ld2daps/Notebooks/Node Treshold.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node Treshold Design\n",
    "\n",
    "We want to apply the treshold filter on the whole stack of rasters, but filter each raster independently."
   ]
  },
  {
   "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",
    "import collections\n",
    "\n",
    "ld2dap_path = Path('../')\n",
    "sys.path.append(str(ld2dap_path.resolve()))\n",
    "import ld2dap\n",
    "from ld2dap.core import Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and display 2 rasters with different order of magnitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rasters = ['../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif',\n",
    "           '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif']\n",
    "\n",
    "in_test = ld2dap.LoadTIFF(rasters)\n",
    "disp_test = ld2dap.ShowFig(stack_id='all')\n",
    "\n",
    "disp_test.input = in_test\n",
    "disp_test.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata copy bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tresh_test = ld2dap.Treshold(treshold=1e4)\n",
    "tresh2_test = ld2dap.Treshold(treshold=1e3)\n",
    "\n",
    "o = ld2dap.RawOutput()\n",
    "\n",
    "in_test = ld2dap.LoadTIFF(rasters)\n",
    "tresh_test.input = in_test\n",
    "tresh2_test.input = tresh_test\n",
    "disp_test.input = tresh_test\n",
    "o.input = in_test\n",
    "\n",
    "\n",
    "disp_test.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New treshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o.data.max(axis=(0,1)).shape, o.data.max(axis=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 1e3\n",
    "\n",
    "o.run()\n",
    "f = o.data > t\n",
    "o.data[f] = np.nan\n",
    "m = np.nanmax(o.data, axis=(0,1))\n",
    "r = f.sum(axis=(0,1))\n",
    "\n",
    "display(m, r)\n",
    "\n",
    "o.data = np.rollaxis(o.data, 2)\n",
    "f = np.rollaxis(f, 2)\n",
    "\n",
    "o.data[f] = np.repeat(m, r)\n",
    "o.data = np.rollaxis(o.data, 0, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o.data.max(axis=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.zeros((10,20,2))\n",
    "display(A.shape)\n",
    "\n",
    "A = np.rollaxis(A, 2)\n",
    "display(A.shape)\n",
    "\n",
    "A = np.rollaxis(A,0,3)\n",
    "A.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.zeros((10,10,2))\n",
    "A[:,:,1] = 1\n",
    "\n",
    "F = np.random.random((10,10,2)) < .2\n",
    "\n",
    "r = F.sum(axis=(0,1))\n",
    "m = np.array([10, 20])\n",
    "\n",
    "A = np.rollaxis(A, 2)\n",
    "F = np.rollaxis(F, 2)\n",
    "\n",
    "A[F] = np.repeat(m, r)\n",
    "\n",
    "A = np.rollaxis(A, 0, 3)\n",
    "A\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "want = f.sum(axis=(0,1))\n",
    "want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.repeat(np.nanmax(o.data, axis=(0,1)), f.sum(axis=(0,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o.data[f] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.tile(m, (2,1))"
   ]
  }
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