diff --git a/config_parameters.yml b/config_parameters.yml
index fa19653d3c8dcec836c67ee0a495ac83db81603d..cf70dc2e5c4b0bfcccff29c1221fa3ce43a75739 100644
--- a/config_parameters.yml
+++ b/config_parameters.yml
@@ -5,5 +5,8 @@ id: CLEM_and_morphological_characterization_7836
date: '2024-07-01'
ppms: https://ppms.eu/pasteur/vproj/?pf=20&projectid=7836
files:
- - notebooks/fig2_t3ss_geometry.ipynb
+ - notebooks/t3ss_geometry.ipynb
+ - notebooks/substack_extraction.ipynb
+ - notebooks/membrain_seg.ipynb
+ - notebooks/membrane_processing.ipynb
sphinx_theme: null # list of built-in themes is here: https://www.sphinx-doc.org/en/master/usage/theming.html
\ No newline at end of file
diff --git a/notebooks/fig2_t3ss_geometry.ipynb b/notebooks/fig2_t3ss_geometry.ipynb
deleted file mode 100644
index 830efc766698879108b3c63ea0c5d190172a2f80..0000000000000000000000000000000000000000
--- a/notebooks/fig2_t3ss_geometry.ipynb
+++ /dev/null
@@ -1,1323 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Fig. 2: T3SS geometry\n",
- "\n",
- "Measurements in nm and degrees\n",
- "1) distance 1<->2\n",
- "2) distance 2<->3 (only if 3 exists within the same object)\n",
- "3) angle between 21 and 23\n",
- "4) for every 3 the closest membrane break point\n",
- "\n",
- "5) distances between all 2\n",
- "\n",
- "Perform measurements for\n",
- "- each dataset\n",
- "- each needle\n",
- "\n",
- "Output is a table containing\n",
- "- dataset\n",
- "- needle number\n",
- "- measurements\n",
- "\n",
- "Use this notebook with a conda env:\n",
- "\n",
- "- `conda create -n t3ss_geo python=3.10`\n",
- "- `conda activate t3ss_geo`\n",
- "- `pip install mrcfile pandas imodmodel ipython jupyter matplotlib seaborn ipympl scipy xarray`\n",
- "\n",
- "\n",
- "20231031 todo\n",
- "- methods section\n",
- "- distances between 2\n",
- "\n",
- "- video z plane\n",
- " - bacteria cyan\n",
- " - galectin magenta\n",
- " - actin yellow\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import imodmodel\n",
- "import mrcfile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "base_dir = '/Volumes/Eirene/Points/Points_corrected'\n",
- "ds_dirs = [d for d in os.listdir(base_dir) if d.startswith('0')]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [],
- "source": [
- "# output_dir = os.path.join(base_dir, 'Marvin', 'points_measurements')\n",
- "output_dir = os.path.join('.', 'points_measurements')\n",
- "\n",
- "if not os.path.exists(output_dir):\n",
- " os.makedirs(output_dir)\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No T3SS model found for 057B30G2_TS_26_bin2_tiltcor_rec_corrected.mrc\n",
- "No T3SS model found for 057B30G2_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 057B30G2_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
- "No T3SS model found for 057B30G2_TS_29_bin2_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 057B30G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
- "No T3SS model found for 054B36G1_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 054B36G1_TS_03_bin2_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 054B36G1_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
- "No T3SS model found for 054B36G1_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 053B40G2_TS_22_bin3_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 053B40G2_TS_08_bin3_tiltcor_rec_corrected.mrc\n",
- "No breaks model found for 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>object_id</th>\n",
- " <th>contour_id</th>\n",
- " <th>x</th>\n",
- " <th>y</th>\n",
- " <th>z</th>\n",
- " <th>source_fn</th>\n",
- " <th>type</th>\n",
- " <th>tomo_id</th>\n",
- " <th>tomo_fn</th>\n",
- " <th>ds</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>430.220528</td>\n",
- " <td>1314.873257</td>\n",
- " <td>108.992062</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>16</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
- " <td>057B30G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>410.232665</td>\n",
- " <td>1334.406249</td>\n",
- " <td>117.338149</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>16</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
- " <td>057B30G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>560.002364</td>\n",
- " <td>1406.025222</td>\n",
- " <td>99.931734</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>16</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
- " <td>057B30G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>551.808454</td>\n",
- " <td>1429.227718</td>\n",
- " <td>113.049751</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>16</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
- " <td>057B30G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>2</td>\n",
- " <td>0</td>\n",
- " <td>409.335017</td>\n",
- " <td>760.494224</td>\n",
- " <td>128.397068</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>16</td>\n",
- " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
- " <td>057B30G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>9</td>\n",
- " <td>863.843156</td>\n",
- " <td>1125.199956</td>\n",
- " <td>27.004798</td>\n",
- " <td>053B40G2_TS_18_bin3_tiltcor_break.mod</td>\n",
- " <td>break</td>\n",
- " <td>18</td>\n",
- " <td>053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc</td>\n",
- " <td>053B40G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>10</td>\n",
- " <td>856.827783</td>\n",
- " <td>1128.298310</td>\n",
- " <td>28.606712</td>\n",
- " <td>053B40G2_TS_18_bin3_tiltcor_break.mod</td>\n",
- " <td>break</td>\n",
- " <td>18</td>\n",
- " <td>053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc</td>\n",
- " <td>053B40G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>678.462245</td>\n",
- " <td>343.503309</td>\n",
- " <td>100.492182</td>\n",
- " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>4</td>\n",
- " <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
- " <td>053B40G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>665.284720</td>\n",
- " <td>334.768706</td>\n",
- " <td>86.524922</td>\n",
- " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>4</td>\n",
- " <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
- " <td>053B40G2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>614.406954</td>\n",
- " <td>302.454664</td>\n",
- " <td>87.218406</td>\n",
- " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
- " <td>T3SS</td>\n",
- " <td>4</td>\n",
- " <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
- " <td>053B40G2</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>299 rows × 10 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " object_id contour_id x y z \\\n",
- "0 0 0 430.220528 1314.873257 108.992062 \n",
- "0 0 1 410.232665 1334.406249 117.338149 \n",
- "0 1 0 560.002364 1406.025222 99.931734 \n",
- "0 1 1 551.808454 1429.227718 113.049751 \n",
- "0 2 0 409.335017 760.494224 128.397068 \n",
- ".. ... ... ... ... ... \n",
- "0 0 9 863.843156 1125.199956 27.004798 \n",
- "0 0 10 856.827783 1128.298310 28.606712 \n",
- "0 0 0 678.462245 343.503309 100.492182 \n",
- "0 0 1 665.284720 334.768706 86.524922 \n",
- "0 0 2 614.406954 302.454664 87.218406 \n",
- "\n",
- " source_fn type tomo_id \\\n",
- "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
- ".. ... ... ... \n",
- "0 053B40G2_TS_18_bin3_tiltcor_break.mod break 18 \n",
- "0 053B40G2_TS_18_bin3_tiltcor_break.mod break 18 \n",
- "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod T3SS 4 \n",
- "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod T3SS 4 \n",
- "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod T3SS 4 \n",
- "\n",
- " tomo_fn ds \n",
- "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
- "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
- ".. ... ... \n",
- "0 053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
- "0 053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
- "0 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
- "0 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
- "0 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
- "\n",
- "[299 rows x 10 columns]"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Extract info from files\n",
- "\n",
- "dfs = []\n",
- "\n",
- "def extract_ts_id_from_fn(fn):\n",
- " return int(fn.split('_')[2])\n",
- "\n",
- "log_msgs = []\n",
- "\n",
- "for ds_dir in ds_dirs[:]:\n",
- " ds_path = os.path.join(base_dir, ds_dir)\n",
- " fns = [fn for fn in os.listdir(ds_path) if fn.startswith(ds_dir) and fn.endswith('.mrc')]\n",
- " for fn in fns:\n",
- "\n",
- " root_name = fn.split('rec_corrected.mrc')[0]\n",
- "\n",
- " t3ss_name = root_name + 'T3SS.mod'\n",
- " t3ss_path = os.path.join(base_dir, ds_dir, t3ss_name)\n",
- "\n",
- " breaks_name = root_name + 'break.mod'\n",
- " breaks_path = os.path.join(base_dir, ds_dir, breaks_name)\n",
- "\n",
- " if not os.path.exists(t3ss_path):\n",
- " msg = 'No T3SS model found for {}'.format(fn)\n",
- " log_msgs.append(msg)\n",
- " print(msg)\n",
- " continue\n",
- "\n",
- " tdf = imodmodel.read(t3ss_path)\n",
- " tdf['source_fn'] = t3ss_name\n",
- " tdf['type'] = 'T3SS'\n",
- "\n",
- " if os.path.exists(breaks_path):\n",
- " bdf = imodmodel.read(breaks_path)\n",
- " bdf['source_fn'] = breaks_name\n",
- " bdf['type'] = 'break'\n",
- " cdf = pd.concat([tdf, bdf])\n",
- " else:\n",
- " cdf = tdf\n",
- " msg = 'No breaks model found for {}'.format(fn)\n",
- " log_msgs.append(msg)\n",
- " print(msg)\n",
- "\n",
- " cdf['tomo_id'] = extract_ts_id_from_fn(fn)\n",
- " cdf['tomo_fn'] = fn\n",
- " cdf['ds'] = ds_dir\n",
- "\n",
- " cdf['contour_id'] = cdf['contour_id'].astype(int)\n",
- " cdf['object_id'] = cdf['object_id'].astype(int)\n",
- "\n",
- " # multiply with voxel size and convert to nm\n",
- " voxel_size = mrcfile.mmap(os.path.join(base_dir, ds_dir, fn), mode='r+').voxel_size.x\n",
- " for dim in ['x', 'y', 'z']:\n",
- " cdf[dim] = cdf[dim] * voxel_size / 10\n",
- "\n",
- " dfs.append(cdf)\n",
- "\n",
- "df = pd.concat(dfs)\n",
- "df\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th>distance_1_2</th>\n",
- " <th>distance_2_3</th>\n",
- " <th>angle_21_23</th>\n",
- " <th>distance_to_closest_break</th>\n",
- " <th>closest_break_contour_id</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>ds</th>\n",
- " <th>tomo_id</th>\n",
- " <th>object_id</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th rowspan=\"14\" valign=\"top\">053B40G2</th>\n",
- " <th>4</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>21.095611</td>\n",
- " <td>60.276241</td>\n",
- " <td>137.868919</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"4\" valign=\"top\">5</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.502676</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>24.832307</td>\n",
- " <td>43.241020</td>\n",
- " <td>164.050163</td>\n",
- " <td>10.670890</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>30.172572</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <th>0</th>\n",
- " <td>31.087429</td>\n",
- " <td>26.944366</td>\n",
- " <td>164.258483</td>\n",
- " <td>603.879757</td>\n",
- " <td>9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>26.332217</td>\n",
- " <td>10.184926</td>\n",
- " <td>171.030794</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"4\" valign=\"top\">18</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.780962</td>\n",
- " <td>58.197185</td>\n",
- " <td>173.998510</td>\n",
- " <td>3.937672</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>29.499533</td>\n",
- " <td>49.947940</td>\n",
- " <td>173.269796</td>\n",
- " <td>3.776994</td>\n",
- " <td>9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>27.753017</td>\n",
- " <td>59.075236</td>\n",
- " <td>171.329462</td>\n",
- " <td>5.644370</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <th>0</th>\n",
- " <td>26.119723</td>\n",
- " <td>52.913860</td>\n",
- " <td>175.469543</td>\n",
- " <td>1040.640748</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"2\" valign=\"top\">20</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>27.564635</td>\n",
- " <td>51.528415</td>\n",
- " <td>171.741981</td>\n",
- " <td>5.901630</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>29.986906</td>\n",
- " <td>54.243535</td>\n",
- " <td>173.332289</td>\n",
- " <td>1091.114905</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"2\" valign=\"top\">22</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.095342</td>\n",
- " <td>65.611610</td>\n",
- " <td>167.552579</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>34.082424</td>\n",
- " <td>45.047355</td>\n",
- " <td>172.796835</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"21\" valign=\"top\">054B36G1</th>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>28.860724</td>\n",
- " <td>50.738282</td>\n",
- " <td>169.974732</td>\n",
- " <td>13.976631</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>32.918810</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"7\" valign=\"top\">7</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>28.186633</td>\n",
- " <td>28.223016</td>\n",
- " <td>154.695752</td>\n",
- " <td>129.037390</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>28.329459</td>\n",
- " <td>48.747746</td>\n",
- " <td>162.660341</td>\n",
- " <td>9.551492</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>30.429561</td>\n",
- " <td>47.480343</td>\n",
- " <td>160.759520</td>\n",
- " <td>23.269485</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <th>0</th>\n",
