pyTrainSeg/V2_example_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "309fd805-71cb-4c0d-b1ff-c8b2123860f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modules\n",
    "import os\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import dask\n",
    "import dask.array\n",
    "from scipy import ndimage\n",
    "from skimage import filters, feature, io\n",
    "from skimage.morphology import disk,ball\n",
    "import sys\n",
    "from itertools import combinations_with_replacement\n",
    "import pickle\n",
    "import imageio\n",
    "import json\n",
    "from dask.distributed import Client, LocalCluster\n",
    "import socket\n",
    "import subprocess\n",
    "import gc\n",
    "import h5py\n",
    "import logging\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "from dask import config as cfg\n",
    "# cfg.set({'distributed.scheduler.worker-ttl': None, # Workaround so that dask does not kill workers while they are busy fetching data: https://dask.discourse.group/t/dask-workers-killed-because-of-heartbeat-fail/856, maybe this helps: https://www.youtube.com/watch?v=vF2VItVU5zg?\n",
    "#         'distributed.scheduler.transition-log-length': 100, #potential workaround for ballooning scheduler memory https://baumgartner.io/posts/how-to-reduce-memory-usage-of-dask-scheduler/\n",
    "#          'distributed.scheduler.events-log-length': 100\n",
    "#         }) seems to be outdate\n",
    "\n",
    "cfg.set({'distributed.scheduler.worker-ttl': None, # Workaround so that dask does not kill workers while they are busy fetching data: https://dask.discourse.group/t/dask-workers-killed-because-of-heartbeat-fail/856, maybe this helps: https://www.youtube.com/watch?v=vF2VItVU5zg?\n",
    "        'distributed.admin.low-level-log-length': 100 #potential workaround for ballooning scheduler memory https://baumgartner.io/posts/how-to-reduce-memory-usage-of-dask-scheduler/\n",
    "        }) # still relevant ?\n",
    "\n",
    "#paths\n",
    "host = socket.gethostname()\n",
    "if host == 'mpc2959.psi.ch':\n",
    "    temppath = '/mnt/SSD/fische_r/tmp'\n",
    "    training_path =  '/mnt/SSD/fische_r/Tomcat_2/'\n",
    "    pytrainpath = '/mpc/homes/fische_r/lib/pytrainseg'\n",
    "    # memlim = '840GB'\n",
    "    memlim = '440GB'\n",
    "    # memlim = '920GB'\n",
    "elif host[:3] == 'ra-':\n",
    "    temppath = '/das/home/fische_r/interlaces/Tomcat_2/tmp'\n",
    "    training_path = '/das/home/fische_r/interlaces/Tomcat_2'\n",
    "    pytrainpath = '/das/home/fische_r/lib/pytrainseg'\n",
    "    memlim = '160GB'  # also fine on the small nodes, you can differentiate more if you want\n",
    "else:\n",
    "    print('host '+host+' currently not supported')\n",
    "    \n",
    "# get the ML functions, TODO: make a library once it works/is in a stable state\n",
    "\n",
    "cwd = os.getcwd()\n",
    "os.chdir(pytrainpath)\n",
    "from V2_feature_stack import image_filter\n",
    "import V2_training as tfs\n",
    "from V2_training import training\n",
    "\n",
    "pytrain_git_sha = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).decode().strip()\n",
    "os.chdir(cwd)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4dd9e6-5b63-4ad6-8a98-c6a12097e95c",
   "metadata": {},
   "source": [
    "### functionalities for interactive training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a5df7a3-4860-4f19-aec8-7c8aa62baeee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import Image\n",
    "from ipywidgets import ColorPicker, IntSlider, link, AppLayout, HBox\n",
    "from ipycanvas import  hold_canvas,  MultiCanvas #RoughCanvas,Canvas,\n",
    "\n",
    "def on_mouse_down(x, y):\n",
    "    global drawing\n",
    "    global position\n",
    "    global shape\n",
    "    drawing = True\n",
    "    position = (x, y)\n",
    "    shape = [position]\n",
    "\n",
    "def on_mouse_move(x, y):\n",
    "    global drawing\n",
    "    global position\n",
    "    global shape\n",
    "    if not drawing:\n",
    "        return\n",
    "    with hold_canvas():\n",
    "        canvas.stroke_line(position[0], position[1], x, y)\n",
    "        position = (x, y)\n",
    "    shape.append(position)\n",
    "\n",
    "def on_mouse_up(x, y, fill=False):\n",
    "    global drawing\n",
    "    global positiondu\n",
    "    global shape\n",
    "    drawing = False\n",
    "    with hold_canvas():\n",
    "        canvas.stroke_line(position[0], position[1], x, y)\n",
    "        if fill:\n",
    "            canvas.fill_polygon(shape)\n",
    "    shape = []\n",
    "    \n",
    "def display_feature(i, TS, feat_stack):\n",
    "    # print('selected '+TS.feature_names[i])\n",
    "    im = feat_stack[:,:,i]\n",
    "    im8 = im-im.min()\n",
    "    im8 = im8/im8.max()*255\n",
    "    return im8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8560ff91-bcd5-40ec-9094-e4f36b7c8099",
   "metadata": {},
   "source": [
    "### fire up dask, distributed Client currently not usable. No idea how not setting up dask affects the computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2542014d-064e-438a-ae38-ca1386c3be3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dask.config.config['temporary-directory'] = temppath\n",
    "def boot_client(dashboard_address=':35000', memory_limit = memlim, n_workers=2): # 2 workers appears to be the optimum, will still distribute over the full machine\n",
    "    tempfolder = temppath  #a big SSD is a major adavantage to allow spill to disk and still be efficient. large dataset might crash with too small SSD or be slow with normal HDD\n",
    "# tempfolder = temppath_2\n",
    "# dask.config.config['distributed']['worker']['memory']['recent-to-old-time'] = '200000s'\n",
    "\n",
    "# here you have the option to use a virtual cluster or even slurm on ra (not attempted yet)\n",
    "    cluster = LocalCluster(dashboard_address=dashboard_address, memory_limit = memory_limit, n_workers=n_workers, silence_logs=logging.ERROR) #settings optimised for mpc2959, play around if needed, if you know nothing else is using RAM then you can almost go to the limit\n",
    "# # maybe less workers with more threads makes better use of shared memory \n",
    "\n",
    "# # scheduler_port = 'tcp://129.129.188.222:8786' #<-- if scheduler on mpc2959; scheduler on mpc2053 -> 'tcp://129.129.188.248:8786'\n",
    "# # cluster = scheduler_port\n",
    "\n",
    "    client = Client(cluster) #don't show warnings, too many seem to block execution\n",
    "# # client.amm.start()\n",
    "    print('Dashboard at '+client.dashboard_link)\n",
    "    return client, cluster\n",
    "\n",
    "def reboot_client(client, dashboard_address=':35000', memory_limit = memlim, n_workers=2):\n",
    "    client.shutdown()\n",
    "    cluster = LocalCluster(dashboard_address=dashboard_address, memory_limit = memory_limit, n_workers=n_workers, silence_logs=logging.ERROR)\n",
    "    client = Client(cluster)\n",
    "    return client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34ac3066-3519-46a7-82a9-ae325dd8a44e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dashboard at http://127.0.0.1:35000/status\n"
     ]
    }
   ],
   "source": [
    "client, cluster = boot_client()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9cc8b5-fdbe-44aa-b33e-330f418c4d6c",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253645ef-8cba-4104-8106-caa12843177e",
   "metadata": {},
   "source": [
    "### let dask load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b429065-b629-4f9b-982c-ebb300e2f840",
   "metadata": {},
   "source": [
    "store data in a hdf5 (eg. xarray to .nc) as an entry 'image_data' containing a 4D array. There is potential in structuring the data on the disk (SSD recommended for fast data streaming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92cb8c50-2675-434d-86ce-2e27b959630a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = 'R_m7_33_200_1_II'\n",
