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21
graphs/3d-eg.py
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21
graphs/3d-eg.py
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@ -0,0 +1,21 @@
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from lib.lib import read_xy_alt, read_load
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import matplotlib as mpl
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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import matplotlib.pyplot as plt
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full_data = read_load("/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/data-analysis/data/rust-3d", read_xy_alt)
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run1 = full_data[full_data.run == '1']
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max_actual_radius = max(run1.x.abs().max(), run1.y.abs().max(), run1.z.abs().max())
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max_radius = int(np.exp2(np.floor(np.log2(max_actual_radius)) + 1))
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data = np.zeros((max_radius * 2, max_radius * 2, max_radius * 2))
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data[run1.x + max_radius, run1.y + max_radius, run1.z + max_radius] = 1
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# %%
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fig = plt.figure(figsize=(10, 10))
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ax = fig.add_subplot(projection='3d')
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ax.scatter(run1.x, run1.y, run1.z, c=run1.N)
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fig.savefig("../figures/3d-eg.png")
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fig.savefig("../figures/3d-eg.svg")
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@ -41,8 +41,8 @@ nc_fd(direct_meaned_by_N, label="IPC + 3D Direct", color="tab:blue", no_error=Tr
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nc_fd(rust_direct_meaned_by_N, label="NF 3D Direct", color="tab:orange", no_error=True)
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nc_fd(rust_offaxis_meaned_by_N, label="NF 3D Off-axis", color="tab:purple", linestyle='dashdot', no_error=True)
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plt.plot([np.min(c_direct_neighbours.N), np.max(c_direct_neighbours.N)], [2.5, 2.5], color='tab:red', label='Literature')
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# plt.fill_between(c_direct_neighbours.N, 2.5 - 0.01, 2.5 + 0.01, alpha=0.2, color='tab:red', label='Literature error band')
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plt.plot([np.min(c_direct_neighbours.N), np.max(c_direct_neighbours.N)], [2.51, 2.51], color='tab:red', label='Literature')
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# plt.fill_between(c_direct_neighbours.N, 2.51 - 0.01, 2.51 + 0.01, alpha=0.2, color='tab:red', label='Literature error band')
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plt.xlabel("$N_C$")
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plt.ylabel("$fd$ (instantaneous)")
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@ -54,6 +54,7 @@ df = read_xy_alt(
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"/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/data-analysis/data/alpha/1.csv"
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)
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max_actual_radius = max(df.x.abs().max(), df.y.abs().max())
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max_radius = int(np.exp2(np.floor(np.log2(max_actual_radius)) + 1))
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data = np.zeros((max_radius * 2, max_radius * 2))
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data[df.x + max_radius, df.y + max_radius] = 1
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78
lib/fd.py
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78
lib/fd.py
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@ -0,0 +1,78 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Sep 16 09:47:15 2019
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@author: daniel
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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def fractal_dimension(array, max_box_size=None, min_box_size=1, n_samples=20, n_offsets=0, plot=False):
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"""Calculates the fractal dimension of a 3D numpy array.
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Args:
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array (np.ndarray): The array to calculate the fractal dimension of.
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max_box_size (int): The largest box size, given as the power of 2 so that
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2**max_box_size gives the sidelength of the largest box.
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min_box_size (int): The smallest box size, given as the power of 2 so that
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2**min_box_size gives the sidelength of the smallest box.
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Default value 1.
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n_samples (int): number of scales to measure over.
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n_offsets (int): number of offsets to search over to find the smallest set N(s) to
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cover all voxels>0.
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plot (bool): set to true to see the analytical plot of a calculation.