- " <td>31.647358</td>\n",
- " <td>45.451366</td>\n",
- " <td>166.210177</td>\n",
- " <td>7.781381</td>\n",
- " <td>7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <th>0</th>\n",
- " <td>26.638607</td>\n",
- " <td>48.631320</td>\n",
- " <td>164.303959</td>\n",
- " <td>16.315059</td>\n",
- " <td>46</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <th>0</th>\n",
- " <td>30.272416</td>\n",
- " <td>53.902126</td>\n",
- " <td>168.519110</td>\n",
- " <td>97.418625</td>\n",
- " <td>39</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <th>0</th>\n",
- " <td>65.285148</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"3\" valign=\"top\">11</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>48.298107</td>\n",
- " <td>55.133918</td>\n",
- " <td>102.926047</td>\n",
- " <td>15.566473</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>27.684032</td>\n",
- " <td>72.729038</td>\n",
- " <td>152.148465</td>\n",
- " <td>10.977328</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>30.399087</td>\n",
- " <td>46.203811</td>\n",
- " <td>156.695492</td>\n",
- " <td>188.565914</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"2\" valign=\"top\">13</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.523980</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>31.098125</td>\n",
- " <td>67.095690</td>\n",
- " <td>148.884104</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"7\" valign=\"top\">14</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>32.563100</td>\n",
- " <td>41.258266</td>\n",
- " <td>168.134535</td>\n",
- " <td>152.079839</td>\n",
- " <td>7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>28.940105</td>\n",
- " <td>27.522238</td>\n",
- " <td>170.971118</td>\n",
- " <td>115.028001</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>27.935260</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <th>0</th>\n",
- " <td>29.533568</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <th>0</th>\n",
- " <td>31.274453</td>\n",
- " <td>41.612607</td>\n",
- " <td>166.921202</td>\n",
- " <td>97.473849</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <th>0</th>\n",
- " <td>28.293434</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <th>0</th>\n",
- " <td>33.042608</td>\n",
- " <td>61.780070</td>\n",
- " <td>174.441777</td>\n",
- " <td>290.449040</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"17\" valign=\"top\">057B30G2</th>\n",
- " <th rowspan=\"4\" valign=\"top\">2</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>18.968181</td>\n",
- " <td>17.610463</td>\n",
- " <td>166.291669</td>\n",
- " <td>7.722758</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>28.534100</td>\n",
- " <td>44.234555</td>\n",
- " <td>165.797150</td>\n",
- " <td>58.574351</td>\n",
- " <td>28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>30.633345</td>\n",
- " <td>32.004088</td>\n",
- " <td>166.788177</td>\n",
- " <td>565.868529</td>\n",
- " <td>30</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <th>0</th>\n",
- " <td>27.835786</td>\n",
- " <td>43.018873</td>\n",
- " <td>169.477370</td>\n",
- " <td>433.225682</td>\n",
- " <td>31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>28.034553</td>\n",
- " <td>53.457678</td>\n",
- " <td>160.674130</td>\n",
- " <td>1002.391912</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>25.565483</td>\n",
- " <td>68.222377</td>\n",
- " <td>160.495419</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"2\" valign=\"top\">12</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.674439</td>\n",
- " <td>40.453873</td>\n",
- " <td>165.654194</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>33.328252</td>\n",
- " <td>40.899023</td>\n",
- " <td>173.268236</td>\n",
- " <td>NaN</td>\n",
- " <td><NA></td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"3\" valign=\"top\">16</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.166927</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>27.885092</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>32.616679</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>29.180735</td>\n",
- " <td>30.049136</td>\n",
- " <td>174.280104</td>\n",
- " <td>170.293861</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"2\" valign=\"top\">23</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>31.895192</td>\n",
- " <td>41.637150</td>\n",
- " <td>149.514568</td>\n",
- " <td>8.084435</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>29.498332</td>\n",
- " <td>50.533080</td>\n",
- " <td>171.461484</td>\n",
- " <td>1201.192824</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th rowspan=\"3\" valign=\"top\">30</th>\n",
- " <th>0</th>\n",
- " <th>0</th>\n",
- " <td>17.554856</td>\n",
- " <td>51.042460</td>\n",
- " <td>168.864374</td>\n",
- " <td>5.343565</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <th>0</th>\n",
- " <td>29.404072</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <th>0</th>\n",
- " <td>32.240981</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " distance_1_2 distance_2_3 angle_21_23 \\\n",
- "ds tomo_id object_id \n",
- "053B40G2 4 0 0 21.095611 60.276241 137.868919 \n",
- " 5 0 0 29.502676 NaN NaN \n",
- " 1 0 24.832307 43.241020 164.050163 \n",
- " 2 0 30.172572 NaN NaN \n",
- " 3 0 31.087429 26.944366 164.258483 \n",
- " 8 0 0 26.332217 10.184926 171.030794 \n",
- " 18 0 0 29.780962 58.197185 173.998510 \n",
- " 1 0 29.499533 49.947940 173.269796 \n",
- " 2 0 27.753017 59.075236 171.329462 \n",
- " 3 0 26.119723 52.913860 175.469543 \n",
- " 20 0 0 27.564635 51.528415 171.741981 \n",
- " 1 0 29.986906 54.243535 173.332289 \n",
- " 22 0 0 29.095342 65.611610 167.552579 \n",
- " 1 0 34.082424 45.047355 172.796835 \n",
- "054B36G1 2 0 0 28.860724 50.738282 169.974732 \n",
- " 3 0 0 32.918810 NaN NaN \n",
- " 7 0 0 28.186633 28.223016 154.695752 \n",
- " 1 0 28.329459 48.747746 162.660341 \n",
- " 2 0 30.429561 47.480343 160.759520 \n",
- " 3 0 31.647358 45.451366 166.210177 \n",
- " 4 0 26.638607 48.631320 164.303959 \n",
- " 5 0 30.272416 53.902126 168.519110 \n",
- " 6 0 65.285148 NaN NaN \n",
- " 11 0 0 48.298107 55.133918 102.926047 \n",
- " 1 0 27.684032 72.729038 152.148465 \n",
- " 2 0 30.399087 46.203811 156.695492 \n",
- " 13 0 0 29.523980 NaN NaN \n",
- " 1 0 31.098125 67.095690 148.884104 \n",
- " 14 0 0 32.563100 41.258266 168.134535 \n",
- " 1 0 28.940105 27.522238 170.971118 \n",
- " 2 0 27.935260 NaN NaN \n",
- " 3 0 29.533568 NaN NaN \n",
- " 4 0 31.274453 41.612607 166.921202 \n",
- " 5 0 28.293434 NaN NaN \n",
- " 6 0 33.042608 61.780070 174.441777 \n",
- "057B30G2 2 0 0 18.968181 17.610463 166.291669 \n",
- " 1 0 28.534100 44.234555 165.797150 \n",
- " 2 0 30.633345 32.004088 166.788177 \n",
- " 3 0 27.835786 43.018873 169.477370 \n",
- " 4 0 0 28.034553 53.457678 160.674130 \n",
- " 10 0 0 25.565483 68.222377 160.495419 \n",
- " 12 0 0 29.674439 40.453873 165.654194 \n",
- " 1 0 33.328252 40.899023 173.268236 \n",
- " 16 0 0 29.166927 NaN NaN \n",
- " 1 0 27.885092 NaN NaN \n",
- " 2 0 32.616679 NaN NaN \n",
- " 20 0 0 29.180735 30.049136 174.280104 \n",
- " 23 0 0 31.895192 41.637150 149.514568 \n",
- " 1 0 29.498332 50.533080 171.461484 \n",
- " 30 0 0 17.554856 51.042460 168.864374 \n",
- " 1 0 29.404072 NaN NaN \n",
- " 2 0 32.240981 NaN NaN \n",
- "\n",
- " distance_to_closest_break \\\n",
- "ds tomo_id object_id \n",
- "053B40G2 4 0 0 NaN \n",
- " 5 0 0 NaN \n",
- " 1 0 10.670890 \n",
- " 2 0 NaN \n",
- " 3 0 603.879757 \n",
- " 8 0 0 NaN \n",
- " 18 0 0 3.937672 \n",
- " 1 0 3.776994 \n",
- " 2 0 5.644370 \n",
- " 3 0 1040.640748 \n",
- " 20 0 0 5.901630 \n",
- " 1 0 1091.114905 \n",
- " 22 0 0 NaN \n",
- " 1 0 NaN \n",
- "054B36G1 2 0 0 13.976631 \n",
- " 3 0 0 NaN \n",
- " 7 0 0 129.037390 \n",
- " 1 0 9.551492 \n",
- " 2 0 23.269485 \n",
- " 3 0 7.781381 \n",
- " 4 0 16.315059 \n",
- " 5 0 97.418625 \n",
- " 6 0 NaN \n",
- " 11 0 0 15.566473 \n",
- " 1 0 10.977328 \n",
- " 2 0 188.565914 \n",
- " 13 0 0 NaN \n",
- " 1 0 NaN \n",
- " 14 0 0 152.079839 \n",
- " 1 0 115.028001 \n",
- " 2 0 NaN \n",
- " 3 0 NaN \n",
- " 4 0 97.473849 \n",
- " 5 0 NaN \n",
- " 6 0 290.449040 \n",
- "057B30G2 2 0 0 7.722758 \n",
- " 1 0 58.574351 \n",
- " 2 0 565.868529 \n",
- " 3 0 433.225682 \n",
- " 4 0 0 1002.391912 \n",
- " 10 0 0 NaN \n",
- " 12 0 0 NaN \n",
- " 1 0 NaN \n",
- " 16 0 0 NaN \n",
- " 1 0 NaN \n",
- " 2 0 NaN \n",
- " 20 0 0 170.293861 \n",
- " 23 0 0 8.084435 \n",
- " 1 0 1201.192824 \n",
- " 30 0 0 5.343565 \n",
- " 1 0 NaN \n",
- " 2 0 NaN \n",
- "\n",
- " closest_break_contour_id \n",
- "ds tomo_id object_id \n",
- "053B40G2 4 0 0 <NA> \n",
- " 5 0 0 0 \n",
- " 1 0 0 \n",
- " 2 0 0 \n",
- " 3 0 9 \n",
- " 8 0 0 <NA> \n",
- " 18 0 0 4 \n",
- " 1 0 9 \n",
- " 2 0 6 \n",
- " 3 0 0 \n",
- " 20 0 0 1 \n",
- " 1 0 1 \n",
- " 22 0 0 <NA> \n",
- " 1 0 <NA> \n",
- "054B36G1 2 0 0 3 \n",
- " 3 0 0 <NA> \n",
- " 7 0 0 0 \n",
- " 1 0 1 \n",
- " 2 0 3 \n",
- " 3 0 7 \n",
- " 4 0 46 \n",
- " 5 0 39 \n",
- " 6 0 0 \n",
- " 11 0 0 3 \n",
- " 1 0 0 \n",
- " 2 0 1 \n",
- " 13 0 0 <NA> \n",
- " 1 0 <NA> \n",
- " 14 0 0 7 \n",
- " 1 0 3 \n",
- " 2 0 0 \n",
- " 3 0 0 \n",
- " 4 0 2 \n",
- " 5 0 0 \n",
- " 6 0 2 \n",
- "057B30G2 2 0 0 1 \n",
- " 1 0 28 \n",
- " 2 0 30 \n",
- " 3 0 31 \n",
- " 4 0 0 2 \n",
- " 10 0 0 <NA> \n",
- " 12 0 0 <NA> \n",
- " 1 0 <NA> \n",
- " 16 0 0 0 \n",
- " 1 0 0 \n",
- " 2 0 0 \n",
- " 20 0 0 2 \n",
- " 23 0 0 0 \n",
- " 1 0 1 \n",
- " 30 0 0 4 \n",
- " 1 0 0 \n",
- " 2 0 0 "
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Perform measurements\n",
- "\n",
- "def measure(gdf):\n",
- " \"\"\"\n",
- " Measure the distance between two contours\n",
- " \"\"\"\n",
- " # get position of contours\n",
- "\n",
- " tdf = gdf[gdf.type=='T3SS']\n",
- " # bdf = gdf[gdf.type=='break']\n",
- "\n",
- " tdf = tdf.sort_values(by=['object_id', 'contour_id'])\n",
- "\n",
- " positions = []\n",
- " for contour_id in [0, 1, 2]:\n",
- " if contour_id not in tdf.contour_id.values:\n",
- " positions.append(np.array([np.nan, np.nan, np.nan]))\n",
- " continue\n",
- " x = tdf[tdf.contour_id==contour_id].x[0]\n",
- " y = tdf[tdf.contour_id==contour_id].y[0]\n",
- " z = tdf[tdf.contour_id==contour_id].z[0]\n",
- " positions.append(np.array([x, y, z]))\n",
- "\n",
- " # 1) distance 1<->2\n",
- " d12 = np.linalg.norm(positions[0] - positions[1])\n",
- "\n",
- " if d12 > 100:\n",
- " # log message indicating gdf\n",
- " msg = 'Distance between 1 and 2 is suspiciously large: {}. Maybe theres a measurement problem?'.format(d12)\n",
- " msg += '\\n\\tdataset: {}\\ttomo_id: {}\\tobject_id (starting at 0): {}'.format(gdf['ds'].values[0], gdf['tomo_id'].values[0], gdf['object_id'].values[0])\n",
- " log_msgs.append(msg)\n",
- "\n",
- " # 2) distance 2<->3 (only if 3 exists within the same object)\n",
- " d23 = np.linalg.norm(positions[1] - positions[2])\n",
- "\n",
- " # 3) angle between 21 and 23\n",
- " v21 = positions[0] - positions[1]\n",
- " v23 = positions[2] - positions[1]\n",
- "\n",
- " angle_rad = np.arccos(\n",
- " np.dot(v21, v23) / np.product([np.linalg.norm(v) for v in [v21, v23]]))\n",
- "\n",
- " and_deg = np.degrees(angle_rad)\n",
- "\n",
- " # # 4) for every 3 the closest membrane break point\n",
- " bdf = df[\\\n",
- " (df.type=='break') &\\\n",
- " (df.ds==gdf['ds'].values[0]) &\\\n",
- " (df.tomo_id==gdf['tomo_id'].values[0])]\n",
- "\n",
- " bdf = bdf.sort_values(by=['contour_id'])\n",
- "\n",
- " if len(bdf):\n",
- " b_positions = np.array([bdf['x'], bdf['y'], bdf['z']]).T\n",
- " b_dists = np.linalg.norm(np.array(positions[2]) - b_positions, axis=1)\n",
- " b_closest_ind = np.argmin(b_dists)\n",
- "\n",
- " b_closest_contour_id = int(list(bdf['contour_id'])[b_closest_ind])\n",
- " b_distance = b_dists[b_closest_ind]\n",
- "\n",
- " # import pdb; pdb.set_trace()\n",
- "\n",
- " else:\n",
- " b_closest_contour_id = np.nan\n",
- " b_distance = np.nan\n",
- "\n",
- " ms = pd.DataFrame({\n",
- " 'distance_1_2': [d12],\n",
- " 'distance_2_3': [d23],\n",
- " 'angle_21_23': [and_deg],\n",
- " 'distance_to_closest_break': [b_distance],\n",
- " # 'closest_break_contour_id': b_closest_contour_id,\n",
- " 'closest_break_contour_id': pd.array([b_closest_contour_id], dtype=pd.Int64Dtype()),\n",
- " \n",
- " })\n",
- "\n",
- " return ms\n",
- " \n",
- "\n",
- "mdf = df.groupby(['ds', 'tomo_id', 'object_id']).apply(measure)\n",
- "# mdf = mdf.reset_index(inplace=True)\n",
- "mdf.to_csv(os.path.join(output_dir, 't3ss_geometry.csv'))\n",
- "open(os.path.join(output_dir, 't3ss_geometry.log'), 'w').write('\\n'.join(log_msgs))\n",
- "mdf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 5) distances between 2\n",
- "\n",
- "from scipy import spatial\n",
- "import xarray as xr\n",
- "\n",
- "tdf = df[df.type=='T3SS']\n",
- "for ds in np.unique(tdf.ds):\n",
- " stdf = tdf[tdf.ds==ds]\n",
- " for tomo_id in np.unique(stdf.tomo_id):\n",
- " tstdf = stdf[stdf.tomo_id==tomo_id]\n",
- " tstdf2 = tstdf[tstdf.contour_id==1]\n",
- " tstdf2 = tstdf2.sort_values(by=['object_id'])\n",
- " poss = np.array([tstdf2.x, tstdf2.y, tstdf2.z]).T\n",
- " d = spatial.distance_matrix(poss, poss)\n",
- " xd = xr.DataArray(d, dims=['object_id', 'object_id'], coords={'object_id': tstdf2.object_id})\n",
- " xd.to_pandas().to_csv(os.path.join(output_dir, f\"t3ss_distances_ds_{tstdf2['ds'].values[0]}_tomo-id_{tstdf2['tomo_id'].values[0]}.csv\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import seaborn as sns\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "pmdf = mdf.reset_index()\n",