    "imagepath = os.path.join(training_path, '01_'+sample+'_cropped.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e4d57ef-beb1-4777-82f7-c149dd26ed81",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = h5py.File(imagepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac6b2b37-ff35-45b4-a1b6-8242abf58bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_space = 36 # potential for optmisation by matching chunksize with planned image filter kernels and file structure on disk for fast data streaming\n",
    "chunks = (chunk_space,chunk_space,chunk_space,len(file['time']))\n",
    "da = dask.array.from_array(file['image_data'], chunks= chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b67658-e114-4f1d-b504-c1f2d438d226",
   "metadata": {},
   "source": [
    "### get data into image filter class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17323a57-2b15-4deb-a09c-3ec30f56fc72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: include this routine into pytrainseg\n",
    "\n",
    "IF = image_filter(sigmas = [0,1,3,6]) \n",
    "IF.data = da\n",
    "shp = da.shape\n",
    "shp_raw = shp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474c8b5e-1960-4d20-b238-7e47128ee15a",
   "metadata": {},
   "source": [
    "### prepare features\n",
    "creates a dask graph with dependent calculations to allow on-demand and larger-than-memory calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c56b37eb-e362-437d-a1ff-29614eba62d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load a feature list if it exists\n",
    "prefix = 'd001fb3'\n",
    "feature_names_to_use = ['Gaussian_4D_Blur_0.0',\n",
    " 'Gaussian_4D_Blur_1.0',\n",
    " 'Gaussian_4D_Blur_6.0',\n",
    " 'diff_of_gauss_4D_6.0_0.0',\n",
    " 'diff_of_gauss_4D_6.0_1.0',\n",
    " 'Gradient_sigma_1.0_0',\n",
    " 'Gradient_sigma_1.0_1',\n",
    " 'Gradient_sigma_1.0_3',\n",
    " 'hessian_sigma_1.0_00',\n",
    " 'hessian_sigma_1.0_01',\n",
    " 'hessian_sigma_1.0_11',\n",
    " 'Gradient_sigma_3.0_3',\n",
    " 'hessian_sigma_3.0_00',\n",
    " 'hessian_sigma_3.0_01',\n",
    " 'hessian_sigma_3.0_02',\n",
    " 'hessian_sigma_3.0_03',\n",
    " 'hessian_sigma_3.0_11',\n",
    " 'hessian_sigma_3.0_33',\n",
    " 'Gradient_sigma_6.0_0',\n",
    " 'Gradient_sigma_6.0_1',\n",
    " 'Gradient_sigma_6.0_2',\n",
    " 'Gradient_sigma_6.0_3',\n",
    " 'hessian_sigma_6.0_01',\n",
    " 'hessian_sigma_6.0_03',\n",
    " 'hessian_sigma_6.0_11',\n",
    " 'hessian_sigma_6.0_12',\n",
    " 'hessian_sigma_6.0_13',\n",
    " 'hessian_sigma_6.0_22',\n",
    " 'hessian_sigma_6.0_23',\n",
    " 'hessian_sigma_6.0_33',\n",
    " 'Gradient_sigma_2.0_0',\n",
    " 'Gradient_sigma_2.0_3',\n",
    " 'hessian_sigma_2.0_00',\n",
    " 'hessian_sigma_2.0_01',\n",
    " 'hessian_sigma_2.0_02',\n",
    " 'hessian_sigma_2.0_11',\n",
    " 'hessian_sigma_2.0_13',\n",
    " 'Gaussian_time_0.0',\n",
    " 'Gaussian_time_1.0',\n",
    " 'Gaussian_time_6.0',\n",
    " 'Gaussian_time_2.0',\n",
    " 'Gaussian_space_0.0',\n",
    " 'Gaussian_space_6.0',\n",
    " 'diff_of_gauss_space_3.0_0.0',\n",
    " 'diff_of_gauss_space_6.0_0.0',\n",
    " 'diff_of_gauss_space_2.0_0.0',\n",
    " 'diff_of_gauss_space_3.0_1.0',\n",
    " 'diff_of_gauss_space_6.0_1.0',\n",
    " 'diff_of_gauss_space_2.0_1.0',\n",
    " 'diff_of_gauss_space_2.0_3.0',\n",
    " 'diff_temp_min_Gauss_2.0',\n",
    " 'diff_to_first_',\n",
    " 'full_temp_min_Gauss_2.0',\n",
    " 'first_',\n",
    " 'last_']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18ec839a-1ff2-43f5-a8b7-82f91ff7c941",
   "metadata": {},
   "outputs": [],
   "source": [
    "IF.prepare()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19ab565-1fc5-460d-ab5a-189cf5995438",
   "metadata": {},
   "outputs": [],
   "source": [
    "IF.stack_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c6c793a-d87d-4426-b2f9-83cbf805c225",
   "metadata": {},
   "outputs": [],
   "source": [
    "IF.feature_stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0fb1def-d745-4f44-a6d2-112b72b229fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment if you want to reduce the feature stack\n",
    "IF.reduce_feature_stack(feature_names_to_use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b42712a-4010-4504-a1f5-16204c2385f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "IF.make_xarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4afe2926-bc44-4537-a082-f57ae4999f13",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50172d94-1bbb-432d-8d8f-5346dd5fa894",
   "metadata": {},
   "source": [
    "### set up some objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24cd34dd-e669-4498-824d-c2b2a007b07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_path_sample = os.path.join(training_path, sample)\n",
    "if not os.path.exists(training_path_sample):\n",
    "    os.mkdir(training_path_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7345b149-95df-4ad9-8d28-e898b4610f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS = training(training_path=training_path_sample)\n",
    "TS.client = client\n",
    "IF.client = client\n",
    "TS.cluster = cluster\n",
    "IF.cluster = cluster\n",
    "TS.memlim = memlim\n",
    "TS.n_workers = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3917ccce-73a8-428d-a4f8-d6a3a10935f6",
   "metadata": {},
   "source": [
    "### give the feature stack to the training class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3586f10e-8005-49bc-9e4b-75e70e691c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS.feat_data = IF.feature_xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d20dcbde-9a1f-417e-bf8d-d5350ef11fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "IF.combined_feature_names = list(IF.feature_names) + list(IF.feature_names_time_independent)\n",
    "TS.combined_feature_names = IF.combined_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "61efde8c-d48b-4b5d-98d4-92dbc89ae2d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Gaussian_4D_Blur_0.0\n",
      "1 Gaussian_4D_Blur_1.0\n",
      "2 Gaussian_4D_Blur_3.0\n",
      "3 Gaussian_4D_Blur_6.0\n",
      "4 Gaussian_4D_Blur_2.0\n",
      "5 diff_of_gauss_4D_1.0_0.0\n",
      "6 diff_of_gauss_4D_3.0_0.0\n",
      "7 diff_of_gauss_4D_6.0_0.0\n",
      "8 diff_of_gauss_4D_2.0_0.0\n",
      "9 diff_of_gauss_4D_3.0_1.0\n",
      "10 diff_of_gauss_4D_6.0_1.0\n",
      "11 diff_of_gauss_4D_2.0_1.0\n",
      "12 diff_of_gauss_4D_6.0_3.0\n",
      "13 diff_of_gauss_4D_2.0_3.0\n",
      "14 diff_of_gauss_4D_2.0_6.0\n",
      "15 Gradient_sigma_1.0_0\n",
      "16 Gradient_sigma_1.0_1\n",
      "17 Gradient_sigma_1.0_2\n",
      "18 Gradient_sigma_1.0_3\n",
      "19 hessian_sigma_1.0_00\n",
      "20 hessian_sigma_1.0_01\n",
      "21 hessian_sigma_1.0_02\n",
      "22 hessian_sigma_1.0_03\n",
      "23 hessian_sigma_1.0_11\n",
      "24 hessian_sigma_1.0_12\n",
      "25 hessian_sigma_1.0_13\n",
      "26 hessian_sigma_1.0_22\n",
      "27 hessian_sigma_1.0_23\n",
      "28 hessian_sigma_1.0_33\n",
      "29 Gradient_sigma_3.0_0\n",
      "30 Gradient_sigma_3.0_1\n",
      "31 Gradient_sigma_3.0_2\n",
      "32 Gradient_sigma_3.0_3\n",
      "33 hessian_sigma_3.0_00\n",
      "34 hessian_sigma_3.0_01\n",
      "35 hessian_sigma_3.0_02\n",
      "36 hessian_sigma_3.0_03\n",
      "37 hessian_sigma_3.0_11\n",
      "38 hessian_sigma_3.0_12\n",
      "39 hessian_sigma_3.0_13\n",
      "40 hessian_sigma_3.0_22\n",
      "41 hessian_sigma_3.0_23\n",
      "42 hessian_sigma_3.0_33\n",
      "43 Gradient_sigma_6.0_0\n",
      "44 Gradient_sigma_6.0_1\n",
      "45 Gradient_sigma_6.0_2\n",
      "46 Gradient_sigma_6.0_3\n",
      "47 hessian_sigma_6.0_00\n",
      "48 hessian_sigma_6.0_01\n",
      "49 hessian_sigma_6.0_02\n",
      "50 hessian_sigma_6.0_03\n",
      "51 hessian_sigma_6.0_11\n",
      "52 hessian_sigma_6.0_12\n",
      "53 hessian_sigma_6.0_13\n",
      "54 hessian_sigma_6.0_22\n",
      "55 hessian_sigma_6.0_23\n",
      "56 hessian_sigma_6.0_33\n",
      "57 Gradient_sigma_2.0_0\n",
      "58 Gradient_sigma_2.0_1\n",
      "59 Gradient_sigma_2.0_2\n",
      "60 Gradient_sigma_2.0_3\n",
      "61 hessian_sigma_2.0_00\n",
      "62 hessian_sigma_2.0_01\n",
      "63 hessian_sigma_2.0_02\n",
      "64 hessian_sigma_2.0_03\n",
      "65 hessian_sigma_2.0_11\n",
      "66 hessian_sigma_2.0_12\n",
      "67 hessian_sigma_2.0_13\n",
      "68 hessian_sigma_2.0_22\n",
      "69 hessian_sigma_2.0_23\n",
      "70 hessian_sigma_2.0_33\n",