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"""
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# determine the scales to measure on
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if max_box_size == None:
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# default max size is the largest power of 2 that fits in the smallest dimension of the array:
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max_box_size = int(np.floor(np.log2(np.min(array.shape))))
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scales = np.floor(np.logspace(max_box_size, min_box_size, num=n_samples, base=2))
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scales = np.unique(scales) # remove duplicates that could occur as a result of the floor
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# get the locations of all non-zero pixels
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locs = np.where(array > 0)
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voxels = np.array([(x, y, z) for x, y, z in zip(*locs)])
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# count the minimum amount of boxes touched
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Ns = []
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# loop over all scales
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for scale in scales:
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touched = []
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if n_offsets == 0:
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offsets = [0]
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else:
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offsets = np.linspace(0, scale, n_offsets)
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# search over all offsets
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for offset in offsets:
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bin_edges = [np.arange(0, i, scale) for i in array.shape]
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bin_edges = [np.hstack([0 - offset, x + offset]) for x in bin_edges]
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H1, e = np.histogramdd(voxels, bins=bin_edges)
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touched.append(np.sum(H1 > 0))
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Ns.append(touched)
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Ns = np.array(Ns)
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# From all sets N found, keep the smallest one at each scale
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Ns = Ns.min(axis=1)
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# Only keep scales at which Ns changed
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scales = np.array([np.min(scales[Ns == x]) for x in np.unique(Ns)])
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Ns = np.unique(Ns)
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Ns = Ns[Ns > 0]
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scales = scales[:len(Ns)]
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# perform fit
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coeffs = np.polyfit(np.log(1 / scales), np.log(Ns), 1)
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# make plot
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if plot:
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fig, ax = plt.subplots(figsize=(8, 6))
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ax.scatter(np.log(1 / scales), np.log(np.unique(Ns)), c="teal", label="Measured ratios")
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ax.set_ylabel("$\log N(\epsilon)$")
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ax.set_xlabel("$\log 1/ \epsilon$")
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fitted_y_vals = np.polyval(coeffs, np.log(1 / scales))
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ax.plot(np.log(1 / scales), fitted_y_vals, "k--", label=f"Fit: {np.round(coeffs[0], 3)}X+{coeffs[1]}")
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ax.legend();
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return (coeffs[0])
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1755
notebooks/.ipynb_checkpoints/bc1-checkpoint.ipynb
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1755
notebooks/.ipynb_checkpoints/bc1-checkpoint.ipynb
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312
notebooks/bc1.ipynb
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notebooks/bc1.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"Created on Mon Sep 16 09:47:15 2019\n",
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"@author: daniel\n",
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"\"\"\"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"def fractal_dimension(array, max_box_size=None, min_box_size=1, n_samples=20, n_offsets=0, plot=False):\n",
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" \"\"\"Calculates the fractal dimension of a 3D numpy array.\n",
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"\n",
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" Args:\n",
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" array (np.ndarray): The array to calculate the fractal dimension of.\n",
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" max_box_size (int): The largest box size, given as the power of 2 so that\n",
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" 2**max_box_size gives the sidelength of the largest box.\n",
|
||||
" min_box_size (int): The smallest box size, given as the power of 2 so that\n",
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" 2**min_box_size gives the sidelength of the smallest box.\n",
|
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" Default value 1.\n",
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" n_samples (int): number of scales to measure over.\n",
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" n_offsets (int): number of offsets to search over to find the smallest set N(s) to\n",
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" cover all voxels>0.\n",
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" plot (bool): set to true to see the analytical plot of a calculation.\n",
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"\n",
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"\n",
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" \"\"\"\n",
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" # determine the scales to measure on\n",
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" if max_box_size == None:\n",
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" # default max size is the largest power of 2 that fits in the smallest dimension of the array:\n",
|
