{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Histogram APs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "from pathlib import Path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "triskele_path = Path('../triskele/python/')\n", "sys.path.append(str(triskele_path.resolve()))\n", "import triskele" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Specific Utils\n", "\n", "def DFC_filter(raster):\n", " raster[raster > 1e4] = raster[raster < 1e4].max()\n", "\n", "def show(im):\n", " plt.figure(figsize=(16*2,3*2))\n", " plt.imshow(im)\n", " plt.colorbar()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "raster_p = Path('../Data/phase1_rasters/Intensity_C3/UH17_GI3F051_TR.tif')\n", "raster = triskele.read(raster_p)\n", "DFC_filter(raster)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "show(raster)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "area = np.array([25, 100, 500, 1e3, 5e3, 10e3, 50e3, 100e3, 150e3])\n", "\n", "t = triskele.Triskele(raster, verbose=False)\n", "attributes = t.filter(tree='tos-tree',\n", " area=area\n", " )\n", "attributes.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = attributes\n", "i = 3\n", "show(a[:,:,i].astype(np.float) - a[:,:,i+1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "patch_size = 5\n", "\n", "### \n", "amp = int((patch_size - 1 ) / 2)\n", "\n", "stack = list()\n", "for i in range(-amp, amp+1):\n", " ai = i if i > 0 else None\n", " bi = i if i < 0 else None\n", " ci = -bi if bi is not None else None\n", " di = -ai if ai is not None else None\n", "\n", " for j in range(-amp, amp+1):\n", " offset = np.zeros(raster.shape)\n", " aj = j if j > 0 else None\n", " bj = j if j < 0 else None\n", " cj = -bj if bj is not None else None\n", " dj = -aj if aj is not None else None\n", " print('{}:{} {}:{} - {}:{} {}:{}'.format(ai, bi, ci, di, aj, bj, cj, dj))\n", " offset[ai:bi, aj:bj] = raster[ci:di, cj:dj]\n", " stack.append(offset)\n", "\n", "offset_stack = np.stack(stack, axis=2)\n", "offset_stack.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "offset_stack[-3,-3,:].reshape(-1,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "show(offset_stack[:,:,0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "del offset_stack" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hist = np.stack(raster, raster[:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.repeat(100,10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pstl = list()\n", "\n", "for c, p in [(100, .06), (60, .1), (12, .5), (20, .3)]:\n", " st = np.random.binomial(np.repeat(c,1e7), p)\n", " pst = pd.DataFrame(st, columns=['{} - {}'.format(c, p)])\n", " pstl.append(pst)\n", " \n", "psta = pd.concat(pstl)\n", "pd.options.display.float_format = '{:.2f}'.format\n", "display(psta.describe())\n", "psta.hist(figsize=(16, 9))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.concat([pst, pst], axis=1)" ] } ], "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" } }, "nbformat": 4, "nbformat_minor": 2 }