      "71 Gaussian_time_0.0\n",
      "72 Gaussian_time_1.0\n",
      "73 Gaussian_time_3.0\n",
      "74 Gaussian_time_6.0\n",
      "75 Gaussian_time_2.0\n",
      "76 diff_of_gauss_time_1.0_0.0\n",
      "77 diff_of_gauss_time_3.0_0.0\n",
      "78 diff_of_gauss_time_6.0_0.0\n",
      "79 diff_of_gauss_time_2.0_0.0\n",
      "80 diff_of_gauss_time_3.0_1.0\n",
      "81 diff_of_gauss_time_6.0_1.0\n",
      "82 diff_of_gauss_time_2.0_1.0\n",
      "83 diff_of_gauss_time_6.0_3.0\n",
      "84 diff_of_gauss_time_2.0_3.0\n",
      "85 diff_of_gauss_time_2.0_6.0\n",
      "86 Gaussian_space_0.0\n",
      "87 Gaussian_space_1.0\n",
      "88 Gaussian_space_3.0\n",
      "89 Gaussian_space_6.0\n",
      "90 Gaussian_space_2.0\n",
      "91 diff_of_gauss_space_1.0_0.0\n",
      "92 diff_of_gauss_space_3.0_0.0\n",
      "93 diff_of_gauss_space_6.0_0.0\n",
      "94 diff_of_gauss_space_2.0_0.0\n",
      "95 diff_of_gauss_space_3.0_1.0\n",
      "96 diff_of_gauss_space_6.0_1.0\n",
      "97 diff_of_gauss_space_2.0_1.0\n",
      "98 diff_of_gauss_space_6.0_3.0\n",
      "99 diff_of_gauss_space_2.0_3.0\n",
      "100 diff_of_gauss_space_2.0_6.0\n",
      "101 diff_to_min_\n",
      "102 diff_temp_min_Gauss_2.0\n",
      "103 diff_to_first_\n",
      "104 diff_to_last_\n",
      "105 full_temp_mean_\n",
      "106 full_temp_min_\n",
      "107 full_temp_min_Gauss_2.0\n",
      "108 first_\n",
      "109 last_\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(TS.combined_feature_names)):\n",
    "    print(i, TS.combined_feature_names[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace7de1a-1f06-41c3-b889-18062569b54a",
   "metadata": {},
   "source": [
    "### interactive training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28dbf4b3-9e2a-49ea-8fa3-cbc2b15999f9",
   "metadata": {},
   "source": [
    "#### load training dict if you have one and want to use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "60943ced-cbdd-499a-b869-9f4d52292259",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_prefix = '093c73c'\n",
    "TS.previous_training_dict = pickle.load(open(os.path.join(training_path, training_prefix+'_training_dict.p'), 'rb'))\n",
    "TS.previous_feature_names = pickle.load(open(os.path.join(training_path, training_prefix+'_feature_names.p'), 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cfe513d-4a8c-4d27-8604-942dbfcd8dc9",
   "metadata": {},
   "source": [
    "#### reduce the previous training dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92da87f4-e323-4998-b410-427f5801f603",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced the previous training dict to  55 / 110 features considering the  55  desired features.\n",
      "0  desired features were not found in the training dict.\n"
     ]
    }
   ],
   "source": [
    "num_feat = len(IF.combined_feature_names)\n",
    "num_feat_to_use = len(feature_names_to_use)\n",
    "feature_ids = np.zeros(num_feat, dtype=bool)\n",
    "for i in range(num_feat):\n",
    "    if IF.combined_feature_names[i] in feature_names_to_use:\n",
    "        feature_ids[i] = True\n",
    "\n",
    "TS.training_dict = {}\n",
    "for training_set in TS.previous_training_dict.keys():\n",
    "    X,y = TS.previous_training_dict[training_set]\n",
    "    X = X[:,feature_ids]                          # since there is no copy, probably previous_training_dict and training_dict will become the same, reload the previous dict if necessary\n",
    "    TS.training_dict[training_set] = X,y\n",
    "\n",
    "print('Reduced the previous training dict to ',str(np.count_nonzero(feature_ids)),'/',str(num_feat),'features considering the ',str(num_feat_to_use),' desired features.')\n",
    "print(str(num_feat_to_use-np.count_nonzero(feature_ids)), ' desired features were not found in the training dict.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1347f9cd-c700-4680-a9da-ea6c1268a734",
   "metadata": {},
   "source": [
    "#### re-train with existing label sets. clear the training dictionary if necessary (training_dict)\n",
    "don't use this if you already have a pickled training_dict  \n",
    "TODO: reduce training dict with feature_ids instead of retraining with the label images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "010cdf6b-3778-4f73-8e5c-91ab98c8dd3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8644b995-120c-4174-8c03-99e23aa6385b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TS.training_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0c11828c-bacf-4e3f-946f-a3b52d357c27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# TS.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612cd613-0f04-4bcb-a949-2fd941a0fe71",
   "metadata": {},
   "source": [
    "#### import training dict of other samples \n",
    "(replace sample name and repeat for multiple samples), if necessary check features for overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "deb31b96-491a-4ba7-bad3-fa2257b22176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: make better naming convention below when pickling the training_dict\n",
    "\n",
    "# oldsample = '4'\n",
    "# oldgitsha = 'e42ad75' #'109a7ce3' #retrain at one point\n",
    "# # if oldsample == '4':\n",
    "# #     training_dict_old = pickle.load(open(os.path.join(toppathSSD, '05_water_GDL_ML', '4', 'ec4415d_training_dict_without_loc_feat.p'), 'rb'))\n",
    "# # else:\n",
    "# training_dict_old = pickle.load(open(os.path.join(training_path, oldsample,  oldgitsha+'_training_dict.p'),'rb'))\n",
    "# oldfeatures = pickle.load(open(os.path.join(training_path, oldsample,  oldgitsha+'_feature_names.p'),'rb'))\n",
    "    \n",
    "#     # pickle.dump(TS.training_dict, open(os.path.join(TS.training_path, pytrain_git_sha+'_training_dict.p'),'wb'))\n",
    "# # pickle.dump(TS.feature_names, open(os.path.join(TS.training_path, pytrain_git_sha+'_feature_names.p'),'wb'))\n",
    "\n",
    "# for key in training_dict_old.keys():\n",
    "#     TS.training_dict[oldsample+key] = training_dict_old[key]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8b9278-4e9d-4929-aae6-1acd9f2f0202",
   "metadata": {},
   "source": [
    "#### suggest a new training coordinate\n",
    "currently retraining with new feature stack not properly implemented. Workaround: choose from the exiting training sets and train with them (additional labeling optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "65e9e69a-fe87-4fbb-8ec6-d724149e5411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You could try  x = 52  at time step  22\n"
     ]
    }
   ],
   "source": [
    "TS.suggest_training_set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0a0819f1-708a-40e0-90bf-98ad2ace0607",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = 'z'\n",
    "p1 = 1304\n",
    "c2 = 'time'  # c2 has always to be time currently . Removed option to chose two spatial coordinates because was not useful, but left syntax to keep the potential to add it again in the future\n",
    "p2 = 51"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e911d2-28ea-4ec9-b82a-a845d3d42cc1",
   "metadata": {},
   "source": [
    "### activate the training set and load label images if existent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "954cda71-ed7c-4d34-af8e-77c0d1235ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "existing label set loaded\n"
     ]
    }
   ],
   "source": [
    "TS.load_training_set(c1, p1, c2, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0c877470-3ea2-46e5-b0c6-f7f5278d7949",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reboot the dask client if it lost a worker\n",
    "if not len(client.cluster.workers)>1:   \n",
    "    client = reboot_client(client)\n",
    "    TS.client = client\n",
    "    IF.client = client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a87f0453-9c02-426f-8c97-01662cd76ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TODO: move  the routine into training class\n",
    "# TODO: add if clause to not do anything if the coordinates did not change and the stack has already been calcualted\n",
    "\n",
    "feat_data = TS.feat_data\n",
    "[c1,p1,c2,p2] = TS.current_coordinates\n",
    "newslice = True\n",