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" max_box_size = int(np.floor(np.log2(np.min(array.shape))))\n",
|
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" scales = np.floor(np.logspace(max_box_size, min_box_size, num=n_samples, base=2))\n",
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" scales = np.unique(scales) # remove duplicates that could occur as a result of the floor\n",
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"\n",
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" # get the locations of all non-zero pixels\n",
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" locs = np.where(array > 0)\n",
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" voxels = np.array([(x, y, z) for x, y, z in zip(*locs)])\n",
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"\n",
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" # count the minimum amount of boxes touched\n",
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" Ns = []\n",
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" # loop over all scales\n",
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" for scale in scales:\n",
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" touched = []\n",
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" if n_offsets == 0:\n",
|
||||
" offsets = [0]\n",
|
||||
" else:\n",
|
||||
" offsets = np.linspace(0, scale, n_offsets)\n",
|
||||
" # search over all offsets\n",
|
||||
" for offset in offsets:\n",
|
||||
" bin_edges = [np.arange(0, i, scale) for i in array.shape]\n",
|
||||
" bin_edges = [np.hstack([0 - offset, x + offset]) for x in bin_edges]\n",
|
||||
" H1, e = np.histogramdd(voxels, bins=bin_edges)\n",
|
||||
" touched.append(np.sum(H1 > 0))\n",
|
||||
" Ns.append(touched)\n",
|
||||
" Ns = np.array(Ns)\n",
|
||||
"\n",
|
||||
" # From all sets N found, keep the smallest one at each scale\n",
|
||||
" Ns = Ns.min(axis=1)\n",
|
||||
"\n",
|
||||
" # Only keep scales at which Ns changed\n",
|
||||
" scales = np.array([np.min(scales[Ns == x]) for x in np.unique(Ns)])\n",
|
||||
"\n",
|
||||
" Ns = np.unique(Ns)\n",
|
||||
" Ns = Ns[Ns > 0]\n",
|
||||
" scales = scales[:len(Ns)]\n",
|
||||
" # perform fit\n",
|
||||
" coeffs = np.polyfit(np.log(1 / scales), np.log(Ns), 1)\n",
|
||||
"\n",
|
||||
" # make plot\n",
|
||||
" if plot:\n",
|
||||
" fig, ax = plt.subplots(figsize=(8, 6))\n",
|
||||
" ax.scatter(np.log(1 / scales), np.log(np.unique(Ns)), c=\"teal\", label=\"Measured ratios\")\n",
|
||||
" ax.set_ylabel(\"$\\log N(\\epsilon)$\")\n",
|
||||
" ax.set_xlabel(\"$\\log 1/ \\epsilon$\")\n",
|
||||
" fitted_y_vals = np.polyval(coeffs, np.log(1 / scales))\n",
|
||||
" ax.plot(np.log(1 / scales), fitted_y_vals, \"k--\", label=f\"Fit: {np.round(coeffs[0], 3)}X+{coeffs[1]}\")\n",
|
||||
" ax.legend();\n",
|
||||
" return (coeffs[0])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from lib.lib import read_xy_alt, read_load\n",
|
||||
"\n",
|
||||
"full_data = read_load(\"/Users/joshuacoles/Developer/checkouts/jc3091/CompB DLA/data-analysis/data/rust-3d\", read_xy_alt)\n",
|
||||
"df = full_data[full_data.run == '1']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"max_actual_radius = max(df.x.abs().max(), df.y.abs().max(), df.z.abs().max())\n",
|
||||
"max_radius = int(np.exp2(np.floor(np.log2(max_actual_radius)) + 1))\n",
|
||||
"data = np.zeros((max_radius * 2, max_radius * 2, max_radius * 2))\n",
|
||||
"data[df.x + max_radius, df.y + max_radius, df.z + max_radius] = 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "(128, 128, 128)"
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "5000.0"
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.sum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "(5000, 8)"
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dfs = []\n",
|
||||
"for _, df in full_data.groupby('run'):\n",
|
||||
" max_actual_radius = max(df.x.abs().max(), df.y.abs().max(), df.z.abs().max())\n",
|
||||
" max_radius = int(np.exp2(np.floor(np.log2(max_actual_radius)) + 1))\n",
|
||||
" data = np.zeros((max_radius * 2, max_radius * 2, max_radius * 2))\n",
|
||||
" data[df.x + max_radius, df.y + max_radius, df.z + max_radius] = 1\n",
|
||||
" dfs.append(fractal_dimension(data))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "0.1543448984741014"
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.mean(np.array(dfs))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.scatter(run1.x, run1.y, run1.z)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib as mpl\n",
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10, 10))\n",
|
||||
"ax = fig.add_subplot(projection='3d')\n",
|
||||
"ax.scatter(run1.x, run1.y,run1.z, c=run1.N)\n",
|
||||
"fig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'mpld3'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
||||
"\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)",
|
||||
"Cell \u001B[0;32mIn[24], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpyplot\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mplt\u001B[39;00m\u001B[38;5;241m,\u001B[39m \u001B[38;5;21;01mmpld3\u001B[39;00m\n",
|
||||
"\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'mpld3'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt, mpld3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
162
poetry.lock
generated
162
poetry.lock
generated
@ -157,14 +157,14 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "beautifulsoup4"
|
||||
version = "4.11.2"
|
||||
version = "4.12.0"
|
||||
description = "Screen-scraping library"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6.0"
|
||||
files = [
|
||||
{file = "beautifulsoup4-4.11.2-py3-none-any.whl", hash = "sha256:0e79446b10b3ecb499c1556f7e228a53e64a2bfcebd455f370d8927cb5b59e39"},
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||||
{file = "beautifulsoup4-4.11.2.tar.gz", hash = "sha256:bc4bdda6717de5a2987436fb8d72f45dc90dd856bdfd512a1314ce90349a0106"},
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -1970,89 +1970,89 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pyzmq"
|
||||
version = "25.0.1"
|
||||
version = "25.0.2"
|
||||
description = "Python bindings for 0MQ"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
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||||
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{file = "pyzmq-25.0.2-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:5d496815074e3e3d183fe2c7fcea2109ad67b74084c254481f87b64e04e9a471"},
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{file = "pyzmq-25.0.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:56a94ab1d12af982b55ca96c6853db6ac85505e820d9458ac76364c1998972f4"},
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{file = "pyzmq-25.0.2.tar.gz", hash = "sha256:6b8c1bbb70e868dc88801aa532cae6bd4e3b5233784692b786f17ad2962e5149"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user