Update Analysis and Plots notebooks

Author: Lukas Schrangl

notebooks/02. Analysis.ipynb

@@ -11,8 +11,7 @@
    "outputs": [],
    "source": [
     "%matplotlib widget\n",
-    "import sys\n",
-    "import os\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "plt.ioff()\n",
     "import numpy as np\n",
@@ -30,9 +29,9 @@
      "output_type": "stream",
      "text": [
       "smFRET analysis software version 2.1\n",
-      "(git revision 2.1-76-gd791ef7)\n",
-      "Output version 12\n",
-      "Using sdt-python version 14.4\n"
+      "(git revision 2.1-87-ga66b93a)\n",
+      "Output version 13\n",
+      "Using sdt-python version 15.2\n"
      ]
     }
    ],
@@ -47,10 +46,7 @@
    "outputs": [],
    "source": [
     "# Create an Analyzer instance. This will load the tracking data.\n",
-    "ana = Analyzer()\n",
-    "# Add a (\"fret\", \"exc_type\") column declaring the excitation type (donor or\n",
-    "# acceptor) for each localization.\n",
-    "ana.flag_excitation_type()"
+    "ana = Analyzer(\"tracking-split\")"
    ]
   },
   {
@@ -100,12 +96,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b78aa53144a04e399b3a05cb841a045e",
+       "model_id": "828de3b2d9ae4638881111479aec9652",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(Dropdown(description='channel', options=('acceptor', 'donor'), value='acceptor'), BoundedIntTex…"
+       "VBox(children=(Dropdown(description='channel', options=('donor', 'acceptor'), value='donor'), BoundedIntText(v…"
       ]
      },
      "metadata": {},
@@ -180,7 +176,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b5b3f0148c874113b3b7bb73e0cf6e03",
+       "model_id": "1c97a41d7f64490faf769dd842633e81",
        "version_major": 2,
        "version_minor": 0
       },
@@ -237,7 +233,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e57740ca6ea34ab4a2ce00f7bac026e8",
+       "model_id": "f8540c53327d4b64996de2dc5159a7c0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -336,12 +332,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "485d7207aba54ca8aab6982fc5e7ed4c",
+       "model_id": "7a57bc95af7447e78a31a8df4af93dcc",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Thresholder(children=(Dropdown(description='image', options=('DPPC_ctrl-J4/cells-01_000_.tif', 'DPPC_ctrl-J4/c…"
+       "Thresholder(children=(ImageSelector(children=(Dropdown(description='image', options=(('DPPC_ctrl-J4/cells-01_0…"
       ]
      },
      "metadata": {},
@@ -355,22 +351,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Threshold cell images and select only data within cell-occupied areas\n",
-    "ana.apply_cell_masks(\"adaptive\", block_size=65, c=-2, smooth=3, method=\"mean\")\n",
+    "ana.apply_cell_masks([k for k in ana.analyzers if not k.endswith(\"no-cells\")],\n",
+    "                     \"adaptive\", block_size=65, c=-2, smooth=3, method=\"mean\")\n",
     "\n",
     "if plot_keys:\n",
     "    dp.dscatter(\"Underneath cells\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lukas/Software/fret-analysis/smfret_analysis/analyzer.py:860: PerformanceWarning: \n",
+      "your performance may suffer as PyTables will pickle object types that it cannot\n",
+      "map directly to c-types [inferred_type->mixed,key->axis1_level0] [items->None]\n",
+      "\n",
+      "  {(\"fret\", \"exc_type\"): str}))\n"
+     ]
+    }
+   ],
    "source": [
     "# Save results to disk (typically \"filtered-v???.h5\")\n",
     "ana.save()"
@@ -401,7 +410,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.8"
   },
   "nbpresent": {
    "slides": {