    "\n",
    "if c1 == 'x' and c2 == 'time':\n",
    "    feat_stack = feat_data['feature_stack'].sel(x = p1, time = p2)\n",
    "    feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, time_0 = 0)\n",
    "# elif c1 == 'x' and c2 == 'y':\n",
    "#     feat_stack = feat_data['feature_stack'].sel(x = p1, y = p2)#.data\n",
    "#     feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, y = p2)\n",
    "# elif c1 == 'x' and c2 == 'z':\n",
    "#     feat_stack = feat_data['feature_stack'].sel(x = p1, z = p2)#.data\n",
    "#     feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, z = p2)\n",
    "# elif c1 == 'y' and c2 == 'z':\n",
    "#     feat_stack = feat_data['feature_stack'].sel(y = p1, z = p2)#.data\n",
    "#     feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, z = p2)\n",
    "elif c1 == 'y' and c2 == 'time':\n",
    "    feat_stack = feat_data['feature_stack'].sel(y = p1, time = p2)#.data\n",
    "    feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, time_0 = 0)\n",
    "elif c1 == 'z' and c2 == 'time':\n",
    "    feat_stack = feat_data['feature_stack'].sel(z = p1, time = p2)#.data\n",
    "    feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(z = p1, time_0 = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5041ac7-dddb-400f-8bee-82e0f1abdf12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reboot the dask client if it lost a worker\n",
    "if not len(client.cluster.workers)>1:   \n",
    "    client = reboot_client(client)\n",
    "    TS.client = client\n",
    "    IF.client = client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db7104c-33d5-493b-a44a-6415e78b3d0c",
   "metadata": {},
   "source": [
    "### calculate the feature stack for the selected slice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c60ef8b-3856-4b7c-be66-544c6e8c410c",
   "metadata": {},
   "source": [
    "#### time dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b4a9ae4-5397-4d83-87a8-50c0852717d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#  TODO: move into training class and keep up to date with dask development for the best way to do this\n",
    "# watch the dashboard for some colorful process tracing, having a eye on the \"workers\" can help to deal with memory issues\n",
    "if type(feat_stack) is not np.ndarray:\n",
    "        fut = client.scatter(feat_stack)\n",
    "        fut = fut.result()\n",
    "        fut = fut.compute()\n",
    "        feat_stack = fut\n",
    "        try:\n",
    "            # restart dask client to wipe leaked memory\n",
    "            client.restart()\n",
    "        except:\n",
    "            # do a full reboot if this fails\n",
    "            client = reboot_client(client)\n",
    "            TS.client = client\n",
    "            IF.client = client   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d152717b-e481-4776-8298-760e8dc79c5d",
   "metadata": {},
   "source": [
    "#### check if the cluster survived the calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e413bea9-9820-4b91-b32c-a95df4f70021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: <Nanny: tcp://127.0.0.1:32821, threads: 28>,\n",
       " 1: <Nanny: tcp://127.0.0.1:38097, threads: 28>}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# needs to stay to be interactive\n",
    "client.cluster.workers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "781b6fe8-fdd4-46e9-90f3-69ca679b194f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # reboot the dask client if it lost a worker\n",
    "if not len(client.cluster.workers)>1:   \n",
    "    client = reboot_client(client)\n",
    "    TS.client = client\n",
    "    IF.client = client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd543110-7e93-44f2-95ca-1b934c0e7dbc",
   "metadata": {},
   "source": [
    "#### time independent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f5cffd5f-93aa-4580-9dcc-32b2565ebffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# move into training class at one pointl\n",
    "if type(feat_stack_t_idp) is not np.ndarray:\n",
    "        fut = client.scatter(feat_stack_t_idp)\n",
    "        fut = fut.result()\n",
    "        fut = fut.compute()\n",
    "        feat_stack_t_idp = fut\n",
    "        try:\n",
    "            client.restart()\n",
    "        except:\n",
    "            client = reboot_client(client)\n",
    "            TS.client = client\n",
    "            IF.client = client   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef3f26f0-87ec-40f8-b8ec-e78a4269a1db",
   "metadata": {},
   "source": [
    "#### check if the cluster survived the calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4f81e1af-6dc0-4e77-9b76-1847cb59eda7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: <Nanny: tcp://127.0.0.1:42863, threads: 28>,\n",
       " 1: <Nanny: tcp://127.0.0.1:44909, threads: 28>}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# needs to stay to be interactive\n",
    "client.cluster.workers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "94384d23-6763-4024-9ac7-8548af2fcc1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am back from calculating and have still 2 workers\n"
     ]
    }
   ],
   "source": [
    "print('I am back from calculating and have still '+str(len(client.cluster.workers))+' workers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8c25dde1-5156-4f55-8643-62ad87982a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # reboot the dask client if it lost a worker\n",
    "if not len(client.cluster.workers)>1:   \n",
    "    client = reboot_client(client)\n",
    "    TS.client = client\n",
    "    IF.client = client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3529eb5f-ac36-47d7-b2f7-5c562ae6642e",
   "metadata": {},
   "source": [
    "#### merge the two feature stacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5821518a-eb06-444e-b84e-9f69ad73bbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# needs to stay to be interactive\n",
    "# feat_stack_full = np.concatenate([feat_stack, feat_stack_t_idp], axis = 2)\n",
    "feat_stack = np.concatenate([feat_stack, feat_stack_t_idp], axis = 2) #this line to save a bit RAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bf0c4088-0aec-4ffe-8741-b4db50770e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 330, 55)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_stack.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b044a99-f5a3-4cb1-b727-bf319ebbcd1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this necessary ??\n",
    "# TS.current_feat_stack_full = feat_stack_full\n",
    "TS.current_feat_stack_full = feat_stack\n",
    "if type(TS.current_feat_stack_full) is not np.ndarray:\n",
    "    TS.current_computed = False\n",
    "else:\n",
    "    TS.current_computed = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5b513b56-5f6f-473b-95fc-aa7c593b5339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 330, 55)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS.current_feat_stack_full.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618c7603-a9ac-457e-989e-cec055800e4a",
   "metadata": {},
   "source": [
    "### canvas for labeling and training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa400a60-f3ed-46d7-9f4c-d5b9e3ac4a6d",
   "metadata": {},
   "source": [
    "#### give index of feature in case you want to use it for training\n",
    "can be very useful to for example label static components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "90e64935-662f-4c53-91f4-0e9637bee002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 diff_of_gauss_4D_6.0_1.0\n"
     ]
    }
   ],
   "source": [
    "# TODO: consider reduced feature number\n",
    "i  = 10\n",
    "print(i, IF.combined_feature_names[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8e4971a5-d3f9-4d57-8fcf-96e46ec67947",
   "metadata": {},
   "outputs": [],
   "source": [
    "im8 = TS.current_im8 # execute this line to get the orignal display"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c39bfc-2864-452d-b564-0ade86db21d6",
   "metadata": {},
   "source": [