- "\n",
- "xcol = 'ds'\n",
- "for ycol in ['distance_1_2', 'distance_2_3', 'angle_21_23', 'distance_to_closest_break']:\n",
- " plt.figure()\n",
- " ax = sns.boxplot(data=pmdf, x='ds', y=ycol, fliersize=0)\n",
- " # ax = sns.violinplot(data=pmdf, x='ds', y=ycol)\n",
- " ax2 = sns.stripplot(data=pmdf, x='ds', y=ycol, size=5)\n",
- "\n",
- " nobs = pmdf[xcol].value_counts().values\n",
- "\n",
- " pos = range(len(nobs))\n",
- " labels = [ax.get_xticklabels()[i].get_text() for i in pos]\n",
- " labels = [l + '\\n(N=%s)'%nobs[i] for i, l in enumerate(labels)]\n",
- " ax.set_xticks(pos)\n",
- " ax.set_xticklabels(labels)\n",
- "\n",
- " plt.tight_layout()\n",
- "\n",
- " plt.savefig(os.path.join(output_dir, 't3ss_geometry_{}.pdf'.format(ycol)), dpi=300)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "pyclem",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/membrain_seg.ipynb b/notebooks/membrain_seg.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aa64acab56c7b47749548a56e3d5f970cd1d5e0f
--- /dev/null
+++ b/notebooks/membrain_seg.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Automated membrane segmentation using MemBrain v2\n",
+ "\n",
+ "This notebook is used to segment membranes in tomograms using [MemBrain v2](https://teamtomo.org/membrain-seg/).\n",
+ "\n",
+ "The notebook was used both for segmenting full tomograms and extracted substacks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Software environment\n",
+ "Use this notebook with a conda env:\n",
+ "\n",
+ "- `conda create -n t3ss_membrain_seg python=3.9`\n",
+ "- `conda activate t3ss_membrain_seg`\n",
+ "- `pip install mrcfile jupyter membrain-seg`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "import os\n",
+ "import mrcfile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# segment using membrain-seg\n",
+ "# (code cell to be executed on DP)\n",
+ "\n",
+ "input_dir = \"/mnt/gaia/eirene/Segmentation/T3SS_zones\"\n",
+ "ckpt_stem = \"MemBrain_seg_v9b\" # found to work slightly better than version 10 on our data\n",
+ "ckpt_path = f\"/mnt/gaia/eirene/Segmentation/membrain_seg/{ckpt_stem}.ckpt\"\n",
+ "output_dir = \"/mnt/gaia/eirene/Segmentation/T3SS_zones_membrain_seg_original_pixel_spacing\"\n",
+ "\n",
+ "if not os.path.exists(output_dir):\n",
+ " os.makedirs(output_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# find and loop over all mrc files\n",
+ "for file in Path(input_dir).glob(\"*.mrc\"):\n",
+ "\n",
+ " ####\n",
+ " # with size matching\n",
+ " ####\n",
+ " # seg_file = os.path.join(output_dir, file.stem + \"_10A_{ckpt_stem}.ckpt_segmented.mrc\")\n",
+ " # if os.path.exists(seg_file):\n",
+ " # print(f\"Skipping {file.stem}\")\n",
+ " # continue\n",
+ "\n",
+ " # # match pixel size\n",
+ " # input_file = os.path.join(input_dir, file)\n",
+ " # output_file = os.path.join(output_dir, file.stem + \"_10A.mrc\")\n",
+ " # input_pixel_size = float(mrcfile.mmap(input_file).voxel_size.x)\n",
+ " # print(input_pixel_size)\n",
+ "\n",
+ " # cmd = f\"tomo_preprocessing match_pixel_size --input-tomogram {input_file} --output-path {output_file} --pixel-size-out 10.0 --pixel-size-in {input_pixel_size} --disable-smooth\" # crashed on dp with smoothing\n",
+ " # os.system(cmd)\n",
+ " # print(cmd)\n",
+ "\n",
+ " # # segment\n",
+ " # input_file = output_file\n",
+ " # cmd = f\"membrain segment --tomogram-path {output_file} --out-folder {output_dir} --ckpt-path {ckpt_path}\"\n",
+ " # os.system(cmd)\n",
+ " # print(cmd)\n",
+ "\n",
+ " ####\n",
+ " # without size matching\n",
+ " ####\n",
+ "\n",
+ " seg_file = os.path.join(output_dir, file.stem + f\"_{ckpt_stem}.ckpt_segmented.mrc\")\n",
+ " if os.path.exists(seg_file):\n",
+ " print(f\"Skipping {file.stem}\")\n",
+ " continue\n",
+ " else:\n",
+ " print(f\"Segmenting {file.stem}\")\n",
+ "\n",
+ " # segment\n",
+ " input_file = os.path.join(input_dir, file)\n",
+ " cmd = f\"membrain segment --tomogram-path {input_file} --out-folder {output_dir} --ckpt-path {ckpt_path}\"\n",
+ " os.system(cmd)\n",
+ " print(cmd)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/membrane_processing.ipynb b/notebooks/membrane_processing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0b79b11433043ddd405efd516b77ad3bfbcb9595
--- /dev/null
+++ b/notebooks/membrane_processing.ipynb
@@ -0,0 +1,1313 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Full tomogram processing\n",
+ "\n",
+ "This notebook is used to\n",
+ "- reconstruct the different surfaces segmented earlier\n",
+ "- calculate distances between the vacuole membrane and the outer membrane\n",
+ "- visualize the results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Software environment\n",
+ "Use this notebook with a conda env:\n",
+ "\n",
+ "- `conda create -n t3ss_rec python=3.10`\n",
+ "- `conda activate t3ss_rec`\n",
+ "- `pip install mrcfile pandas imodmodel ipython jupyter matplotlib seaborn ipympl scipy xarray pyvista`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Surface reconstruction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import mrcfile\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import os, sys\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import imodmodel\n",
+ "\n",
+ "import numpy as np\n",
+ "import pyvista as pv\n",
+ "\n",
+ "from matplotlib import pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define utility functions\n",
+ "\n",
+ "def get_border_vertex_inds(surf):\n",
+ " faces = surf.faces.reshape(-1,4)[:,1:4]\n",
+ " edges = np.concatenate((faces[:,0:2], faces[:,1:], faces[:,0::2]), axis=0)\n",
+ " edges = np.sort(edges, axis=1)\n",
+ " edges_unique, edge_counts = np.unique(edges, return_counts=True, axis=0)\n",
+ "\n",
+ " border_edges = edges_unique[edge_counts==1]\n",
+ " border_vertices = np.unique(border_edges)\n",
+ " border_face_ids = np.where( np.any(np.isin(faces, border_vertices), axis=1) )[0]\n",
+ "\n",
+ " return border_vertices\n",
+ "\n",
+ "def measure_distances_surfs(surf_q, surf_r):\n",
+ " \"\"\"\n",
+ " For each point of the vacuole membrane (the query_object_id),\n",
+ " we calculate the distance to the nearest point on the outer membrane (ref_object_id).\n",
+ " \"\"\"\n",
+ "\n",
+ " pts_q = surf_q.points\n",
+ " pts_r = surf_r.points\n",
+ "\n",
+ " border_indices_r = get_border_vertex_inds(surf_r)\n",
+ "\n",
+ " dists = np.zeros(len(pts_q)) * np.nan\n",
+ "\n",
+ " # for each point in pts1, find the closest point in pts2\n",
+ " tree = cKDTree(pts_r)\n",
+ " distances, indices = tree.query(pts_q)\n",
+ "\n",
+ " distances[np.where(np.isin(indices, border_indices_r))] = np.nan\n",
+ "\n",
+ " # where distances are nan, assign distances of neighboring points\n",
+ " # nan_indices = np.where(np.isnan(distances))[0]\n",
+ " tree = cKDTree(pts_q[~np.isnan(distances)])\n",
+ " _, indices = tree.query(pts_q[np.isnan(distances)])\n",
+ "\n",
+ " distances[np.isnan(distances)] = distances[~np.isnan(distances)][indices]\n",
+ "\n",
+ " return distances"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_dir = Path(\"/Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation\")\n",
+ "out_dir = Path(\"/Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology\")\n",
+ "\n",
+ "# use pathlib\n",
+ "files = sorted(list(data_dir.glob(\"*_cor.tif\")))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Preprocessing: Reconstruction and distance calculation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/053B40G2_TS_18_bin3_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/053B41G2_TS_13_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/054B36G1_TS_13_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/054B36G1_TS_14_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_16_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_20_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_23_bin2_tiltcor_rec_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_29_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_30_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B36G3_TS_01_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B36G3_TS_10_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B36G3_TS_12_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
+ "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B37G4_TS_09_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tifffile\n",
+ "from scipy import ndimage\n",
+ "import copy\n",
+ "from scipy.spatial import cKDTree\n",
+ "\n",
+ "for i, file in enumerate(files):\n",
+ "\n",
+ " # 23, 01, 10\n",
+ " # if i not in [23]: continue\n",
+ " \n",
+ "\n",
+ " tmpout = out_dir / (file.stem + f\"_surf_{3}.obj\")\n",
+ " if os.path.exists(tmpout):\n",
+ " print('skipping', tmpout)\n",
+ " continue\n",
+ "\n",
+ " print(f\"Processing file {i+1} of {len(files)}\")\n",
+ "\n",
+ " im = tifffile.imread(files[i])\n",
+ "\n",
+ " # erode\n",
+ " seg = im > 0\n",
+ " seg = ndimage.binary_erosion(seg, iterations=2)\n",
+ " im = seg * im\n",
+ "\n",
+ " # ds = distances[i]\n",
+ " # spacing = spacings[i]\n",
+ " spacing = 1\n",
+ "\n",
+ " k_dec = 50\n",
+ "\n",
+ " surfs = []\n",
+ "\n",
+ " object_ids = np.unique(im)[1:-1]\n",
+ "\n",
+ " for object_id in object_ids[:]:\n",
+ "\n",
+ " labels, N = ndimage.label(im == object_id)\n",
+ " # get sizes\n",
+ " sizes = ndimage.sum(labels > 0, labels, range(0, N+1))\n",
+ " # filter sizes\n",
+ " labels = (sizes > 10000)[labels] * labels\n",
+ " unique_labels = np.unique(labels)[1:]\n",
+ "\n",
+ " surf_labels = []\n",
+ " print(unique_labels)\n",
+ " pts = np.array(np.where(labels > 0)).T * spacing\n",
+ "\n",
+ " for label in unique_labels:\n",
+ " pts_label = np.array(np.where(labels == label)).T * spacing\n",
+ " pts_label_sub = pts_label[::k_dec]\n",
+ "\n",
+ " # simply pass the numpy points to the PolyData constructor\n",
+ " cloud = pv.PolyData(pts_label_sub)\n",
+ " surf_label = cloud.reconstruct_surface(sample_spacing=spacing * 5, nbr_sz=100, progress_bar=True)\n",
+ " surf_labels.append(surf_label)\n",
+ "\n",
+ " surf = surf_labels[0]\n",
+ " for surf_label in surf_labels[1:]:\n",
+ " surf += surf_label\n",
+ "\n",
+ " # pts = np.array(np.where(im == object_id)).T * spacing\n",
+ " # pts_sub = pts[::k_dec]\n",
+ " # cloud = pv.PolyData(pts_sub)\n",
+ " # surf = cloud.reconstruct_surface(sample_spacing=spacing * 5, nbr_sz=100, progress_bar=True)\n",
+ " # surfrc = copy.deepcopy(surf)\n",
+ "\n",
+ " # remove points that are too far from original points\n",
+ " # points_to_remove = np.where((distances > 5) & np.isin(np.arange(len(pts_mesh)), border_indices))[0]\n",
+ " # cloud, ridx = cloud.remove_points(points_to_remove)\n",
+ " pts_mesh = surf.points\n",
+ " tree = cKDTree(pts)\n",
+ " distances, indices = tree.query(pts_mesh)\n",
+ " surf, ridx = surf.remove_points(distances > 10)\n",
+ "\n",
+ " # smooth\n",
+ " surf = surf.smooth_taubin(n_iter=50)\n",
+ "\n",
+ " # surf = surf.smooth(n_iter=10000, boundary_smoothing=True, relaxation_factor=1)\n",
+ "\n",
+ " # creates wedges, so try to be more conservative\n",
+ " surf = surf.smooth(n_iter=1000, boundary_smoothing=True, relaxation_factor=.1)\n",
+ "\n",
+ " # # for visualization, smooth borders\n",
+ " # s = surf.subdivide(2, subfilter='linear')\n",
+ " # pts_mesh = s.points\n",
+ " # tree = cKDTree(pts)\n",
+ " # distances, indices = tree.query(pts_mesh)\n",
+ " # s, ridx = s.remove_points(distances > 15)\n",
+ "\n",
+ " surfs.append(surf)\n",
+ "\n",
+ " # save cloud points\n",
+ " np.save(out_dir / (file.stem + f\"_pts_{object_id}.npy\"), pts)\n",
+ "\n",
+ " # save distances and curvatures for VM\n",
+ " if object_id == 3:\n",
+ " d = measure_distances_surfs(surfs[2], surfs[1])\n",
+ " np.save(out_dir / (file.stem + \"_distances.npy\"), d)\n",
+ "\n",
+ " c = surfs[2].curvature(curv_type=\"mean\")\n",
+ " np.save(out_dir / (file.stem + \"_curvature.npy\"), c)\n",
+ "\n",
+ " out_file = out_dir / (file.stem + f\"_surf_{object_id}.obj\")\n",
+ " # make sure the output directory exists\n",
+ " if not out_file.parent.exists():\n",
+ " out_file.parent.mkdir(parents=True)\n",
+ " \n",
+ " pv.save_meshio(out_file, surf)\n",
+ "\n",
+ " # s = surf.subdivide(2, subfilter='linear')\n",
+ " # pts_mesh = s.points\n",
+ " # tree = cKDTree(pts)\n",
+ " # distances, indices = tree.query(pts_mesh)\n",
+ " # s, ridx = s.remove_points(distances > 15)\n",
+ "\n",
+ " # only eliminate from border\n",
+ "\n",
+ " # for qualities, use nearest neighbor for vis\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extraction of distance measurements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1300x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get measurements for all files and combine into one dataframe\n",
+ "\n",
+ "df = []\n",
+ "for i, file in enumerate(files):\n",
+ " d = np.load(out_dir / (file.stem + \"_distances.npy\"))\n",
+ " # c = np.load(out_dir / (file.stem + \"_curvature.npy\"))\n",
+ " \n",
+ " tmpdf = pd.DataFrame({\n",
+ " 'distance': d,\n",
+ " # 'curvature': c,\n",
+ " 'file': file.stem,\n",
+ " 'tomogram': file.stem.split('_bin')[0],\n",
+ " })\n",
+ " df.append(tmpdf)\n",
+ "\n",
+ "df = pd.concat(df)\n",
+ "\n",
+ "# save summary\n",
+ "df.to_csv(out_dir / \"measurements.csv\", index=False)\n",
+ "\n",
+ "plt.figure(figsize=(13, 8))\n",
+ "\n",
+ "sns.histplot(\n",
+ " df,\n",
+ " x='distance',\n",
+ " hue='tomogram',\n",
+ " bins=100,\n",
+ " log_scale=True,\n",
+ " stat = 'density',\n",
+ " common_norm = False,\n",
