving a BIG fractal, try using the to_text() method instead\n", | ||
| "# as large dataframes are very slow to generate beyond 6 iterations.\n", | ||
| "# (Very slow as in multiple hours slow!)\n", | ||
| "# with open(f'results/fractal-X-{nn}-centred.csv', 'w') as ff:\n", | ||
| "# ff.write(vf.to_text())\n", | ||
| "vf.to_frame().to_csv(f\"results/fractal-X-{nn}-centred.csv\", index=False, sep=\" \")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<img src='results/fractal-X-3-centred.svg'>" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Generating a Rectangular Shape\n", | ||
| "The seed `XFXFX` will generate a rectangular shape with aspect ratio 1:1:3" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Start with the initial L-String XFXFX for a Hilbert Curve\n", | ||
| "initial_string = \"XFXFX\"\n", | ||
| "# Iterate it as required (here, nn=4)\n", | ||
| "nn = 4\n", | ||
| "iterated_lstring = h.iterate_lstring(initial_string)\n", | ||
| "for _ in range(nn - 1):\n", | ||
| " iterated_lstring = h.iterate_lstring(iterated_lstring)\n", | ||
| "\n", | ||
| "vf = v.VoxelisedFractal.fromLString(iterated_lstring, pbar=True)\n", | ||
| "vf.center_fractal()\n", | ||
| "# fig = vf.to_plot()\n", | ||
| "# fig.savefig(f'results/fractal-XFXFX-{nn}-centred.svg')\n", | ||
| "\n", | ||
| "vf.to_frame().to_csv(f\"results/fractal-XFXFX-{nn}-centred.csv\", index=False, sep=\" \")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "*Note that the x-axis is compressed relative to the others in the below image*\n", | ||
| "\n", | ||
| "<img src='results/fractal-XFXFX-2-centred.svg'>" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Importing a shape from a path\n", | ||
| "\n", | ||
| "The `voxelisation` model can convert the path of this curve to a voxelised representation, of straight\n", | ||
| "and curved boxes.\n", | ||
| "\n", | ||
| "In this example we perform this on a text file with X/Y/Z columns:\n", | ||
| "```\n", | ||
| "X\tY\tZ\n", | ||
| "-22\t-106\t216\n", | ||
| "-22\t-107\t216\n", | ||
| "-22\t-107\t215\n", | ||
| "-22\t-108\t215\n", | ||
| "-22\t-108\t214\n", | ||
| "-23\t-108\t214\n", | ||
| "-23\t-109\t214\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%capture\n", | ||
| "df = pd.read_csv(\"results/example-path.csv\", sep=\"\\t\")\n", | ||
| "fig = plt.figure()\n", | ||
| "ax = fig.add_subplot(111, projection=\"3d\")\n", | ||
| "ax.plot(df.X, df.Y, df.Z)\n", | ||
| "fig.savefig(\"example-path.svg\")\n", | ||
| "\n", | ||
| "vf = v.VoxelisedFractal.from_path(df.values)\n", | ||
| "fig_fractal = vf.to_plot()\n", | ||
| "fig_fractal.savefig(\"example-path-voxels.svg\")\n", | ||
| "\n", | ||
| "vf.to_frame().to_csv(\"results/example-path-voxels.csv\", sep=\" \")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Left: Source Plot, Right:Voxelised Plot\n", | ||
| "\n", | ||
| "<img src='results/example-path.svg' width='45%'> <img src='results/example-path-voxels.svg' width='45%'>\n", | ||
| "\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Generating Random Volumes\n", | ||
| "\n", | ||
| "It can be useful to generate randomised volumes for testing a simulation. \n", | ||
| "This was the subject of (this article)[https://doi.org/10.1016/j.ejmp.2018.02.011].\n", | ||
| "\n", | ||
| "To generate a randomised volume, the `fractaldna.structure_models.random_placements`\n", | ||
| "is available.\n", | ||
| "\n", | ||
| "In that paper, 200,000 non overlapping prisms were simulated in a r=3000nm \n", | ||
| "ball.\n", | ||
| "The prisms had dimensions 30x30x100nm" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from fractaldna.structure_models import random_placements as rp\n", | ||
| "\n", | ||
| "# Generating 200,000 prisms can take around 4-5 hours\n", | ||
| "prisms = rp.generate_non_overlapping_prisms(\n", | ||
| " n_prisms=200_000,\n", | ||
| " size=[30, 30, 100], # nanometres\n", | ||
| " rad=3000, # nanometres\n", | ||
| " early_exit=-1,\n", | ||
| " verbose=True,\n", | ||
| ")\n", | ||
| "\n", | ||
| "df = prisms.to_frame()\n", | ||
| "df.to_csv(\"results/prisms_200k_r3000.csv\", sep=\" \", index=False)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "language_info": { | ||
| "name": "python", | ||
| "pygments_lexer": "ipython3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.