    "#### set up the canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c66f0843-f98c-45dc-b555-902005275a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original shape:  (700, 330)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f64072f853d49a380218efba4f81ff4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(MultiCanvas(height=700, width=330), ColorPicker(value='#ff0000', description='Color:')))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# move to training class at low prio\n",
    "# needs interactive buttons if the code is hidden in class\n",
    "# button for color and alpha\n",
    "# for now, leave exposed\n",
    "\n",
    "alpha = 0.35 #transparance\n",
    "\n",
    "## in case you want to zoom, but it works better if you pan (not zoom) in the browser. panning can be done with the trackpad of a laptop, but there is also some key combinations TODO lock up\n",
    "# zoom1 = (-500,-1)\n",
    "# zoom2 = (600,1400)\n",
    "# zoom1 = (0, -1)\n",
    "# zoom2 = (0, -1)\n",
    "#trick: use gaussian_time_4_0 to label static phases ()\n",
    "# im8 = display_feature(103, TS, feat_stack)\n",
    "\n",
    "# print(IF.combined_feature_names[-20])\n",
    "print('original shape: ',im8.shape)\n",
    "im8_display = im8.copy() #[zoom1[0]:zoom1[1], zoom2[0]:zoom2[1]]\n",
    "# print('diyplay shape : ',im8_display.shape,' at: ', (zoom1[0], zoom2[0]))\n",
    "\n",
    "resultim = TS.current_result.copy()\n",
    "resultim_display = resultim #[zoom1[0]:zoom1[1], zoom2[0]:zoom2[1]]\n",
    "\n",
    "\n",
    "width = im8_display.shape[1]\n",
    "height = im8_display.shape[0]\n",
    "Mcanvas = MultiCanvas(4, width=width, height=height)\n",
    "background = Mcanvas[0]\n",
    "resultdisplay = Mcanvas[2]\n",
    "truthdisplay = Mcanvas[1]\n",
    "canvas = Mcanvas[3]\n",
    "canvas.sync_image_data = True\n",
    "drawing = False\n",
    "position = None\n",
    "shape = []\n",
    "image_data = np.stack((im8_display, im8_display, im8_display), axis=2)\n",
    "background.put_image_data(image_data, 0, 0)\n",
    "slidealpha = IntSlider(description=\"Result overlay\", value=0.15)\n",
    "resultdisplay.global_alpha = alpha #slidealpha.value\n",
    "if np.any(resultim>0):\n",
    "    result_data = np.stack(((resultim_display==0), (resultim_display==1),(resultim_display==2)), axis=2)*255\n",
    "    mask3 = resultim_display==3\n",
    "    result_data[mask3,0] = 255\n",
    "    result_data[mask3,1] = 255\n",
    "else:\n",
    "    result_data = np.stack((0*resultim, 0*resultim, 0*resultim), axis=2)\n",
    "resultdisplay.put_image_data(result_data, 0, 0)\n",
    "canvas.on_mouse_down(on_mouse_down)\n",
    "canvas.on_mouse_move(on_mouse_move)\n",
    "canvas.on_mouse_up(on_mouse_up)\n",
    "picker = ColorPicker(description=\"Color:\", value=\"#ff0000\") #red\n",
    "# picker = ColorPicker(description=\"Color:\", value=\"#0000ff\") #blue\n",
    "# picker = ColorPicker(description=\"Color:\", value=\"#00ff00\") #green\n",
    "# picker = ColorPicker(description=\"Color:\", value=\"#ffff00\") #yellow\n",
    "### all currently supported color options. -> gives the possibility to label 4 different phases\n",
    "\n",
    "link((picker, \"value\"), (canvas, \"stroke_style\"))\n",
    "link((picker, \"value\"), (canvas, \"fill_style\"))\n",
    "link((slidealpha, \"value\"), (resultdisplay, \"global_alpha\"))\n",
    "\n",
    "HBox((Mcanvas,picker))\n",
    "# HBox((Mcanvas,)) #picker "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530c9b50-0aeb-4c56-bc1f-d13d18d1c770",
   "metadata": {},
   "source": [
    "#### adjust grayscale range of the display of the image by playing with the lines below\n",
    "has no effect on the data. If you mess up, there are some lines at beggining of the canvas cell or below to get the previous display back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "29f81dff-2aa0-4a93-950b-fca5a5c8144b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfs.display.plot_im_histogram(im8)\n",
    "# im8 = TS.current_im8 # uncomment this line to get the original back display (raw at original grayscale range)\n",
    "# im8 = tfs.display.adjust_image_contrast(im8,0,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1141eddf-c56f-4212-9d23-9bc6a3d5cfb2",
   "metadata": {},
   "source": [
    "#### update training set if labels are ok or clear the current canvas by re-running the cell above if not\n",
    "automatically updates the stored label image on the disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3400c108-f8af-48f0-947d-72c29eb5450d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# same as above\n",
    "\n",
    "label_set = canvas.get_image_data()\n",
    "\n",
    "test = TS.current_truth.copy()\n",
    "\n",
    "test[np.bitwise_and(label_set[:,:,0]>0,np.bitwise_xor(label_set[:,:,0]>0,label_set[:,:,1]>0))] = 1\n",
    "test[label_set[:,:,1]>0] = 2\n",
    "test[label_set[:,:,2]>0] = 4 #order of 4&3 flipped for legacy reasons (existing training labels)\n",
    "test[np.bitwise_and(label_set[:,:,0]>0,label_set[:,:,1]>0)] = 3\n",
    "\n",
    "TS.current_truth = test.copy()\n",
    "imageio.imsave(TS.current_truthpath, TS.current_truth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed185c4-d8c3-4e8d-bbbc-10d260e2c8ea",
   "metadata": {},
   "source": [
    "#### inspect labels and training progress\n",
    "can be sometimes useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ada177cb-fc0f-4ef9-9e1f-eeb177be55aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same as above\n",
    "\n",
    "fig, axes = plt.subplots(1,4, figsize=(20,10))\n",
    "axes[0].imshow(TS.current_result, 'gray')\n",
    "axes[0].set_title('current result')\n",
    "axes[1].imshow(TS.current_im8, 'gray')\n",
    "axes[1].set_title('original grayscale')\n",
    "\n",
    "# TS.current_diff_im = TS.current_im-TS.current_first_im\n",
    "# TS.current_diff_im = TS.current_diff_im/TS.current_diff_im.max()*255\n",
    "# axes[2].imshow(-TS.current_diff_im)#,vmin=6e4)\n",
    "# axes[3].imshow(im8old, 'gray')\n",
    "# axes[3].imshow(TS.current_first_im, 'gray')\n",
    "axes[2].imshow(TS.current_truth)\n",
    "axes[2].set_title('label image')\n",
    "if TS.current_computed:\n",
    "    axes[3].imshow(TS.current_feat_stack_full[:,:,i], 'gray')\n",
    "    axes[3].set_title(str(i)+': '+IF.combined_feature_names[i])\n",
    "else:\n",
    "    axes[3].imshow(TS.current_result, 'gray')\n",
    "    axes[3].set_title('current result')\n",
    "# for ax in axes:\n",
    "    # ax.set_xticks([])\n",
    "    # ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3836ae72-b55b-4187-9ed0-56cf99f4114c",
   "metadata": {},
   "source": [
    "#### train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8769b6d8-4e1a-454d-af12-cfde57083178",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training and classifying\n"
     ]
    }
   ],
   "source": [
    "TS.train_slice()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83df5eb3-2749-4135-8365-deb72495a507",
   "metadata": {},
   "source": [
    "#### revise feature importance to decide on omiting a few to make calculation more efficient\n",
    "TODO implementation to be done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ab3271f0-51bc-4975-8437-e329cf42b4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'importance')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO consider reduced feature stack\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.stem(np.array(IF.combined_feature_names)[feature_ids], TS.clf.feature_importances_,'x')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('importance') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4599c5b3-65c3-43fa-8f33-305c878a0dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(TS.training_dict, open(os.path.join(training_path, pytrain_git_sha+'_training_dict.p'),'wb'))\n",
    "pickle.dump(TS.combined_feature_names, open(os.path.join(training_path, pytrain_git_sha+'_feature_names.p'),'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "42b23ce5-75ef-43ce-bdaa-ed8372aef184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/das/home/fische_r/interlaces/Tomcat_2'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0335ac-cdac-40f5-84b6-780a2a25bffa",
   "metadata": {},
   "source": [