+ " )\n",
+ "\n",
+ "plt.savefig(out_dir / \"histogram_distances_all.png\")\n",
+ "\n",
+ "# save individual\n",
+ "for i, file in enumerate(files[:]):\n",
+ " tmpdf = df[df.file == file.stem]\n",
+ " tmpdf.to_csv(out_dir / f'{file.stem}_measurements.csv', index=False)\n",
+ "\n",
+ " plt.figure(figsize=(13, 8))\n",
+ "\n",
+ " sns.histplot(\n",
+ " tmpdf,\n",
+ " x='distance',\n",
+ " hue='tomogram',\n",
+ " bins=100,\n",
+ " log_scale=True,\n",
+ " stat = 'density',\n",
+ " common_norm = False,\n",
+ " )\n",
+ "\n",
+ " plt.savefig(out_dir / (file.stem + \"_histogram_distances.png\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 1 of 13\n",
+ "it 0 5706\n",
+ "it 1 3783\n",
+ "it 2 2784\n",
+ "it 3 1704\n",
+ "it 4 756\n",
+ "it 5 220\n",
+ "it 6 37\n",
+ "it 7 6\n",
+ "it 8 1\n",
+ "it 9 0\n",
+ "it 0 5700\n",
+ "it 1 3196\n",
+ "it 2 2290\n",
+ "it 3 1338\n",
+ "it 4 580\n",
+ "it 5 181\n",
+ "it 6 48\n",
+ "it 7 15\n",
+ "it 8 7\n",
+ "it 9 4\n",
+ "it 10 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "802483d3f9814a09be7d683871b7ef9f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28e0884f0_1&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 2 of 13\n",
+ "it 0 6891\n",
+ "it 1 4965\n",
+ "it 2 3244\n",
+ "it 3 1869\n",
+ "it 4 783\n",
+ "it 5 260\n",
+ "it 6 74\n",
+ "it 7 20\n",
+ "it 8 6\n",
+ "it 9 0\n",
+ "it 0 5754\n",
+ "it 1 2683\n",
+ "it 2 1634\n",
+ "it 3 913\n",
+ "it 4 424\n",
+ "it 5 167\n",
+ "it 6 73\n",
+ "it 7 40\n",
+ "it 8 17\n",
+ "it 9 9\n",
+ "it 10 4\n",
+ "it 11 1\n",
+ "it 12 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "59a9ec3d217948e38cb65811eb2ba3c1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x2a0807e50_2&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 3 of 13\n",
+ "it 0 6646\n",
+ "it 1 4491\n",
+ "it 2 3124\n",
+ "it 3 1960\n",
+ "it 4 889\n",
+ "it 5 306\n",
+ "it 6 70\n",
+ "it 7 15\n",
+ "it 8 5\n",
+ "it 9 0\n",
+ "it 0 7064\n",
+ "it 1 5019\n",
+ "it 2 3721\n",
+ "it 3 2352\n",
+ "it 4 1077\n",
+ "it 5 437\n",
+ "it 6 156\n",
+ "it 7 63\n",
+ "it 8 34\n",
+ "it 9 24\n",
+ "it 10 18\n",
+ "it 11 17\n",
+ "it 12 13\n",
+ "it 13 11\n",
+ "it 14 8\n",
+ "it 15 8\n",
+ "it 16 7\n",
+ "it 17 4\n",
+ "it 18 3\n",
+ "it 19 3\n",
+ "it 20 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "75f6f56e055e42e390da272db4fe2691",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28044bd60_3&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 4 of 13\n",
+ "it 0 7187\n",
+ "it 1 6356\n",
+ "it 2 5031\n",
+ "it 3 3480\n",
+ "it 4 1904\n",
+ "it 5 871\n",
+ "it 6 319\n",
+ "it 7 135\n",
+ "it 8 73\n",
+ "it 9 41\n",
+ "it 10 22\n",
+ "it 11 20\n",
+ "it 12 22\n",
+ "it 13 18\n",
+ "it 14 18\n",
+ "it 15 18\n",
+ "it 16 16\n",
+ "it 17 18\n",
+ "it 18 22\n",
+ "it 19 21\n",
+ "it 20 23\n",
+ "it 21 22\n",
+ "it 22 17\n",
+ "it 23 13\n",
+ "it 24 10\n",
+ "it 25 8\n",
+ "it 26 7\n",
+ "it 27 5\n",
+ "it 28 3\n",
+ "it 29 0\n",
+ "it 0 7813\n",
+ "it 1 6835\n",
+ "it 2 5332\n",
+ "it 3 3579\n",
+ "it 4 1781\n",
+ "it 5 706\n",
+ "it 6 252\n",
+ "it 7 119\n",
+ "it 8 77\n",
+ "it 9 42\n",
+ "it 10 29\n",
+ "it 11 19\n",
+ "it 12 15\n",
+ "it 13 13\n",
+ "it 14 10\n",
+ "it 15 7\n",
+ "it 16 2\n",
+ "it 17 1\n",
+ "it 18 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "04c8c8fc8b9244ac878bf5e0794530a7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28e45ba60_4&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 5 of 13\n",
+ "it 0 2257\n",
+ "it 1 1031\n",
+ "it 2 703\n",
+ "it 3 458\n",
+ "it 4 250\n",
+ "it 5 106\n",
+ "it 6 35\n",
+ "it 7 8\n",
+ "it 8 1\n",
+ "it 9 0\n",
+ "it 0 2442\n",
+ "it 1 1808\n",
+ "it 2 1292\n",
+ "it 3 844\n",
+ "it 4 406\n",
+ "it 5 162\n",
+ "it 6 66\n",
+ "it 7 32\n",
+ "it 8 8\n",
+ "it 9 5\n",
+ "it 10 3\n",
+ "it 11 1\n",
+ "it 12 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "45702daa240b47caacf8f512e00303b4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28e459cf0_5&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 6 of 13\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "19325418e9cb44fa8968eb99bcf15f4a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x2806fc430_6&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 7 of 13\n",
+ "it 0 8261\n",
+ "it 1 5466\n",
+ "it 2 3708\n",
+ "it 3 2264\n",
+ "it 4 1075\n",
+ "it 5 432\n",
+ "it 6 138\n",
+ "it 7 38\n",
+ "it 8 9\n",
+ "it 9 0\n",
+ "it 0 7937\n",
+ "it 1 5041\n",
+ "it 2 3415\n",
+ "it 3 2071\n",
+ "it 4 929\n",
+ "it 5 344\n",
+ "it 6 80\n",
+ "it 7 33\n",
+ "it 8 19\n",
+ "it 9 4\n",
+ "it 10 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f21c873d39134c83a0814b911b0095b2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28e3ba0e0_7&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 8 of 13\n",
+ "it 0 7471\n",
+ "it 1 6454\n",
+ "it 2 4987\n",
+ "it 3 3311\n",
+ "it 4 1680\n",
+ "it 5 701\n",
+ "it 6 214\n",
+ "it 7 70\n",
+ "it 8 11\n",
+ "it 9 3\n",
+ "it 10 2\n",
+ "it 11 1\n",
+ "it 12 0\n",
+ "it 0 7661\n",
+ "it 1 6552\n",
+ "it 2 5038\n",
+ "it 3 3264\n",
+ "it 4 1527\n",
+ "it 5 580\n",
+ "it 6 195\n",
+ "it 7 52\n",
+ "it 8 15\n",
+ "it 9 6\n",
+ "it 10 3\n",
+ "it 11 2\n",
+ "it 12 1\n",
+ "it 13 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "25830620765a422bbd5c7d6dcbf004a1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bf43d0_8&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 9 of 13\n",
+ "it 0 5092\n",
+ "it 1 3027\n",
+ "it 2 1937\n",
+ "it 3 992\n",
+ "it 4 466\n",
+ "it 5 171\n",
+ "it 6 46\n",
+ "it 7 12\n",
+ "it 8 0\n",
+ "it 0 4869\n",
+ "it 1 2956\n",
+ "it 2 1966\n",
+ "it 3 1170\n",
+ "it 4 541\n",
+ "it 5 198\n",
+ "it 6 41\n",
+ "it 7 12\n",
+ "it 8 7\n",
+ "it 9 5\n",
+ "it 10 4\n",
+ "it 11 1\n",
+ "it 12 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "142a3c7370da487b9f4a97800357e4fe",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bf6b90_9&reconnect=auto\" class=\"pyvista…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 10 of 13\n",
+ "it 0 9064\n",
+ "it 1 5417\n",
+ "it 2 3944\n",
+ "it 3 2640\n",
+ "it 4 1349\n",
+ "it 5 622\n",
+ "it 6 284\n",
+ "it 7 130\n",
+ "it 8 32\n",
+ "it 9 18\n",
+ "it 10 3\n",
+ "it 11 0\n",
+ "it 0 8924\n",
+ "it 1 5587\n",
+ "it 2 3929\n",
+ "it 3 2453\n",
+ "it 4 1257\n",
+ "it 5 537\n",
+ "it 6 186\n",
+ "it 7 93\n",
+ "it 8 58\n",
+ "it 9 22\n",
+ "it 10 6\n",
+ "it 11 2\n",
+ "it 12 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8a074cd304fb499c969fc2515b7a107c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bc0070_10&reconnect=auto\" class=\"pyvist…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 11 of 13\n",
+ "it 0 9670\n",
+ "it 1 8406\n",
+ "it 2 6765\n",
+ "it 3 4726\n",
+ "it 4 2518\n",
+ "it 5 1012\n",
+ "it 6 297\n",
+ "it 7 85\n",
+ "it 8 18\n",
+ "it 9 5\n",
+ "it 10 0\n",
+ "it 0 12344\n",
+ "it 1 10981\n",
+ "it 2 8821\n",
+ "it 3 6179\n",
+ "it 4 3552\n",
+ "it 5 1758\n",
+ "it 6 822\n",
+ "it 7 435\n",
+ "it 8 286\n",
+ "it 9 207\n",
+ "it 10 140\n",
+ "it 11 94\n",
+ "it 12 80\n",
+ "it 13 68\n",
+ "it 14 53\n",
+ "it 15 43\n",
+ "it 16 39\n",
+ "it 17 38\n",
+ "it 18 36\n",
+ "it 19 34\n",
+ "it 20 30\n",
+ "it 21 23\n",
+ "it 22 18\n",
+ "it 23 17\n",
+ "it 24 17\n",
+ "it 25 16\n",
+ "it 26 14\n",
+ "it 27 15\n",
+ "it 28 14\n",
+ "it 29 13\n",
+ "it 30 13\n",
+ "it 31 13\n",
+ "it 32 12\n",
+ "it 33 14\n",
+ "it 34 15\n",
+ "it 35 16\n",
+ "it 36 15\n",
+ "it 37 14\n",
+ "it 38 13\n",
+ "it 39 12\n",
+ "it 40 10\n",
+ "it 41 7\n",
+ "it 42 4\n",
+ "it 43 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0ef6e42ae17b46f6ae0adf517b385d58",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bf6170_11&reconnect=auto\" class=\"pyvist…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 12 of 13\n",
+ "it 0 8816\n",
+ "it 1 7857\n",
+ "it 2 6196\n",
+ "it 3 4323\n",
+ "it 4 2546\n",
+ "it 5 1245\n",
+ "it 6 541\n",
+ "it 7 233\n",
+ "it 8 107\n",
+ "it 9 59\n",
+ "it 10 34\n",
+ "it 11 25\n",
+ "it 12 25\n",
+ "it 13 25\n",
+ "it 14 27\n",
+ "it 15 29\n",
+ "it 16 31\n",
+ "it 17 28\n",
+ "it 18 24\n",
+ "it 19 22\n",
+ "it 20 18\n",
+ "it 21 17\n",
+ "it 22 18\n",
+ "it 23 18\n",
+ "it 24 20\n",
+ "it 25 21\n",
+ "it 26 23\n",
+ "it 27 23\n",
+ "it 28 24\n",
+ "it 29 29\n",
+ "it 30 32\n",
+ "it 31 35\n",
+ "it 32 39\n",
+ "it 33 43\n",
+ "it 34 38\n",
+ "it 35 39\n",
+ "it 36 34\n",
+ "it 37 37\n",
+ "it 38 30\n",
+ "it 39 30\n",
+ "it 40 30\n",
+ "it 41 32\n",
+ "it 42 29\n",
+ "it 43 30\n",
+ "it 44 30\n",
+ "it 45 26\n",
+ "it 46 26\n",
+ "it 47 23\n",
+ "it 48 17\n",
+ "it 49 11\n",
+ "it 50 7\n",
+ "it 51 4\n",
+ "it 52 1\n",
+ "it 53 0\n",
+ "it 0 6823\n",
+ "it 1 6258\n",
+ "it 2 5183\n",
+ "it 3 3723\n",
+ "it 4 2136\n",
+ "it 5 1066\n",
+ "it 6 499\n",
+ "it 7 254\n",
+ "it 8 121\n",
+ "it 9 50\n",
+ "it 10 15\n",
+ "it 11 9\n",
+ "it 12 4\n",
+ "it 13 1\n",
+ "it 14 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9f1ffe4c44694ac09e62783306cad407",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bf6140_12&reconnect=auto\" class=\"pyvist…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing file 13 of 13\n",
+ "it 0 6577\n",
+ "it 1 5928\n",
+ "it 2 4813\n",
+ "it 3 3457\n",
+ "it 4 1957\n",
+ "it 5 876\n",
+ "it 6 358\n",
+ "it 7 186\n",
+ "it 8 114\n",
+ "it 9 82\n",
+ "it 10 54\n",
+ "it 11 41\n",
+ "it 12 35\n",
+ "it 13 24\n",
+ "it 14 16\n",
+ "it 15 10\n",
+ "it 16 5\n",
+ "it 17 4\n",
+ "it 18 4\n",
+ "it 19 4\n",
+ "it 20 3\n",
+ "it 21 2\n",
+ "it 22 1\n",
+ "it 23 0\n",
+ "it 0 7046\n",
+ "it 1 6070\n",
+ "it 2 4744\n",
+ "it 3 3348\n",
+ "it 4 2075\n",
+ "it 5 1061\n",
+ "it 6 482\n",
+ "it 7 271\n",
+ "it 8 150\n",
+ "it 9 108\n",
+ "it 10 91\n",
+ "it 11 69\n",
+ "it 12 61\n",
+ "it 13 49\n",
+ "it 14 35\n",
+ "it 15 28\n",
+ "it 16 20\n",
+ "it 17 14\n",
+ "it 18 1\n",
+ "it 19 0\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "93a21dcabede40f295b4233c60069997",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bc03d0_13&reconnect=auto\" class=\"pyvist…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Load meshes from disk and visualize\n",
+ "\n",
+ "import tifffile\n",
+ "from scipy import ndimage\n",
+ "import copy\n",
+ "from scipy.spatial import cKDTree\n",
+ "\n",
+ "# df_vis = df[[int(r.tomogram[-2:]) in [1, 10, 23] for i, r in df.iterrows()]]\n",
+ "\n",
+ "distance_clim = [np.percentile(df.distance, p) for p in [5, 95]]\n",
+ "distance_clim = None\n",
+ "\n",
+ "# output file suffix\n",
+ "# suffix = \"_distances\"\n",
+ "suffix = \"_distances_clim\"\n",
+ "\n",
+ "for i, file in enumerate(files[:]):\n",
+ "\n",
+ " # if not 'TS_23' in file.stem:\n",
+ " # if (not 'TS_01' in file.stem):\n",
+ " # if (not 'TS_10' in file.stem):\n",
+ " # continue\n",
+ "\n",
+ " # if not 'TS_10' in file.stem:\n",
+ " # continue\n",
+ "\n",
+ " # if i not in [0]: continue\n",
+ " # if not 'TS_23' in file.stem: continue\n",
+ "\n",
+ " print(f\"Processing file {i+1} of {len(files)}\")\n",
+ "\n",
+ " orig_surfs = []\n",
+ " surfs = []\n",
+ " for object_id in [2,3]:\n",
+ "\n",
+ " out_file = out_dir / (file.stem + f\"_surf_{object_id}.obj\") \n",
+ " surf = pv.reader.OBJReader(out_file).read()\n",
+ " orig_surfs.append(surf)\n",
+ "\n",
+ " if '057B30G2_TS_20_bin2_tiltcor_rec_corrected_flatcrop' in file.stem:\n",
+ " s = surf\n",
+ " else:\n",
+ " # smooth borders\n",
+ " pts = np.load(out_dir / (file.stem + f\"_pts_{object_id}.npy\"))\n",
+ " tree = cKDTree(pts)\n",
+ " s = surf.subdivide(2, subfilter='linear')\n",
+ "\n",
+ " it = 0\n",
+ " while 1:\n",
+ " pts_mesh = s.points\n",
+ " distances, indices = tree.query(pts_mesh)\n",
+ " points_to_remove = np.where(distances > 5)[0]\n",
+ "\n",
+ " border_indices = get_border_vertex_inds(s)\n",
+ " points_to_remove = np.where((distances > 5) & np.isin(np.arange(len(pts_mesh)), border_indices))[0]\n",
+ " \n",
+ " s, ridx = s.remove_points(points_to_remove)\n",
+ "\n",
+ " print('it', it, len(points_to_remove))\n",
+ " \n",
+ " # break # testing\n",
+ "\n",
+ " if len(points_to_remove) == 0:\n",
+ " break\n",
+ " it += 1\n",
+ "\n",
+ " surfs.append(s)\n",
+ "\n",
+ " d = np.load(out_dir / (file.stem + \"_distances.npy\"))\n",
+ " c = np.load(out_dir / (file.stem + \"_curvature.npy\"))\n",
+ "\n",
+ " # get measurements for smoothed surface using nearest neighbors\n",
+ " tree = cKDTree(orig_surfs[-1].points)\n",
+ " distances, indices = tree.query(surfs[-1].points)\n",
+ " d_smooth = d[indices]\n",
+ "\n",
+ " pl = pv.Plotter()\n",
+ "\n",
+ " pl.add_mesh(surfs[-2], point_size=5,\n",
+ " color='Grey',\n",
+ " # show_edges=True,\n",
+ " opacity=0.5,\n",
+ " )\n",
+ "\n",
+ " pl.add_mesh(surfs[-1], point_size=5,\n",
+ " color='Grey',\n",
+ " scalars=d_smooth,\n",
+ " clim=distance_clim,\n",
+ " # show_edges=True,\n",
+ " )\n",
+ "\n",
+ " pl.show()\n",
+ "\n",
+ " pl.export_html(os.path.join(out_dir, file.stem + suffix +'.html'))\n",
+ "\n",
+ " screenshot_path = os.path.join(out_dir, file.stem + suffix +'.png')\n",
+ " graphic_path = os.path.join(out_dir, file.stem + suffix +'.pdf')\n",
+ " if os.path.exists(screenshot_path):\n",
+ " os.remove(screenshot_path)\n",
+ " \n",
+ " # save in 4k\n",
+ " pl.screenshot(screenshot_path, window_size=[3840, 2160])\n",
+ "\n",
+ " # save in vector format (?)\n",
+ " pl.save_graphic(graphic_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/substack_extraction.ipynb b/notebooks/substack_extraction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..354029f3a566649bd13847de7fe3ec62b2cc67eb