    "### Select features to keep to reduce feature stack\n",
    "throw away 50% of the features by using the median of the feature importance  \n",
    "the yarn sample showed no susceptible redcution of segmentation quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5ba898d6-eff1-4c10-9023-98ad4dcd59fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "importance_median = np.median(TS.clf.feature_importances_)\n",
    "feature_ids = TS.clf.feature_importances_>importance_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "afec8b5f-4326-4d7a-beb5-cce5adbef9b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_use = []\n",
    "for i in range(len(feature_ids)):\n",
    "    if feature_ids[i]:\n",
    "        features_to_use.append(IF.combined_feature_names[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "01440b49-4789-412d-b25a-543df4ffd30f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "093c73c\n"
     ]
    }
   ],
   "source": [
    "# TODO write txt instead of pickle dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "23eef3c6-d786-4f8b-a222-c47c98d099e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gaussian_4D_Blur_0.0',\n",
       " 'Gaussian_4D_Blur_1.0',\n",
       " 'Gaussian_4D_Blur_6.0',\n",
       " 'diff_of_gauss_4D_6.0_0.0',\n",
       " 'diff_of_gauss_4D_6.0_1.0',\n",
       " 'Gradient_sigma_1.0_0',\n",
       " 'Gradient_sigma_1.0_1',\n",
       " 'Gradient_sigma_1.0_3',\n",
       " 'hessian_sigma_1.0_00',\n",
       " 'hessian_sigma_1.0_01',\n",
       " 'hessian_sigma_1.0_11',\n",
       " 'Gradient_sigma_3.0_3',\n",
       " 'hessian_sigma_3.0_00',\n",
       " 'hessian_sigma_3.0_01',\n",
       " 'hessian_sigma_3.0_02',\n",
       " 'hessian_sigma_3.0_03',\n",
       " 'hessian_sigma_3.0_11',\n",
       " 'hessian_sigma_3.0_33',\n",
       " 'Gradient_sigma_6.0_0',\n",
       " 'Gradient_sigma_6.0_1',\n",
       " 'Gradient_sigma_6.0_2',\n",
       " 'Gradient_sigma_6.0_3',\n",
       " 'hessian_sigma_6.0_01',\n",
       " 'hessian_sigma_6.0_03',\n",
       " 'hessian_sigma_6.0_11',\n",
       " 'hessian_sigma_6.0_12',\n",
       " 'hessian_sigma_6.0_13',\n",
       " 'hessian_sigma_6.0_22',\n",
       " 'hessian_sigma_6.0_23',\n",
       " 'hessian_sigma_6.0_33',\n",
       " 'Gradient_sigma_2.0_0',\n",
       " 'Gradient_sigma_2.0_3',\n",
       " 'hessian_sigma_2.0_00',\n",
       " 'hessian_sigma_2.0_01',\n",
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       " 'hessian_sigma_2.0_13',\n",
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       " 'Gaussian_time_1.0',\n",
       " 'Gaussian_time_6.0',\n",
       " 'Gaussian_time_2.0',\n",
       " 'Gaussian_space_0.0',\n",
       " 'Gaussian_space_6.0',\n",
       " 'diff_of_gauss_space_3.0_0.0',\n",
       " 'diff_of_gauss_space_6.0_0.0',\n",
       " 'diff_of_gauss_space_2.0_0.0',\n",
       " 'diff_of_gauss_space_3.0_1.0',\n",
       " 'diff_of_gauss_space_6.0_1.0',\n",
       " 'diff_of_gauss_space_2.0_1.0',\n",
       " 'diff_of_gauss_space_2.0_3.0',\n",
       " 'diff_temp_min_Gauss_2.0',\n",
       " 'diff_to_first_',\n",
       " 'full_temp_min_Gauss_2.0',\n",
       " 'first_',\n",
       " 'last_']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_use"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9de65d-3c14-4021-820b-32b52bed3318",
   "metadata": {},
   "source": [
    "#### Save classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c1eabc86-44be-4221-b5d0-f5da6190924d",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = TS.clf\n",
    "pickle.dump(clf, open(os.path.join(training_path, pytrain_git_sha+'_clf.p'),'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb46cb8d-ed6e-47af-bd16-aa3f57301c42",
   "metadata": {},
   "source": [
    "## Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdafb7f4-2740-4633-b0ab-b5e75f823bfe",
   "metadata": {},
   "source": [
    "### load classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "602d6693-5ce4-42c0-aba6-f6fe28154883",
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = 'e765007'\n",
    "clf = pickle.load(open(os.path.join(training_path, prefix+'_clf.p'),'rb'))\n",
    "# clf.n_jobs = 40 #threads to be used for classifier. more is faster but needs more RAM, default is all available, tune if you get memory issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a65dc42-b9de-4373-983f-3dc45460e3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat = TS.feat_data['feature_stack']\n",
    "feat_idp = TS.feat_data['feature_stack_time_independent']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3eb4b7-301e-4242-be57-c8ecfcec5ed3",
   "metadata": {},
   "source": [
    "### figure out good dimensions for peacewise segmentation\n",
    "the chunks contain the entire time series --> makes no sense to split in time  \n",
    "take the two biggest dimensions an don't split along the smallest spatial dimension --> saves one loop if you get away with it  \n",
    "TODO: automate somehow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6fb7c8a8-4013-46e1-a286-1577647bb207",
   "metadata": {},
   "outputs": [],
   "source": [
    "def round_up(val, dec=1):\n",
    "    rval = np.round(val, dec)\n",
    "    if rval < val:\n",
    "        rval = rval+10**(-dec)\n",
    "    rval = np.round(rval, dec) # to get rid of floating point uncertainties\n",
    "    return rval\n",
    "\n",
    "def calculate_part(part):\n",
    "    if type(part) is not np.ndarray:\n",
    "        fut = client.scatter(part)\n",
    "        fut = fut.result()\n",
    "        fut = fut.compute()\n",
    "        part = fut\n",
    "    return part\n",
    "\n",
    "def get_the_client_back(client):\n",
    "    try:\n",
    "        client.restart()\n",
    "    except:\n",
    "        client = reboot_client(client)\n",
    "    if not len(client.cluster.workers)>1:   \n",
    "        client = reboot_client(client)\n",
    "    return client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05450ff5-ca88-4894-a202-cdcf47cf8979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 20\n"
     ]
    },
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       "dask.array&lt;getitem, shape=(36, 330, 105, 100, 52), dtype=float64, chunksize=(28, 36, 36, 100, 1), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
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       "  * y        (y) int64 3kB 0 1 2 3 4 5 6 7 8 ... 322 323 324 325 326 327 328 329\n",
       "  * z        (z) int64 840B 1890 1891 1892 1893 1894 ... 1991 1992 1993 1994\n",
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       "  * feature  (feature) &lt;U27 6kB &#x27;Gaussian_4D_Blur_0.0&#x27; ... &#x27;diff_to_first_&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'feature_stack'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 36</li><li><span class='xr-has-index'>y</span>: 330</li><li><span class='xr-has-index'>z</span>: 105</li><li><span class='xr-has-index'>time</span>: 100</li><li><span class='xr-has-index'>feature</span>: 52</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a7208446-0d93-4dbe-89d1-a35a707e56bf' class='xr-array-in' type='checkbox' checked><label for='section-a7208446-0d93-4dbe-89d1-a35a707e56bf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(28, 36, 18, 100, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 48.33 GiB </td>\n",
       "                        <td> 27.69 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (36, 330, 105, 100, 52) </td>\n",
       "                        <td> (28, 36, 36, 100, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4992 chunks in 442 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-3c665df3-320a-4a4a-9f79-d8f4d4bcb488' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c665df3-320a-4a4a-9f79-d8f4d4bcb488' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>648 649 650 651 ... 680 681 682 683</div><input id='attrs-d7ccba69-13c7-47e2-a968-2237af6c4e8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7ccba69-13c7-47e2-a968-2237af6c4e8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28530fd6-6ae1-4bc5-b6a9-cf70403037ec' class='xr-var-data-in' type='checkbox'><label for='data-28530fd6-6ae1-4bc5-b6a9-cf70403037ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661,\n",