--- /dev/null
+++ b/notebooks/substack_extraction.ipynb
@@ -0,0 +1,1085 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Substack extraction\n",
+ "\n",
+ "This notebook is used to extract substacks from tomogram at the positions of the T3SS. Manually determined T3SS landmark coordinates (see the definition of 0, 1, 2 in the T3SS geometry notebook) are used to\n",
+ "- determine the centers of the substacks and\n",
+ "- and their orientations in 3D.\n",
+ "\n",
+ "The input to this notebook is an IMOD .mod file and the corresponding tomograms.\n",
+ "\n",
+ "The output is a table containing the processed coordinates and the extracted substacks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Software environment\n",
+ "Use this notebook with a conda env:\n",
+ "\n",
+ "- `conda create -n t3ss_geo python=3.10`\n",
+ "- `conda activate t3ss_geo`\n",
+ "- `pip install mrcfile pandas imodmodel ipython jupyter matplotlib seaborn ipympl scipy xarray`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 215,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import imodmodel\n",
+ "import mrcfile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 227,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "base_dirs = [\n",
+ " '/Volumes/Eirene/Points/Points_corrected',\n",
+ " '/Volumes/Eirene/Points/20240502_Points',\n",
+ " ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 242,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/Volumes/Eirene/Points/Points_corrected/057B30G2\n",
+ "No T3SS model found for 057B30G2_TS_26_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_20_bin2_tiltcor_T3SS.mod\n",
+ "No T3SS model found for 057B30G2_TS_05_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_20_bin2_tiltcor_T3SS.mod\n",
+ "No breaks model found for 057B30G2_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 057B30G2_TS_29_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_10_bin2_tiltcor_T3SS.mod\n",
+ "No breaks model found for 057B30G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
+ "/Volumes/Eirene/Points/Points_corrected/054B36G1\n",
+ "No T3SS model found for 054B36G1_TS_05_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_12_bin2_tiltcor_T3SS.mod\n",
+ "No breaks model found for 054B36G1_TS_03_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 054B36G1_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 054B36G1_TS_12_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/054B36G1/054B36G1_TS_13_bin2_tiltcor_T3SS.mod\n",
+ "/Volumes/Eirene/Points/Points_corrected/053B40G2\n",
+ "No breaks model found for 053B40G2_TS_22_bin3_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B40G2_TS_08_bin3_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc\n",
+ "/Volumes/Eirene/Points/20240502_Points/060B37G4\n",
+ "No breaks model found for 060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B37G4_TS_02_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B37G4_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
+ "/Volumes/Eirene/Points/20240502_Points/060B36G3\n",
+ "No breaks model found for 060B36G3_TS_07_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B36G3_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B36G3_TS_01_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B36G3_TS_09_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B36G3_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B36G3_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 060B36G3_TS_06_bin2_tiltcor_rec_corrected.mrc\n",
+ "/Volumes/Eirene/Points/20240502_Points/053B41G2\n",
+ "No breaks model found for 053B41G2_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_21_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 053B41G2_TS_03_bin2_tiltcor_rec.mrc, /Volumes/Eirene/Points/20240502_Points/053B41G2/053B41G2_TS_21_bin2_tiltcor_rec_corrected_T3SS.mod\n",
+ "No T3SS model found for 053B41G2_TS_07_bin2_tiltcor_rec.mrc, /Volumes/Eirene/Points/20240502_Points/053B41G2/053B41G2_TS_21_bin2_tiltcor_rec_corrected_T3SS.mod\n",
+ "No breaks model found for 053B41G2_TS_20_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_15_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 053B41G2_TS_01_bin2_tiltcor_rec.mrc, /Volumes/Eirene/Points/20240502_Points/053B41G2/053B41G2_TS_15_bin2_tiltcor_rec_corrected_T3SS.mod\n",
+ "No breaks model found for 053B41G2_TS_17_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_16_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_19_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_25_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_23_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B41G2_TS_14_bin2_tiltcor_rec_corrected.mrc\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>object_id</th>\n",
+ " <th>contour_id</th>\n",
+ " <th>x</th>\n",
+ " <th>y</th>\n",
+ " <th>z</th>\n",
+ " <th>source_fn</th>\n",
+ " <th>type</th>\n",
+ " <th>tomo_id</th>\n",
+ " <th>tomo_fn</th>\n",
+ " <th>ds</th>\n",
+ " <th>x_nm</th>\n",
+ " <th>y_nm</th>\n",
+ " <th>z_nm</th>\n",
+ " <th>voxel_size</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>447.214691</td>\n",
+ " <td>1366.812134</td>\n",
+ " <td>113.297363</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
+ " <td>057B30G2</td>\n",
+ " <td>430.220528</td>\n",
+ " <td>1314.873257</td>\n",
+ " <td>108.992062</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>426.437286</td>\n",
+ " <td>1387.116699</td>\n",
+ " <td>121.973129</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
+ " <td>057B30G2</td>\n",
+ " <td>410.232665</td>\n",
+ " <td>1334.406249</td>\n",
+ " <td>117.338149</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>582.123047</td>\n",
+ " <td>1461.564697</td>\n",
+ " <td>103.879143</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
+ " <td>057B30G2</td>\n",
+ " <td>560.002364</td>\n",
+ " <td>1406.025222</td>\n",
+ " <td>99.931734</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>573.605469</td>\n",
+ " <td>1485.683716</td>\n",
+ " <td>117.515335</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
+ " <td>057B30G2</td>\n",
+ " <td>551.808454</td>\n",
+ " <td>1429.227718</td>\n",
+ " <td>113.049751</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>425.504181</td>\n",
+ " <td>790.534546</td>\n",
+ " <td>133.468887</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
+ " <td>057B30G2</td>\n",
+ " <td>409.335017</td>\n",
+ " <td>760.494224</td>\n",
+ " <td>128.397068</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>1200.307251</td>\n",
+ " <td>561.571533</td>\n",
+ " <td>209.283905</td>\n",
+ " <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>14</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
+ " <td>053B41G2</td>\n",
+ " <td>1154.695562</td>\n",
+ " <td>540.231809</td>\n",
+ " <td>201.331114</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>1242.644043</td>\n",
+ " <td>543.317078</td>\n",
+ " <td>225.512543</td>\n",
+ " <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>14</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
+ " <td>053B41G2</td>\n",
+ " <td>1195.423555</td>\n",
+ " <td>522.671022</td>\n",
+ " <td>216.943064</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>9</td>\n",
+ " <td>0</td>\n",
+ " <td>886.989563</td>\n",
+ " <td>459.770081</td>\n",
+ " <td>170.415802</td>\n",
+ " <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>14</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
+ " <td>053B41G2</td>\n",
+ " <td>853.283949</td>\n",
+ " <td>442.298812</td>\n",
+ " <td>163.940000</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>9</td>\n",
+ " <td>1</td>\n",
+ " <td>910.624268</td>\n",
+ " <td>429.405426</td>\n",
+ " <td>160.421310</td>\n",
+ " <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>14</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
+ " <td>053B41G2</td>\n",
+ " <td>876.020535</td>\n",
+ " <td>413.088015</td>\n",
+ " <td>154.325299</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>945.903870</td>\n",
+ " <td>404.319824</td>\n",
+ " <td>147.044846</td>\n",
+ " <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>14</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
+ " <td>053B41G2</td>\n",
+ " <td>909.959512</td>\n",
+ " <td>388.955666</td>\n",
+ " <td>141.457140</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>548 rows × 14 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " object_id contour_id x y z \\\n",
+ "0 0 0 447.214691 1366.812134 113.297363 \n",
+ "0 0 1 426.437286 1387.116699 121.973129 \n",
+ "0 1 0 582.123047 1461.564697 103.879143 \n",
+ "0 1 1 573.605469 1485.683716 117.515335 \n",
+ "0 2 0 425.504181 790.534546 133.468887 \n",
+ ".. ... ... ... ... ... \n",
+ "0 8 1 1200.307251 561.571533 209.283905 \n",
+ "0 8 2 1242.644043 543.317078 225.512543 \n",
+ "0 9 0 886.989563 459.770081 170.415802 \n",
+ "0 9 1 910.624268 429.405426 160.421310 \n",
+ "0 9 2 945.903870 404.319824 147.044846 \n",
+ "\n",
+ " source_fn type tomo_id \\\n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ ".. ... ... ... \n",
+ "0 053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS... T3SS 14 \n",
+ "0 053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS... T3SS 14 \n",
+ "0 053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS... T3SS 14 \n",
+ "0 053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS... T3SS 14 \n",
+ "0 053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS... T3SS 14 \n",
+ "\n",
+ " tomo_fn ds x_nm \\\n",
+ "0 /Volumes/Eirene/Points/Points_corrected/057B30... 057B30G2 430.220528 \n",
+ "0 /Volumes/Eirene/Points/Points_corrected/057B30... 057B30G2 410.232665 \n",
+ "0 /Volumes/Eirene/Points/Points_corrected/057B30... 057B30G2 560.002364 \n",
+ "0 /Volumes/Eirene/Points/Points_corrected/057B30... 057B30G2 551.808454 \n",
+ "0 /Volumes/Eirene/Points/Points_corrected/057B30... 057B30G2 409.335017 \n",
+ ".. ... ... ... \n",
+ "0 /Volumes/Eirene/Points/20240502_Points/053B41G... 053B41G2 1154.695562 \n",
+ "0 /Volumes/Eirene/Points/20240502_Points/053B41G... 053B41G2 1195.423555 \n",
+ "0 /Volumes/Eirene/Points/20240502_Points/053B41G... 053B41G2 853.283949 \n",
+ "0 /Volumes/Eirene/Points/20240502_Points/053B41G... 053B41G2 876.020535 \n",
+ "0 /Volumes/Eirene/Points/20240502_Points/053B41G... 053B41G2 909.959512 \n",
+ "\n",
+ " y_nm z_nm voxel_size \n",
+ "0 1314.873257 108.992062 9.62 \n",
+ "0 1334.406249 117.338149 9.62 \n",
+ "0 1406.025222 99.931734 9.62 \n",
+ "0 1429.227718 113.049751 9.62 \n",
+ "0 760.494224 128.397068 9.62 \n",
+ ".. ... ... ... \n",
+ "0 540.231809 201.331114 9.62 \n",
+ "0 522.671022 216.943064 9.62 \n",
+ "0 442.298812 163.940000 9.62 \n",
+ "0 413.088015 154.325299 9.62 \n",
+ "0 388.955666 141.457140 9.62 \n",
+ "\n",
+ "[548 rows x 14 columns]"
+ ]
+ },
+ "execution_count": 242,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Extract info from files\n",
+ "\n",
+ "dfs = []\n",
+ "\n",
+ "def extract_ts_id_from_fn(fn):\n",
+ " return int(fn.split('_')[2])\n",
+ "\n",
+ "log_msgs = []\n",
+ "\n",
+ "for base_dir in base_dirs:\n",
+ " for ds_dir in [d for d in os.listdir(base_dir) if d.startswith('0')]:\n",
+ "\n",
+ " ds_path = os.path.join(base_dir, ds_dir)\n",
+ " print(ds_path)\n",
+ " fns = [fn for fn in os.listdir(ds_path) if fn.startswith(ds_dir) and fn.endswith('.mrc')]\n",
+ " for fn in fns:\n",
+ "\n",
+ " root_name = fn.split('rec_corrected.mrc')[0]\n",
+ "\n",
+ " # t3ss_name = root_name + 'T3SS.mod'\n",
+ " # t3ss_path = os.path.join(base_dir, ds_dir, t3ss_name)\n",
+ "\n",
+ " t3ss_paths = [os.path.join(ds_path, f) for f in os.listdir(ds_path) if f.startswith(root_name) and f.endswith('T3SS.mod')]\n",
+ " # if not os.path.exists(t3ss_path):\n",
+ " if not len(t3ss_paths):\n",
+ " msg = 'No T3SS model found for {}, {}'.format(fn, t3ss_path)\n",
+ " log_msgs.append(msg)\n",
+ " print(msg)\n",
+ " continue\n",
+ " t3ss_path = t3ss_paths[0]\n",
+ " t3ss_name = os.path.basename(t3ss_path)\n",
+ "\n",
+ " breaks_name = root_name + 'break.mod'\n",
+ " breaks_path = os.path.join(base_dir, ds_dir, breaks_name)\n",
+ "\n",
+ " tdf = imodmodel.read(t3ss_path)\n",
+ " tdf['source_fn'] = t3ss_name\n",
+ " tdf['type'] = 'T3SS'\n",
+ "\n",
+ " if os.path.exists(breaks_path):\n",
+ " bdf = imodmodel.read(breaks_path)\n",
+ " bdf['source_fn'] = breaks_name\n",
+ " bdf['type'] = 'break'\n",
+ " cdf = pd.concat([tdf, bdf])\n",
+ " else:\n",
+ " cdf = tdf\n",
+ " msg = 'No breaks model found for {}'.format(fn)\n",
+ " log_msgs.append(msg)\n",
+ " print(msg)\n",
+ "\n",
+ " cdf['tomo_id'] = extract_ts_id_from_fn(fn)\n",
+ " cdf['tomo_fn'] = os.path.join(ds_path, fn)\n",
+ " cdf['ds'] = ds_dir\n",
+ "\n",
+ " cdf['contour_id'] = cdf['contour_id'].astype(int)\n",
+ " cdf['object_id'] = cdf['object_id'].astype(int)\n",
+ "\n",
+ " # multiply with voxel size and convert to nm\n",
+ " voxel_size = mrcfile.mmap(os.path.join(base_dir, ds_dir, fn), mode='r+').voxel_size.x\n",
+ " for dim in ['x', 'y', 'z']:\n",
+ " cdf[dim+'_nm'] = cdf[dim] * voxel_size / 10\n",
+ "\n",
+ " cdf['voxel_size'] = voxel_size\n",
+ "\n",
+ " dfs.append(cdf)\n",
+ "\n",
+ "df = pd.concat(dfs)\n",
+ "df\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 244,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# keep only T3SS\n",
+ "df = df.set_index(['ds', 'tomo_id', 'object_id', 'contour_id'], inplace=False)\n",
+ "df = df[df['type'] == 'T3SS']\n",
+ "\n",
+ "# keep only needles that have a contour 2\n",
+ "df['has_contour_2'] = df.groupby(['ds', 'tomo_id', 'object_id']).apply(lambda x: x.reset_index()['contour_id'].max() > 1)\n",
+ "df = df[df['has_contour_2'] == True]\n",
+ "\n",
+ "df = df.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 246,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead tr th {\n",
+ " text-align: left;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <th>ds</th>\n",
+ " <th>tomo_id</th>\n",
+ " <th>object_id</th>\n",
+ " <th>voxel_size</th>\n",
+ " <th>source_fn</th>\n",
+ " <th>tomo_fn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">x</th>\n",
+ " <th colspan=\"4\" halign=\"left\">y</th>\n",
+ " <th colspan=\"2\" halign=\"left\">z</th>\n",
+ " <th colspan=\"2\" halign=\"left\">x</th>\n",
+ " <th colspan=\"2\" halign=\"left\">y</th>\n",
+ " <th colspan=\"2\" halign=\"left\">z</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>contour_id</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>0</th>\n",
+ " <th>1</th>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <th>...</th>\n",
+ " <th>12</th>\n",
+ " <th>10</th>\n",
+ " <th>12</th>\n",
+ " <th>10</th>\n",
+ " <th>12_t</th>\n",
+ " <th>10_t</th>\n",
+ " <th>12_t</th>\n",
+ " <th>10_t</th>\n",
+ " <th>12_t</th>\n",
+ " <th>10_t</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>053B40G2</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>9.311999</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
+ " <td>728.589233</td>\n",
+ " <td>714.438110</td>\n",
+ " <td>659.801331</td>\n",
+ " <td>368.882446</td>\n",
+ " <td>...</td>\n",
+ " <td>-34.701508</td>\n",
+ " <td>9.379944</td>\n",
+ " <td>0.744720</td>\n",
+ " <td>14.999207</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>-0.104016</td>\n",
+ " <td>-64.729645</td>\n",
+ " <td>16.800645</td>\n",
+ " <td>-2.220446e-16</td>\n",
+ " <td>28.340858</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>053B40G2</td>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>9.311999</td>\n",
+ " <td>053B40G2_TS_05_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
+ " <td>693.882324</td>\n",
+ " <td>684.748718</td>\n",
+ " <td>658.552246</td>\n",
+ " <td>1216.467163</td>\n",
+ " <td>...</td>\n",
+ " <td>32.204224</td>\n",
+ " <td>-18.961182</td>\n",
+ " <td>-20.806656</td>\n",
+ " <td>16.376190</td>\n",
+ " <td>-3.552714e-15</td>\n",
+ " <td>-7.820059</td>\n",
+ " <td>-46.435806</td>\n",
+ " <td>25.640389</td>\n",
+ " <td>7.105427e-15</td>\n",
+ " <td>-4.196947</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>053B40G2</td>\n",
+ " <td>5</td>\n",
+ " <td>3</td>\n",
+ " <td>9.311999</td>\n",
+ " <td>053B40G2_TS_05_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
+ " <td>1136.781128</td>\n",
+ " <td>1166.509277</td>\n",
+ " <td>1194.055298</td>\n",
+ " <td>546.148804</td>\n",
+ " <td>...</td>\n",
+ " <td>-8.783081</td>\n",
+ " <td>13.524414</td>\n",
+ " <td>1.146484</td>\n",
+ " <td>6.916412</td>\n",
+ " <td>-7.105427e-15</td>\n",
+ " <td>9.193258</td>\n",
+ " <td>-28.935103</td>\n",
+ " <td>32.132206</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>28.122263</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>053B40G2</td>\n",
+ " <td>8</td>\n",
+ " <td>0</td>\n",
+ " <td>9.311999</td>\n",
+ " <td>053B40G2_TS_08_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
+ " <td>659.922302</td>\n",
+ " <td>632.182800</td>\n",
+ " <td>621.798279</td>\n",
+ " <td>811.559875</td>\n",
+ " <td>...</td>\n",
+ " <td>-2.920349</td>\n",
+ " <td>5.416809</td>\n",
+ " <td>-1.805672</td>\n",
+ " <td>0.898895</td>\n",
+ " <td>-8.881784e-16</td>\n",
+ " <td>12.375231</td>\n",
+ " <td>-10.937421</td>\n",
+ " <td>27.931954</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>-6.639047</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>053B40G2</td>\n",
+ " <td>18</td>\n",
+ " <td>0</td>\n",
+ " <td>9.311999</td>\n",
+ " <td>053B40G2_TS_18_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
+ " <td>666.500000</td>\n",
+ " <td>660.333313</td>\n",
+ " <td>651.250000</td>\n",
+ " <td>1190.425171</td>\n",
+ " <td>...</td>\n",
+ " <td>61.833374</td>\n",
+ " <td>-31.241455</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.957275</td>\n",
+ " <td>1.776357e-15</td>\n",
+ " <td>1.577322</td>\n",
+ " <td>-62.496982</td>\n",
+ " <td>31.805992</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>-2.989013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109</th>\n",
+ " <td>060B37G4</td>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>9.620000</td>\n",
+ " <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
+ " <td>1815.878540</td>\n",
+ " <td>1825.607788</td>\n",
+ " <td>1856.567627</td>\n",
+ " <td>1285.116211</td>\n",
+ " <td>...</td>\n",
+ " <td>43.918091</td>\n",
+ " <td>-27.732544</td>\n",
+ " <td>-33.255699</td>\n",
+ " <td>18.096863</td>\n",
+ " <td>-3.552714e-15</td>\n",
+ " <td>8.835842</td>\n",
+ " <td>-63.192182</td>\n",
+ " <td>33.564281</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>2.654621</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>110</th>\n",
+ " <td>060B37G4</td>\n",
+ " <td>10</td>\n",
+ " <td>0</td>\n",
+ " <td>9.620000</td>\n",
+ " <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
+ " <td>1130.231934</td>\n",
+ " <td>1110.025513</td>\n",
+ " <td>1072.568115</td>\n",
+ " <td>1364.009277</td>\n",
+ " <td>...</td>\n",
+ " <td>40.578247</td>\n",
+ " <td>-23.065186</td>\n",
+ " <td>7.318817</td>\n",
+ " <td>-0.768005</td>\n",
+ " <td>-7.105427e-15</td>\n",
+ " <td>-0.537872</td>\n",
+ " <td>-55.706515</td>\n",
+ " <td>30.489174</td>\n",
+ " <td>-1.776357e-15</td>\n",
+ " <td>-4.510022</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>111</th>\n",
+ " <td>060B37G4</td>\n",
+ " <td>10</td>\n",
+ " <td>1</td>\n",
+ " <td>9.620000</td>\n",
+ " <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
+ " <td>1418.198730</td>\n",
+ " <td>1404.029663</td>\n",
+ " <td>1387.274658</td>\n",
+ " <td>1547.150391</td>\n",
+ " <td>...</td>\n",
+ " <td>44.911133</td>\n",
+ " <td>-24.726318</td>\n",
+ " <td>7.034332</td>\n",
+ " <td>-4.091049</td>\n",
+ " <td>3.552714e-15</td>\n",
+ " <td>4.931969</td>\n",
+ " <td>-48.448136</td>\n",
+ " <td>28.415284</td>\n",
+ " <td>1.776357e-15</td>\n",
+ " <td>0.229769</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>112</th>\n",
+ " <td>060B37G4</td>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>9.620000</td>\n",
+ " <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
+ " <td>676.623718</td>\n",
+ " <td>647.250366</td>\n",
+ " <td>597.244324</td>\n",
+ " <td>589.625671</td>\n",
+ " <td>...</td>\n",
+ " <td>15.999146</td>\n",
+ " <td>-9.414612</td>\n",
+ " <td>10.191650</td>\n",
+ " <td>-5.982864</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>-0.032102</td>\n",
+ " <td>-53.483144</td>\n",
+ " <td>31.420107</td>\n",
+ " <td>-3.552714e-15</td>\n",
+ " <td>-0.034142</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>113</th>\n",
+ " <td>060B37G4</td>\n",
+ " <td>10</td>\n",
+ " <td>3</td>\n",
+ " <td>9.620000</td>\n",
+ " <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+ " <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
+ " <td>1515.624390</td>\n",
+ " <td>1546.256470</td>\n",
+ " <td>1601.316040</td>\n",
+ " <td>477.224579</td>\n",
+ " <td>...</td>\n",
+ " <td>12.226410</td>\n",
+ " <td>-2.622223</td>\n",
+ " <td>21.913696</td>\n",
+ " <td>-10.990875</td>\n",
+ " <td>1.776357e-15</td>\n",
+ " <td>-6.769159</td>\n",
+ " <td>-60.508276</td>\n",
+ " <td>32.384004</td>\n",
+ " <td>-3.552714e-15</td>\n",
+ " <td>7.390998</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>114 rows × 27 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ds tomo_id object_id voxel_size \\\n",
+ "contour_id \n",
+ "0 053B40G2 4 0 9.311999 \n",
+ "1 053B40G2 5 1 9.311999 \n",
+ "2 053B40G2 5 3 9.311999 \n",
+ "3 053B40G2 8 0 9.311999 \n",
+ "4 053B40G2 18 0 9.311999 \n",
+ ".. ... ... ... ... \n",
+ "109 060B37G4 9 2 9.620000 \n",
+ "110 060B37G4 10 0 9.620000 \n",
+ "111 060B37G4 10 1 9.620000 \n",
+ "112 060B37G4 10 2 9.620000 \n",
+ "113 060B37G4 10 3 9.620000 \n",
+ "\n",
+ " source_fn \\\n",
+ "contour_id \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod \n",
+ "1 053B40G2_TS_05_bin3_tiltcor_T3SS.mod \n",
+ "2 053B40G2_TS_05_bin3_tiltcor_T3SS.mod \n",
+ "3 053B40G2_TS_08_bin3_tiltcor_T3SS.mod \n",
+ "4 053B40G2_TS_18_bin3_tiltcor_T3SS.mod \n",
+ ".. ... \n",
+ "109 060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS... \n",
+ "110 060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS... \n",
+ "111 060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS... \n",
+ "112 060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS... \n",
+ "113 060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS... \n",
+ "\n",
+ " tomo_fn x \\\n",
+ "contour_id 0 \n",
+ "0 /Volumes/Eirene/Points/Points_corrected/053B40... 728.589233 \n",
+ "1 /Volumes/Eirene/Points/Points_corrected/053B40... 693.882324 \n",
+ "2 /Volumes/Eirene/Points/Points_corrected/053B40... 1136.781128 \n",
+ "3 /Volumes/Eirene/Points/Points_corrected/053B40... 659.922302 \n",
+ "4 /Volumes/Eirene/Points/Points_corrected/053B40... 666.500000 \n",
+ ".. ... ... \n",
+ "109 /Volumes/Eirene/Points/20240502_Points/060B37G... 1815.878540 \n",
+ "110 /Volumes/Eirene/Points/20240502_Points/060B37G... 1130.231934 \n",
+ "111 /Volumes/Eirene/Points/20240502_Points/060B37G... 1418.198730 \n",
+ "112 /Volumes/Eirene/Points/20240502_Points/060B37G... 676.623718 \n",
+ "113 /Volumes/Eirene/Points/20240502_Points/060B37G... 1515.624390 \n",
+ "\n",
+ " y ... \\\n",
+ "contour_id 1 2 0 ... 12 10 \n",
+ "0 714.438110 659.801331 368.882446 ... -34.701508 9.379944 \n",
+ "1 684.748718 658.552246 1216.467163 ... 32.204224 -18.961182 \n",
+ "2 1166.509277 1194.055298 546.148804 ... -8.783081 13.524414 \n",
+ "3 632.182800 621.798279 811.559875 ... -2.920349 5.416809 \n",
+ "4 660.333313 651.250000 1190.425171 ... 61.833374 -31.241455 \n",
+ ".. ... ... ... ... ... ... \n",
+ "109 1825.607788 1856.567627 1285.116211 ... 43.918091 -27.732544 \n",
+ "110 1110.025513 1072.568115 1364.009277 ... 40.578247 -23.065186 \n",
+ "111 1404.029663 1387.274658 1547.150391 ... 44.911133 -24.726318 \n",
+ "112 647.250366 597.244324 589.625671 ... 15.999146 -9.414612 \n",
+ "113 1546.256470 1601.316040 477.224579 ... 12.226410 -2.622223 \n",
+ "\n",
+ " z x y \\\n",
+ "contour_id 12 10 12_t 10_t 12_t \n",
+ "0 0.744720 14.999207 0.000000e+00 -0.104016 -64.729645 \n",
+ "1 -20.806656 16.376190 -3.552714e-15 -7.820059 -46.435806 \n",
+ "2 1.146484 6.916412 -7.105427e-15 9.193258 -28.935103 \n",
+ "3 -1.805672 0.898895 -8.881784e-16 12.375231 -10.937421 \n",
+ "4 0.000000 2.957275 1.776357e-15 1.577322 -62.496982 \n",
+ ".. ... ... ... ... ... \n",
+ "109 -33.255699 18.096863 -3.552714e-15 8.835842 -63.192182 \n",
+ "110 7.318817 -0.768005 -7.105427e-15 -0.537872 -55.706515 \n",
+ "111 7.034332 -4.091049 3.552714e-15 4.931969 -48.448136 \n",
+ "112 10.191650 -5.982864 0.000000e+00 -0.032102 -53.483144 \n",
+ "113 21.913696 -10.990875 1.776357e-15 -6.769159 -60.508276 \n",
+ "\n",
+ " z \n",
+ "contour_id 10_t 12_t 10_t \n",
+ "0 16.800645 -2.220446e-16 28.340858 \n",
+ "1 25.640389 7.105427e-15 -4.196947 \n",
+ "2 32.132206 0.000000e+00 28.122263 \n",
+ "3 27.931954 0.000000e+00 -6.639047 \n",
+ "4 31.805992 0.000000e+00 -2.989013 \n",
+ ".. ... ... ... \n",
+ "109 33.564281 0.000000e+00 2.654621 \n",
+ "110 30.489174 -1.776357e-15 -4.510022 \n",
+ "111 28.415284 1.776357e-15 0.229769 \n",
+ "112 31.420107 -3.552714e-15 -0.034142 \n",
+ "113 32.384004 -3.552714e-15 7.390998 \n",
+ "\n",
+ "[114 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 246,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "coord_cols = ['x', 'y', 'z']\n",
+ "\n",
+ "df = df.pivot_table(index=['ds', 'tomo_id', 'object_id', 'voxel_size', 'source_fn', 'tomo_fn'], columns='contour_id', values=coord_cols).reset_index()\n",
+ "\n",
+ "for coord_col in coord_cols:\n",
+ " df[coord_col, '12'] = df[coord_col, 2] - df[coord_col, 1]\n",
+ " df[coord_col, '10'] = df[coord_col, 0] - df[coord_col, 1]\n",
+ "\n",
+ "# Calculate the projection of the needle onto the transformed substacks\n",
+ "\n",
+ "coord_cols = ['x', 'y', 'z']\n",
+ "\n",
+ "v12 = np.array([df[cc, '12'] for cc in coord_cols]).T\n",
+ "v10 = np.array([df[cc, '10'] for cc in coord_cols]).T\n",
+ "v12_norm = (v12.T / np.linalg.norm(v12, axis=1)).T\n",
+ "\n",
+ "proj = np.array([np.eye(4)] * len(df))\n",
+ "\n",
+ "proj[:, :3, 1] = -v12_norm\n",
+ "proj[:, :3, 2] = np.cross(v12_norm, [1, 0, 0], axisa=1)\n",
+ "proj[:, :3, 0] = np.cross(proj[:, :3, 1], proj[:, :3, 2], axisa=1, axisb=1)\n",
+ "\n",
+ "v12_t = np.array([\n",
+ " np.dot(np.linalg.inv(proj_el), np.concatenate([v, [0]]))[:3] for proj_el, v in zip(proj, v12)\n",
+ "])\n",
+ "v10_t = np.array([\n",
+ " np.dot(np.linalg.inv(proj_el), np.concatenate([v, [0]]))[:3] for proj_el, v in zip(proj, v10)\n",
+ "])\n",
+ "\n",
+ "for icc, coord_col in enumerate(coord_cols):\n",
+ " df[coord_col, '12_t'] = v12_t[:, icc]\n",
+ " df[coord_col, '10_t'] = v10_t[:, icc]\n",
+ "\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 247,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save coordinate table\n",
+ "\n",
+ "outdir = \"/Volumes/Eirene/Points/extracted_images\"\n",
+ "\n",
+ "df.to_csv(os.path.join(outdir, 'T3SS_coordinates.csv'), index=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 176,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1, 322, 322)\n",