       "       662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,\n",
       "       676, 677, 678, 679, 680, 681, 682, 683])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 325 326 327 328 329</div><input id='attrs-d0a35900-c85d-417f-8588-4134ed6c7f43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0a35900-c85d-417f-8588-4134ed6c7f43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07fffacf-f611-4c32-b664-eeb308f124c8' class='xr-var-data-in' type='checkbox'><label for='data-07fffacf-f611-4c32-b664-eeb308f124c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 327, 328, 329], shape=(330,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1890 1891 1892 ... 1992 1993 1994</div><input id='attrs-36235dd6-6381-4626-9287-ffc611a69110' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36235dd6-6381-4626-9287-ffc611a69110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-426977c0-89f9-4f35-b765-112eda98d4ae' class='xr-var-data-in' type='checkbox'><label for='data-426977c0-89f9-4f35-b765-112eda98d4ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901,\n",
       "       1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913,\n",
       "       1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925,\n",
       "       1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n",
       "       1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949,\n",
       "       1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 94 95 96 97 98 99</div><input id='attrs-13e1d0d9-09c2-43a2-b519-003afae19e2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13e1d0d9-09c2-43a2-b519-003afae19e2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9440ef7-c490-4398-a47a-d26b480f8b83' class='xr-var-data-in' type='checkbox'><label for='data-d9440ef7-c490-4398-a47a-d26b480f8b83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>feature</span></div><div class='xr-var-dims'>(feature)</div><div class='xr-var-dtype'>&lt;U27</div><div class='xr-var-preview xr-preview'>&#x27;Gaussian_4D_Blur_0.0&#x27; ... &#x27;diff...</div><input id='attrs-d543716b-5985-437a-b48f-6c44ba82e77f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d543716b-5985-437a-b48f-6c44ba82e77f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc2e95ac-4350-477c-ad7d-3dee182fef76' class='xr-var-data-in' type='checkbox'><label for='data-dc2e95ac-4350-477c-ad7d-3dee182fef76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Gaussian_4D_Blur_0.0&#x27;, &#x27;Gaussian_4D_Blur_1.0&#x27;, &#x27;Gaussian_4D_Blur_6.0&#x27;,\n",
       "       &#x27;diff_of_gauss_4D_6.0_0.0&#x27;, &#x27;diff_of_gauss_4D_6.0_1.0&#x27;,\n",
       "       &#x27;Gradient_sigma_1.0_0&#x27;, &#x27;Gradient_sigma_1.0_1&#x27;, &#x27;Gradient_sigma_1.0_3&#x27;,\n",
       "       &#x27;hessian_sigma_1.0_00&#x27;, &#x27;hessian_sigma_1.0_01&#x27;, &#x27;hessian_sigma_1.0_11&#x27;,\n",
       "       &#x27;Gradient_sigma_3.0_3&#x27;, &#x27;hessian_sigma_3.0_00&#x27;, &#x27;hessian_sigma_3.0_01&#x27;,\n",
       "       &#x27;hessian_sigma_3.0_02&#x27;, &#x27;hessian_sigma_3.0_03&#x27;, &#x27;hessian_sigma_3.0_11&#x27;,\n",
       "       &#x27;hessian_sigma_3.0_33&#x27;, &#x27;Gradient_sigma_6.0_0&#x27;, &#x27;Gradient_sigma_6.0_1&#x27;,\n",
       "       &#x27;Gradient_sigma_6.0_2&#x27;, &#x27;Gradient_sigma_6.0_3&#x27;, &#x27;hessian_sigma_6.0_01&#x27;,\n",
       "       &#x27;hessian_sigma_6.0_03&#x27;, &#x27;hessian_sigma_6.0_11&#x27;, &#x27;hessian_sigma_6.0_12&#x27;,\n",
       "       &#x27;hessian_sigma_6.0_13&#x27;, &#x27;hessian_sigma_6.0_22&#x27;, &#x27;hessian_sigma_6.0_23&#x27;,\n",
       "       &#x27;hessian_sigma_6.0_33&#x27;, &#x27;Gradient_sigma_2.0_0&#x27;, &#x27;Gradient_sigma_2.0_3&#x27;,\n",
       "       &#x27;hessian_sigma_2.0_00&#x27;, &#x27;hessian_sigma_2.0_01&#x27;, &#x27;hessian_sigma_2.0_02&#x27;,\n",
       "       &#x27;hessian_sigma_2.0_11&#x27;, &#x27;hessian_sigma_2.0_13&#x27;, &#x27;Gaussian_time_0.0&#x27;,\n",
       "       &#x27;Gaussian_time_1.0&#x27;, &#x27;Gaussian_time_6.0&#x27;, &#x27;Gaussian_time_2.0&#x27;,\n",
       "       &#x27;Gaussian_space_0.0&#x27;, &#x27;Gaussian_space_6.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_3.0_0.0&#x27;, &#x27;diff_of_gauss_space_6.0_0.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_2.0_0.0&#x27;, &#x27;diff_of_gauss_space_3.0_1.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_6.0_1.0&#x27;, &#x27;diff_of_gauss_space_2.0_1.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_2.0_3.0&#x27;, &#x27;diff_temp_min_Gauss_2.0&#x27;,\n",
       "       &#x27;diff_to_first_&#x27;], dtype=&#x27;&lt;U27&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c218801a-6490-4095-81c0-7593a8bc7eb3' class='xr-section-summary-in' type='checkbox'  ><label for='section-c218801a-6490-4095-81c0-7593a8bc7eb3' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-407b5faf-fdc6-439c-870c-1c850d5d3031' class='xr-index-data-in' type='checkbox'/><label for='index-407b5faf-fdc6-439c-870c-1c850d5d3031' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661,\n",
       "       662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,\n",
       "       676, 677, 678, 679, 680, 681, 682, 683],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4a3c369d-cde9-4fc3-a5df-c60f9d4850f9' class='xr-index-data-in' type='checkbox'/><label for='index-4a3c369d-cde9-4fc3-a5df-c60f9d4850f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       320, 321, 322, 323, 324, 325, 326, 327, 328, 329],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;y&#x27;, length=330))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1088bc99-9497-4704-8abe-ad01a5e60a8c' class='xr-index-data-in' type='checkbox'/><label for='index-1088bc99-9497-4704-8abe-ad01a5e60a8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899,\n",
       "       ...\n",
       "       1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;z&#x27;, length=105))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e3b279b4-d925-4f7d-955e-0d46420a8c33' class='xr-index-data-in' type='checkbox'/><label for='index-e3b279b4-d925-4f7d-955e-0d46420a8c33' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>feature</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-39965edf-7d01-4e8d-b413-9f1168ce301d' class='xr-index-data-in' type='checkbox'/><label for='index-39965edf-7d01-4e8d-b413-9f1168ce301d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Gaussian_4D_Blur_0.0&#x27;, &#x27;Gaussian_4D_Blur_1.0&#x27;, &#x27;Gaussian_4D_Blur_6.0&#x27;,\n",
       "       &#x27;diff_of_gauss_4D_6.0_0.0&#x27;, &#x27;diff_of_gauss_4D_6.0_1.0&#x27;,\n",
       "       &#x27;Gradient_sigma_1.0_0&#x27;, &#x27;Gradient_sigma_1.0_1&#x27;, &#x27;Gradient_sigma_1.0_3&#x27;,\n",
       "       &#x27;hessian_sigma_1.0_00&#x27;, &#x27;hessian_sigma_1.0_01&#x27;, &#x27;hessian_sigma_1.0_11&#x27;,\n",
       "       &#x27;Gradient_sigma_3.0_3&#x27;, &#x27;hessian_sigma_3.0_00&#x27;, &#x27;hessian_sigma_3.0_01&#x27;,\n",
       "       &#x27;hessian_sigma_3.0_02&#x27;, &#x27;hessian_sigma_3.0_03&#x27;, &#x27;hessian_sigma_3.0_11&#x27;,\n",
       "       &#x27;hessian_sigma_3.0_33&#x27;, &#x27;Gradient_sigma_6.0_0&#x27;, &#x27;Gradient_sigma_6.0_1&#x27;,\n",
       "       &#x27;Gradient_sigma_6.0_2&#x27;, &#x27;Gradient_sigma_6.0_3&#x27;, &#x27;hessian_sigma_6.0_01&#x27;,\n",
       "       &#x27;hessian_sigma_6.0_03&#x27;, &#x27;hessian_sigma_6.0_11&#x27;, &#x27;hessian_sigma_6.0_12&#x27;,\n",
       "       &#x27;hessian_sigma_6.0_13&#x27;, &#x27;hessian_sigma_6.0_22&#x27;, &#x27;hessian_sigma_6.0_23&#x27;,\n",
       "       &#x27;hessian_sigma_6.0_33&#x27;, &#x27;Gradient_sigma_2.0_0&#x27;, &#x27;Gradient_sigma_2.0_3&#x27;,\n",
       "       &#x27;hessian_sigma_2.0_00&#x27;, &#x27;hessian_sigma_2.0_01&#x27;, &#x27;hessian_sigma_2.0_02&#x27;,\n",
       "       &#x27;hessian_sigma_2.0_11&#x27;, &#x27;hessian_sigma_2.0_13&#x27;, &#x27;Gaussian_time_0.0&#x27;,\n",
       "       &#x27;Gaussian_time_1.0&#x27;, &#x27;Gaussian_time_6.0&#x27;, &#x27;Gaussian_time_2.0&#x27;,\n",