+ "(322, 322, 322)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# produce substacks\n",
+ "\n",
+ "from scipy import ndimage\n",
+ "from skimage.transform import EuclideanTransform\n",
+ "\n",
+ "for irow, (index, row) in enumerate(df.iterrows()):\n",
+ " if irow != 1:\n",
+ " continue\n",
+ " \n",
+ " # im = np.array(mrcfile.read(row['tomo_fn','']).data)\n",
+ " pos = np.array([row[coord_col, 1] for coord_col in coord_cols[::-1]])\n",
+ " n = np.array([row[coord_col, '12'] for coord_col in coord_cols[::-1]])\n",
+ " v10 = np.array([row[coord_col, '10'] for coord_col in coord_cols[::-1]])\n",
+ "\n",
+ " voxel_size = row['voxel_size','']\n",
+ "\n",
+ " # phys in nm\n",
+ " c_phys = pos\n",
+ " R_phys = 150\n",
+ " n_phys = n * voxel_size / 10\n",
+ "\n",
+ " # pixel coords\n",
+ " c = c_phys\n",
+ " R = R_phys * 10 / voxel_size\n",
+ "\n",
+ " # normalize n\n",
+ " n_norm = n / np.linalg.norm(n)\n",
+ "\n",
+ " proj = np.eye(4)\n",
+ "\n",
+ " proj[:3, 1] = -n_norm\n",
+ " proj[:3, 2] = np.cross(n_norm, [1, 0, 0])\n",
+ " proj[:3, 0] = np.cross(proj[:3, 1], proj[:3, 2])\n",
+ "\n",
+ " # output_shape = [int(2*R)] * 3\n",
+ " output_shape = [1] + [int(2*R)] * 2\n",
+ " output_shape_3d = [int(2*R)] * 3\n",
+ " output_shape0 = [1] + [int(2*R)] * 2\n",
+ " output_shape0_3d = [int(2*R)] * 3\n",
+ "\n",
+ " p = EuclideanTransform(translation=[c[0], c[1], c[2]]).params @ proj @ EuclideanTransform(translation=[-s / 2. for s in output_shape]).params\n",
+ " p0 = EuclideanTransform(translation=c).params @ EuclideanTransform(translation=[-s / 2. for s in output_shape]).params\n",
+ "\n",
+ " tims = []\n",
+ " for t, d3 in zip(\n",
+ " [True] * 2 + [False] * 2,\n",
+ " [False, True, False, True],\n",
+ " ):\n",
+ " if d3:\n",
+ " output_shape = [int(2*R)] * 3\n",
+ " else:\n",
+ " output_shape = [1] + [int(2*R)] * 2\n",
+ "\n",
+ " if t:\n",
+ " param = EuclideanTransform(translation=[c[0], c[1], c[2]]).params @ proj @ EuclideanTransform(translation=[-s / 2. for s in output_shape]).params\n",
+ " else:\n",
+ " param = EuclideanTransform(translation=c).params @ EuclideanTransform(translation=[-s / 2. for s in output_shape]).params\n",
+ "\n",
+ " tmp = ndimage.affine_transform(im, param, output_shape=output_shape, order=1)\n",
+ "\n",
+ " # mark center\n",
+ " tmp[tuple([s//2 for s in output_shape])] = 5\n",
+ " if not t:\n",
+ " if d3:\n",
+ " tmp[tuple([int(s/2. + n[i]) for i, s in enumerate(output_shape)])] = 5\n",
+ " print(tmp.shape)\n",
+ " else:\n",
+ " if d3:\n",
+ " n_t = np.dot(np.linalg.inv(proj), np.concatenate([n, [0]]))[:3]\n",
+ " tmp[tuple([int(s/2. + n_t[i]) for i, s in enumerate(output_shape)])] = 5\n",
+ " v10_t = np.dot(np.linalg.inv(proj), np.concatenate([v10, [0]]))[:3]\n",
+ " tmp[tuple([int(s/2. + v10_t[i]) for i, s in enumerate(output_shape)])] = 5\n",
+ "\n",
+ " out_filename = f\"{row.ds}_tomo_{row.tomo_id:02d}_id_{row.object_id:02d}_{['original', 'transformed'][int(t)]}_{['2D', '3D'][int(d3)]}.tif\"\n",
+ " if not os.path.exists(outdir):\n",
+ " os.makedirs(outdir)\n",
+ "\n",
+ " import tifffile\n",
+ " tifffile.imwrite(os.path.join(outdir, out_filename), tmp)\n",
+ "\n",
+ " tims.append(tmp)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "needles",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/t3ss_geometry.ipynb b/notebooks/t3ss_geometry.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4dd71e50c9c310a94b4ad27364f323718c72cc23
--- /dev/null
+++ b/notebooks/t3ss_geometry.ipynb
@@ -0,0 +1,1295 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# T3SS geometry\n",
+ "\n",
+ "This notebook is used to measure the geometry of T3SS from three manually determined: 0 (close to inner membrane), 1 (close to OM) and 2(outside of the cell, not always present).\n",
+ "\n",
+ "The input to this notebook is an IMOD .mod file and the corresponding tomograms.\n",
+ "\n",
+ "The output is a table containing the measurements.\n",
+ "\n",
+ "## Description\n",
+ "\n",
+ "Measurements in nm and degrees\n",
+ "1) distance 1<->2\n",
+ "2) distance 2<->3 (only if 3 exists within the same object)\n",
+ "3) angle between 21 and 23\n",
+ "4) for every 3 the closest membrane break point\n",
+ "\n",
+ "5) distances between all 2\n",
+ "\n",
+ "Perform measurements for\n",
+ "- each dataset\n",
+ "- each T3SS\n",
+ "\n",
+ "Output is a table containing\n",
+ "- dataset\n",
+ "- needle number\n",
+ "- measurements\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Software environment\n",
+ "Use this notebook with a conda env:\n",
+ "\n",
+ "- `conda create -n t3ss_geo python=3.10`\n",
+ "- `conda activate t3ss_geo`\n",
+ "- `pip install mrcfile pandas imodmodel ipython jupyter matplotlib seaborn ipympl scipy xarray`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import imodmodel\n",
+ "import mrcfile\n",
+ "from scipy import spatial\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "base_dir = '/Volumes/Eirene/Points/Points_corrected'\n",
+ "output_dir = os.path.join(base_dir, 'Marvin_test', 'points_measurements')\n",
+ "\n",
+ "ds_dirs = [d for d in os.listdir(base_dir) if d.startswith('0')]\n",
+ "\n",
+ "if not os.path.exists(output_dir):\n",
+ " os.makedirs(output_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No T3SS model found for 057B30G2_TS_26_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 057B30G2_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 057B30G2_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 057B30G2_TS_29_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 057B30G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 054B36G1_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 054B36G1_TS_03_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 054B36G1_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
+ "No T3SS model found for 054B36G1_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B40G2_TS_22_bin3_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B40G2_TS_08_bin3_tiltcor_rec_corrected.mrc\n",
+ "No breaks model found for 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>object_id</th>\n",
+ " <th>contour_id</th>\n",
+ " <th>x</th>\n",
+ " <th>y</th>\n",
+ " <th>z</th>\n",
+ " <th>source_fn</th>\n",
+ " <th>type</th>\n",
+ " <th>tomo_id</th>\n",
+ " <th>tomo_fn</th>\n",
+ " <th>ds</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>430.220528</td>\n",
+ " <td>1314.873257</td>\n",
+ " <td>108.992062</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>057B30G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>410.232665</td>\n",
+ " <td>1334.406249</td>\n",
+ " <td>117.338149</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>057B30G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>560.002364</td>\n",
+ " <td>1406.025222</td>\n",
+ " <td>99.931734</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>057B30G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>551.808454</td>\n",
+ " <td>1429.227718</td>\n",
+ " <td>113.049751</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>057B30G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>409.335017</td>\n",
+ " <td>760.494224</td>\n",
+ " <td>128.397068</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>16</td>\n",
+ " <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>057B30G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " <td>863.843156</td>\n",
+ " <td>1125.199956</td>\n",
+ " <td>27.004798</td>\n",
+ " <td>053B40G2_TS_18_bin3_tiltcor_break.mod</td>\n",
+ " <td>break</td>\n",
+ " <td>18</td>\n",
+ " <td>053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>053B40G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>10</td>\n",
+ " <td>856.827783</td>\n",
+ " <td>1128.298310</td>\n",
+ " <td>28.606712</td>\n",
+ " <td>053B40G2_TS_18_bin3_tiltcor_break.mod</td>\n",
+ " <td>break</td>\n",
+ " <td>18</td>\n",
+ " <td>053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>053B40G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>678.462245</td>\n",
+ " <td>343.503309</td>\n",
+ " <td>100.492182</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>4</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>053B40G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>665.284720</td>\n",
+ " <td>334.768706</td>\n",
+ " <td>86.524922</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>4</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>053B40G2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>614.406954</td>\n",
+ " <td>302.454664</td>\n",
+ " <td>87.218406</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+ " <td>T3SS</td>\n",
+ " <td>4</td>\n",
+ " <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
+ " <td>053B40G2</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>299 rows × 10 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " object_id contour_id x y z \\\n",
+ "0 0 0 430.220528 1314.873257 108.992062 \n",
+ "0 0 1 410.232665 1334.406249 117.338149 \n",
+ "0 1 0 560.002364 1406.025222 99.931734 \n",
+ "0 1 1 551.808454 1429.227718 113.049751 \n",
+ "0 2 0 409.335017 760.494224 128.397068 \n",
+ ".. ... ... ... ... ... \n",
+ "0 0 9 863.843156 1125.199956 27.004798 \n",
+ "0 0 10 856.827783 1128.298310 28.606712 \n",
+ "0 0 0 678.462245 343.503309 100.492182 \n",
+ "0 0 1 665.284720 334.768706 86.524922 \n",
+ "0 0 2 614.406954 302.454664 87.218406 \n",
+ "\n",
+ " source_fn type tomo_id \\\n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_T3SS.mod T3SS 16 \n",
+ ".. ... ... ... \n",
+ "0 053B40G2_TS_18_bin3_tiltcor_break.mod break 18 \n",
+ "0 053B40G2_TS_18_bin3_tiltcor_break.mod break 18 \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod T3SS 4 \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod T3SS 4 \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_T3SS.mod T3SS 4 \n",
+ "\n",
+ " tomo_fn ds \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
+ "0 057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc 057B30G2 \n",
+ ".. ... ... \n",
+ "0 053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
+ "0 053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
+ "0 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc 053B40G2 \n",
+ "\n",
+ "[299 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Extract info from files\n",
+ "\n",
+ "dfs = []\n",
+ "\n",
+ "def extract_ts_id_from_fn(fn):\n",
+ " return int(fn.split('_')[2])\n",
+ "\n",
+ "log_msgs = []\n",
+ "\n",
+ "for ds_dir in ds_dirs[:]:\n",
+ " ds_path = os.path.join(base_dir, ds_dir)\n",
+ " fns = [fn for fn in os.listdir(ds_path) if fn.startswith(ds_dir) and fn.endswith('.mrc')]\n",
+ " for fn in fns:\n",
+ "\n",
+ " root_name = fn.split('rec_corrected.mrc')[0]\n",
+ "\n",
+ " t3ss_name = root_name + 'T3SS.mod'\n",
+ " t3ss_path = os.path.join(base_dir, ds_dir, t3ss_name)\n",
+ "\n",
+ " breaks_name = root_name + 'break.mod'\n",
+ " breaks_path = os.path.join(base_dir, ds_dir, breaks_name)\n",
+ "\n",
+ " if not os.path.exists(t3ss_path):\n",
+ " msg = 'No T3SS model found for {}'.format(fn)\n",
+ " log_msgs.append(msg)\n",
+ " print(msg)\n",
+ " continue\n",
+ "\n",
+ " tdf = imodmodel.read(t3ss_path)\n",
+ " tdf['source_fn'] = t3ss_name\n",
+ " tdf['type'] = 'T3SS'\n",
+ "\n",
+ " if os.path.exists(breaks_path):\n",
+ " bdf = imodmodel.read(breaks_path)\n",
+ " bdf['source_fn'] = breaks_name\n",
+ " bdf['type'] = 'break'\n",
+ " cdf = pd.concat([tdf, bdf])\n",
+ " else:\n",
+ " cdf = tdf\n",
+ " msg = 'No breaks model found for {}'.format(fn)\n",
+ " log_msgs.append(msg)\n",
+ " print(msg)\n",
+ "\n",
+ " cdf['tomo_id'] = extract_ts_id_from_fn(fn)\n",
+ " cdf['tomo_fn'] = fn\n",
+ " cdf['ds'] = ds_dir\n",
+ "\n",
+ " cdf['contour_id'] = cdf['contour_id'].astype(int)\n",
+ " cdf['object_id'] = cdf['object_id'].astype(int)\n",
+ "\n",
+ " # multiply with voxel size and convert to nm\n",
+ " voxel_size = mrcfile.mmap(os.path.join(base_dir, ds_dir, fn), mode='r+').voxel_size.x\n",
+ " for dim in ['x', 'y', 'z']:\n",
+ " cdf[dim] = cdf[dim] * voxel_size / 10\n",
+ "\n",
+ " dfs.append(cdf)\n",
+ "\n",
+ "df = pd.concat(dfs)\n",
+ "df\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>distance_1_2</th>\n",
+ " <th>distance_2_3</th>\n",
+ " <th>angle_21_23</th>\n",
+ " <th>distance_to_closest_break</th>\n",
+ " <th>closest_break_contour_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>ds</th>\n",
+ " <th>tomo_id</th>\n",
+ " <th>object_id</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"14\" valign=\"top\">053B40G2</th>\n",
+ " <th>4</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>21.095611</td>\n",
+ " <td>60.276241</td>\n",
+ " <td>137.868919</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"4\" valign=\"top\">5</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.502676</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>24.832307</td>\n",
+ " <td>43.241020</td>\n",
+ " <td>164.050163</td>\n",
+ " <td>10.670890</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>30.172572</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>0</th>\n",
+ " <td>31.087429</td>\n",
+ " <td>26.944366</td>\n",
+ " <td>164.258483</td>\n",
+ " <td>603.879757</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>26.332217</td>\n",
+ " <td>10.184926</td>\n",
+ " <td>171.030794</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"4\" valign=\"top\">18</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.780962</td>\n",