       "       &#x27;Gaussian_space_0.0&#x27;, &#x27;Gaussian_space_6.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_3.0_0.0&#x27;, &#x27;diff_of_gauss_space_6.0_0.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_2.0_0.0&#x27;, &#x27;diff_of_gauss_space_3.0_1.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_6.0_1.0&#x27;, &#x27;diff_of_gauss_space_2.0_1.0&#x27;,\n",
       "       &#x27;diff_of_gauss_space_2.0_3.0&#x27;, &#x27;diff_temp_min_Gauss_2.0&#x27;,\n",
       "       &#x27;diff_to_first_&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;feature&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebfdc0f1-28d2-4a89-9dd2-4f9c5de5ac42' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebfdc0f1-28d2-4a89-9dd2-4f9c5de5ac42' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'feature_stack' (x: 36, y: 330, z: 105, time: 100, feature: 52)> Size: 52GB\n",
       "dask.array<getitem, shape=(36, 330, 105, 100, 52), dtype=float64, chunksize=(28, 36, 36, 100, 1), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * x        (x) int64 288B 648 649 650 651 652 653 ... 678 679 680 681 682 683\n",
       "  * y        (y) int64 3kB 0 1 2 3 4 5 6 7 8 ... 322 323 324 325 326 327 328 329\n",
       "  * z        (z) int64 840B 1890 1891 1892 1893 1894 ... 1991 1992 1993 1994\n",
       "  * time     (time) int64 800B 0 1 2 3 4 5 6 7 8 ... 91 92 93 94 95 96 97 98 99\n",
       "  * feature  (feature) <U27 6kB 'Gaussian_4D_Blur_0.0' ... 'diff_to_first_'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim1 = 36#better use multiple of chunk size !?  <-- tune this parameter to minimize imax, jmax and the size of the result\n",
    "\n",
    "# shp = feat.shape[:-1]\n",
    "shp = shp_raw\n",
    "\n",
    "# aspect ratio \n",
    "dimsize = np.sort(shp[:-1] )\n",
    "aspect = round_up(dimsize[-1]/dimsize[-2])\n",
    "\n",
    "# check length of loops to process entire dataset, estimate size of obtained sub-feature to avoid out-of-memory issues\n",
    "\n",
    "dim2 = int(round_up(aspect*dim1, 0))\n",
    "jmax = int(round_up(dimsize[-2]/dim1, 0))\n",
    "imax = int(round_up(dimsize[-1]/dim2, 0))\n",
    "\n",
    "i = imax -2\n",
    "j = jmax -2\n",
    "print(imax,jmax)\n",
    "feat[i*dim1:(i+1)*dim1,:,j*dim2:(j+1)*dim2,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0699380a-be7f-4a1d-ba4b-ce8c7b410957",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# segs = np.zeros(feat.shape[:4], dtype=np.uint8)\n",
    "# segs = pickle.load(open(os.path.join(training_path, 'segs.p'),'rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "19500c3c-2fdc-4504-ab26-09b3765dcd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 17\n"
     ]
    }
   ],
   "source": [
    "piecepath = os.path.join(os.path.join(temppath, 'segmentation_pieces'))\n",
    "\n",
    "restart_i = 0\n",
    "restart_j = 0  #replace with the iterations you coudl reach before dask crashed\n",
    "# get from the written files the restart coordinates\n",
    "if os.path.exists(piecepath):\n",
    "    ij_mat = np.zeros((imax,jmax), dtype=bool)\n",
    "    for filename in os.listdir(piecepath):\n",
    "        i = int(filename.split('_')[2])\n",
    "        j = int(filename.split('_')[4])\n",
    "        ij_mat[i,j] = True\n",
    "        restart_i = int(np.min(np.argmin(ij_mat, axis=0)))\n",
    "        restart_j = int(np.max(np.argmin(ij_mat, axis=1)))\n",
    "\n",
    "print(restart_i, restart_j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "558c2163-a274-450c-9007-25cf7bd77945",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/20\n",
      "17\n",
      "18\n",
      "19\n",
      "2/20\n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# restart_i = 0\n",
    "# restart_j = 14\n",
    "\n",
    "# elapsed walltime: subprocess.check_output(['squeue','-u', 'fische_r']).decode().strip().split(' ')[-8]\n",
    "\n",
    "# piecepath = os.path.join(os.path.join(temppath, 'segmentation_pieces'))\n",
    "if not os.path.exists(piecepath):\n",
    "    os.mkdir(piecepath)\n",
    "\n",
    "for i in range(restart_i,imax):\n",
    "    print(str(i+1)+'/'+str(imax))\n",
    "    start_j = 0\n",
    "    if i == restart_i:\n",
    "        start_j = restart_j\n",
    "    for j in range(start_j,jmax):\n",
    "        print(j)\n",
    "        part = feat[i*dim1:(i+1)*dim1,:,j*dim2:(j+1)*dim2,:,:] \n",
    "        part_idp = feat_idp[i*dim1:(i+1)*dim1,:,j*dim2:(j+1)*dim2,:]  \n",
    "        if 0 in part.shape:\n",
    "            print('hit the edge (one dimension 0), ignore')\n",
    "            continue\n",
    "        part = calculate_part(part)\n",
    "        client = get_the_client_back(client)\n",
    "        part_idp = calculate_part(part_idp)\n",
    "        client = get_the_client_back(client)\n",
    "        \n",
    "        part_idp = np.stack([part_idp]*shp[-1], axis=-2)[:,:,:,0,:,:] #expand in time, a bit ugly, could maybe more elegant\n",
    "        part = np.concatenate([part, part_idp], axis = -1)\n",
    "        del part_idp # drop the time independent part, is this garbage collected?\n",
    "\n",
    "        shp_orig = part.shape\n",
    "        num_feat = part.shape[-1]  \n",
    "        part = part.reshape(-1,num_feat)\n",
    "\n",
    "        seg = clf.predict(part).astype(np.uint8)\n",
    "\n",
    "        # put segs together when all calculated\n",
    "        seg = seg.reshape(shp_orig[:4])\n",
    "\n",
    "        pickle.dump(seg, open(os.path.join(piecepath, 'seg_i_'+str(i)+'_j_'+str(j)+'_.p'), 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb91dff-a5a0-42ac-ba88-52422e9b187c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('You got until i',i,'& j',j)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4b7e8f-8b4f-48db-9f87-d83304091244",
   "metadata": {},
   "source": [
    "### piece segmentation back together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ede4145-bbab-493a-b5ae-fb8ca981543a",
   "metadata": {},
   "outputs": [],
   "source": [
    "segs = np.zeros(shp_raw, dtype=np.uint8)\n",
    "\n",
    "for filename in os.listdir(piecepath):\n",
    "    i = int(filename.split('_')[2])\n",
    "    j = int(filename.split('_')[4])\n",
    "    ### not sure if this switch cases are necessary\n",
    "    seg = pickle.load(open(os.path.join(piecepath, filename),'rb'))\n",
    "    if i < imax-1 and j < jmax-1:\n",
    "        segs[i*dim1:(i+1)*dim1,:,j*dim2:(j+1)*dim2,:] = seg\n",
    "    elif not i < imax-1 and j < jmax-1:\n",
    "        segs[i*dim1:,:,j*dim2:(j+1)*dim2,:] =  seg\n",
    "    elif not j < jmax-1 and i < imax-1:\n",
    "        segs[i*dim1:(i+1)*dim1,:,j*dim2:,:] =  seg\n",
    "    else:\n",
    "        segs[i*dim1:,:,j*dim2:,:] = seg\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f17dd106-e0be-46f9-96e0-4a2daecdbac5",
   "metadata": {},
   "source": [
    "### save result to disk when full volume has been processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8632bff7-e96f-4011-abde-e804e1245d1c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TODO: include metadata in segmented nc\n",
    "\n",
    "shp = segs.shape\n",
    "segdata = xr.Dataset({'segmented': (['x','y','z','timestep'], segs),\n",
    "                     't_utc': ('timestep', t_utc),\n",
    "                     'time': ('timestep', time)},\n",
    "                               coords = {'x': np.arange(shp[0]),\n",
    "                               'y': np.arange(shp[1]),\n",
    "                               'z': np.arange(shp[2]),\n",
    "                               'timestep': np.arange(shp[3]),\n",
    "                               'feature': TS.combined_feature_names}\n",
    "                     )\n",
    "segdata.attrs = data.attrs.copy()\n",
    "segdata.attrs['pytrain_git'] = pytrain_git_sha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37888c8-a425-4338-9bb4-e79c5f1b82e8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "segpath = os.path.join(training_path_sample, sample+'_segmentation.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b10814b-a909-4aa4-ac9f-6267b1a662bf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "segdata.to_netcdf(segpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d68ce6a-96d1-44d0-95f9-673e858c9509",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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