+ " <td>58.197185</td>\n",
+ " <td>173.998510</td>\n",
+ " <td>3.937672</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>29.499533</td>\n",
+ " <td>49.947940</td>\n",
+ " <td>173.269796</td>\n",
+ " <td>3.776994</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>27.753017</td>\n",
+ " <td>59.075236</td>\n",
+ " <td>171.329462</td>\n",
+ " <td>5.644370</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>0</th>\n",
+ " <td>26.119723</td>\n",
+ " <td>52.913860</td>\n",
+ " <td>175.469543</td>\n",
+ " <td>1040.640748</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">20</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>27.564635</td>\n",
+ " <td>51.528415</td>\n",
+ " <td>171.741981</td>\n",
+ " <td>5.901630</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>29.986906</td>\n",
+ " <td>54.243535</td>\n",
+ " <td>173.332289</td>\n",
+ " <td>1091.114905</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">22</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.095342</td>\n",
+ " <td>65.611610</td>\n",
+ " <td>167.552579</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>34.082424</td>\n",
+ " <td>45.047355</td>\n",
+ " <td>172.796835</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"21\" valign=\"top\">054B36G1</th>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>28.860724</td>\n",
+ " <td>50.738282</td>\n",
+ " <td>169.974732</td>\n",
+ " <td>13.976631</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>32.918810</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"7\" valign=\"top\">7</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>28.186633</td>\n",
+ " <td>28.223016</td>\n",
+ " <td>154.695752</td>\n",
+ " <td>129.037390</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>28.329459</td>\n",
+ " <td>48.747746</td>\n",
+ " <td>162.660341</td>\n",
+ " <td>9.551492</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>30.429561</td>\n",
+ " <td>47.480343</td>\n",
+ " <td>160.759520</td>\n",
+ " <td>23.269485</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>0</th>\n",
+ " <td>31.647358</td>\n",
+ " <td>45.451366</td>\n",
+ " <td>166.210177</td>\n",
+ " <td>7.781381</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <th>0</th>\n",
+ " <td>26.638607</td>\n",
+ " <td>48.631320</td>\n",
+ " <td>164.303959</td>\n",
+ " <td>16.315059</td>\n",
+ " <td>46</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <th>0</th>\n",
+ " <td>30.272416</td>\n",
+ " <td>53.902126</td>\n",
+ " <td>168.519110</td>\n",
+ " <td>97.418625</td>\n",
+ " <td>39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <th>0</th>\n",
+ " <td>65.285148</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"3\" valign=\"top\">11</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>48.298107</td>\n",
+ " <td>55.133918</td>\n",
+ " <td>102.926047</td>\n",
+ " <td>15.566473</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>27.684032</td>\n",
+ " <td>72.729038</td>\n",
+ " <td>152.148465</td>\n",
+ " <td>10.977328</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>30.399087</td>\n",
+ " <td>46.203811</td>\n",
+ " <td>156.695492</td>\n",
+ " <td>188.565914</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">13</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.523980</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>31.098125</td>\n",
+ " <td>67.095690</td>\n",
+ " <td>148.884104</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"7\" valign=\"top\">14</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>32.563100</td>\n",
+ " <td>41.258266</td>\n",
+ " <td>168.134535</td>\n",
+ " <td>152.079839</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>28.940105</td>\n",
+ " <td>27.522238</td>\n",
+ " <td>170.971118</td>\n",
+ " <td>115.028001</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>27.935260</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>0</th>\n",
+ " <td>29.533568</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <th>0</th>\n",
+ " <td>31.274453</td>\n",
+ " <td>41.612607</td>\n",
+ " <td>166.921202</td>\n",
+ " <td>97.473849</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <th>0</th>\n",
+ " <td>28.293434</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <th>0</th>\n",
+ " <td>33.042608</td>\n",
+ " <td>61.780070</td>\n",
+ " <td>174.441777</td>\n",
+ " <td>290.449040</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"17\" valign=\"top\">057B30G2</th>\n",
+ " <th rowspan=\"4\" valign=\"top\">2</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>18.968181</td>\n",
+ " <td>17.610463</td>\n",
+ " <td>166.291669</td>\n",
+ " <td>7.722758</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>28.534100</td>\n",
+ " <td>44.234555</td>\n",
+ " <td>165.797150</td>\n",
+ " <td>58.574351</td>\n",
+ " <td>28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>30.633345</td>\n",
+ " <td>32.004088</td>\n",
+ " <td>166.788177</td>\n",
+ " <td>565.868529</td>\n",
+ " <td>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <th>0</th>\n",
+ " <td>27.835786</td>\n",
+ " <td>43.018873</td>\n",
+ " <td>169.477370</td>\n",
+ " <td>433.225682</td>\n",
+ " <td>31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>28.034553</td>\n",
+ " <td>53.457678</td>\n",
+ " <td>160.674130</td>\n",
+ " <td>1002.391912</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>25.565483</td>\n",
+ " <td>68.222377</td>\n",
+ " <td>160.495419</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">12</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.674439</td>\n",
+ " <td>40.453873</td>\n",
+ " <td>165.654194</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>33.328252</td>\n",
+ " <td>40.899023</td>\n",
+ " <td>173.268236</td>\n",
+ " <td>NaN</td>\n",
+ " <td><NA></td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"3\" valign=\"top\">16</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.166927</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>27.885092</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>32.616679</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>29.180735</td>\n",
+ " <td>30.049136</td>\n",
+ " <td>174.280104</td>\n",
+ " <td>170.293861</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">23</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>31.895192</td>\n",
+ " <td>41.637150</td>\n",
+ " <td>149.514568</td>\n",
+ " <td>8.084435</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>29.498332</td>\n",
+ " <td>50.533080</td>\n",
+ " <td>171.461484</td>\n",
+ " <td>1201.192824</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"3\" valign=\"top\">30</th>\n",
+ " <th>0</th>\n",
+ " <th>0</th>\n",
+ " <td>17.554856</td>\n",
+ " <td>51.042460</td>\n",
+ " <td>168.864374</td>\n",
+ " <td>5.343565</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <th>0</th>\n",
+ " <td>29.404072</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <th>0</th>\n",
+ " <td>32.240981</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " distance_1_2 distance_2_3 angle_21_23 \\\n",
+ "ds tomo_id object_id \n",
+ "053B40G2 4 0 0 21.095611 60.276241 137.868919 \n",
+ " 5 0 0 29.502676 NaN NaN \n",
+ " 1 0 24.832307 43.241020 164.050163 \n",
+ " 2 0 30.172572 NaN NaN \n",
+ " 3 0 31.087429 26.944366 164.258483 \n",
+ " 8 0 0 26.332217 10.184926 171.030794 \n",
+ " 18 0 0 29.780962 58.197185 173.998510 \n",
+ " 1 0 29.499533 49.947940 173.269796 \n",
+ " 2 0 27.753017 59.075236 171.329462 \n",
+ " 3 0 26.119723 52.913860 175.469543 \n",
+ " 20 0 0 27.564635 51.528415 171.741981 \n",
+ " 1 0 29.986906 54.243535 173.332289 \n",
+ " 22 0 0 29.095342 65.611610 167.552579 \n",
+ " 1 0 34.082424 45.047355 172.796835 \n",
+ "054B36G1 2 0 0 28.860724 50.738282 169.974732 \n",
+ " 3 0 0 32.918810 NaN NaN \n",
+ " 7 0 0 28.186633 28.223016 154.695752 \n",
+ " 1 0 28.329459 48.747746 162.660341 \n",
+ " 2 0 30.429561 47.480343 160.759520 \n",
+ " 3 0 31.647358 45.451366 166.210177 \n",
+ " 4 0 26.638607 48.631320 164.303959 \n",
+ " 5 0 30.272416 53.902126 168.519110 \n",
+ " 6 0 65.285148 NaN NaN \n",
+ " 11 0 0 48.298107 55.133918 102.926047 \n",
+ " 1 0 27.684032 72.729038 152.148465 \n",
+ " 2 0 30.399087 46.203811 156.695492 \n",
+ " 13 0 0 29.523980 NaN NaN \n",
+ " 1 0 31.098125 67.095690 148.884104 \n",
+ " 14 0 0 32.563100 41.258266 168.134535 \n",
+ " 1 0 28.940105 27.522238 170.971118 \n",
+ " 2 0 27.935260 NaN NaN \n",
+ " 3 0 29.533568 NaN NaN \n",
+ " 4 0 31.274453 41.612607 166.921202 \n",
+ " 5 0 28.293434 NaN NaN \n",
+ " 6 0 33.042608 61.780070 174.441777 \n",
+ "057B30G2 2 0 0 18.968181 17.610463 166.291669 \n",
+ " 1 0 28.534100 44.234555 165.797150 \n",
+ " 2 0 30.633345 32.004088 166.788177 \n",
+ " 3 0 27.835786 43.018873 169.477370 \n",
+ " 4 0 0 28.034553 53.457678 160.674130 \n",
+ " 10 0 0 25.565483 68.222377 160.495419 \n",
+ " 12 0 0 29.674439 40.453873 165.654194 \n",
+ " 1 0 33.328252 40.899023 173.268236 \n",
+ " 16 0 0 29.166927 NaN NaN \n",
+ " 1 0 27.885092 NaN NaN \n",
+ " 2 0 32.616679 NaN NaN \n",
+ " 20 0 0 29.180735 30.049136 174.280104 \n",
+ " 23 0 0 31.895192 41.637150 149.514568 \n",
+ " 1 0 29.498332 50.533080 171.461484 \n",
+ " 30 0 0 17.554856 51.042460 168.864374 \n",
+ " 1 0 29.404072 NaN NaN \n",
+ " 2 0 32.240981 NaN NaN \n",
+ "\n",
+ " distance_to_closest_break \\\n",
+ "ds tomo_id object_id \n",
+ "053B40G2 4 0 0 NaN \n",
+ " 5 0 0 NaN \n",
+ " 1 0 10.670890 \n",
+ " 2 0 NaN \n",
+ " 3 0 603.879757 \n",
+ " 8 0 0 NaN \n",
+ " 18 0 0 3.937672 \n",
+ " 1 0 3.776994 \n",
+ " 2 0 5.644370 \n",
+ " 3 0 1040.640748 \n",
+ " 20 0 0 5.901630 \n",
+ " 1 0 1091.114905 \n",
+ " 22 0 0 NaN \n",
+ " 1 0 NaN \n",
+ "054B36G1 2 0 0 13.976631 \n",
+ " 3 0 0 NaN \n",
+ " 7 0 0 129.037390 \n",
+ " 1 0 9.551492 \n",
+ " 2 0 23.269485 \n",
+ " 3 0 7.781381 \n",
+ " 4 0 16.315059 \n",
+ " 5 0 97.418625 \n",
+ " 6 0 NaN \n",
+ " 11 0 0 15.566473 \n",
+ " 1 0 10.977328 \n",
+ " 2 0 188.565914 \n",
+ " 13 0 0 NaN \n",
+ " 1 0 NaN \n",
+ " 14 0 0 152.079839 \n",
+ " 1 0 115.028001 \n",
+ " 2 0 NaN \n",
+ " 3 0 NaN \n",
+ " 4 0 97.473849 \n",
+ " 5 0 NaN \n",
+ " 6 0 290.449040 \n",
+ "057B30G2 2 0 0 7.722758 \n",
+ " 1 0 58.574351 \n",
+ " 2 0 565.868529 \n",
+ " 3 0 433.225682 \n",
+ " 4 0 0 1002.391912 \n",
+ " 10 0 0 NaN \n",
+ " 12 0 0 NaN \n",
+ " 1 0 NaN \n",
+ " 16 0 0 NaN \n",
+ " 1 0 NaN \n",
+ " 2 0 NaN \n",
+ " 20 0 0 170.293861 \n",
+ " 23 0 0 8.084435 \n",
+ " 1 0 1201.192824 \n",
+ " 30 0 0 5.343565 \n",
+ " 1 0 NaN \n",
+ " 2 0 NaN \n",
+ "\n",
+ " closest_break_contour_id \n",
+ "ds tomo_id object_id \n",
+ "053B40G2 4 0 0 <NA> \n",
+ " 5 0 0 0 \n",
+ " 1 0 0 \n",
+ " 2 0 0 \n",
+ " 3 0 9 \n",
+ " 8 0 0 <NA> \n",
+ " 18 0 0 4 \n",
+ " 1 0 9 \n",
+ " 2 0 6 \n",
+ " 3 0 0 \n",
+ " 20 0 0 1 \n",
+ " 1 0 1 \n",
+ " 22 0 0 <NA> \n",
+ " 1 0 <NA> \n",
+ "054B36G1 2 0 0 3 \n",
+ " 3 0 0 <NA> \n",
+ " 7 0 0 0 \n",
+ " 1 0 1 \n",
+ " 2 0 3 \n",
+ " 3 0 7 \n",
+ " 4 0 46 \n",
+ " 5 0 39 \n",
+ " 6 0 0 \n",
+ " 11 0 0 3 \n",
+ " 1 0 0 \n",
+ " 2 0 1 \n",
+ " 13 0 0 <NA> \n",
+ " 1 0 <NA> \n",
+ " 14 0 0 7 \n",
+ " 1 0 3 \n",
+ " 2 0 0 \n",
+ " 3 0 0 \n",
+ " 4 0 2 \n",
+ " 5 0 0 \n",
+ " 6 0 2 \n",
+ "057B30G2 2 0 0 1 \n",
+ " 1 0 28 \n",
+ " 2 0 30 \n",
+ " 3 0 31 \n",
+ " 4 0 0 2 \n",
+ " 10 0 0 <NA> \n",
+ " 12 0 0 <NA> \n",
+ " 1 0 <NA> \n",
+ " 16 0 0 0 \n",
+ " 1 0 0 \n",
+ " 2 0 0 \n",
+ " 20 0 0 2 \n",
+ " 23 0 0 0 \n",
+ " 1 0 1 \n",
+ " 30 0 0 4 \n",
+ " 1 0 0 \n",
+ " 2 0 0 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Perform measurements\n",
+ "\n",
+ "def measure(gdf):\n",
+ " \"\"\"\n",
+ " Measure the distance between two contours\n",
+ " \"\"\"\n",
+ " # get position of contours\n",
+ "\n",
+ " tdf = gdf[gdf.type=='T3SS']\n",
+ " # bdf = gdf[gdf.type=='break']\n",
+ "\n",
+ " tdf = tdf.sort_values(by=['object_id', 'contour_id'])\n",
+ "\n",
+ " positions = []\n",
+ " for contour_id in [0, 1, 2]:\n",
+ " if contour_id not in tdf.contour_id.values:\n",
+ " positions.append(np.array([np.nan, np.nan, np.nan]))\n",
+ " continue\n",
+ " x = tdf[tdf.contour_id==contour_id].x[0]\n",
+ " y = tdf[tdf.contour_id==contour_id].y[0]\n",
+ " z = tdf[tdf.contour_id==contour_id].z[0]\n",
+ " positions.append(np.array([x, y, z]))\n",
+ "\n",
+ " # 1) distance 1<->2\n",
+ " d12 = np.linalg.norm(positions[0] - positions[1])\n",
+ "\n",
+ " if d12 > 100:\n",
+ " # log message indicating gdf\n",
+ " msg = 'Distance between 1 and 2 is suspiciously large: {}. Maybe theres a measurement problem?'.format(d12)\n",
+ " msg += '\\n\\tdataset: {}\\ttomo_id: {}\\tobject_id (starting at 0): {}'.format(gdf['ds'].values[0], gdf['tomo_id'].values[0], gdf['object_id'].values[0])\n",
+ " log_msgs.append(msg)\n",
+ "\n",
+ " # 2) distance 2<->3 (only if 3 exists within the same object)\n",
+ " d23 = np.linalg.norm(positions[1] - positions[2])\n",
+ "\n",
+ " # 3) angle between 21 and 23\n",
+ " v21 = positions[0] - positions[1]\n",
+ " v23 = positions[2] - positions[1]\n",
+ "\n",
+ " angle_rad = np.arccos(\n",
+ " np.dot(v21, v23) / np.product([np.linalg.norm(v) for v in [v21, v23]]))\n",
+ "\n",
+ " and_deg = np.degrees(angle_rad)\n",
+ "\n",
+ " # # 4) for every 3 the closest membrane break point\n",
+ " bdf = df[\\\n",
+ " (df.type=='break') &\\\n",
+ " (df.ds==gdf['ds'].values[0]) &\\\n",
+ " (df.tomo_id==gdf['tomo_id'].values[0])]\n",
+ "\n",
+ " bdf = bdf.sort_values(by=['contour_id'])\n",
+ "\n",
+ " if len(bdf):\n",
+ " b_positions = np.array([bdf['x'], bdf['y'], bdf['z']]).T\n",
+ " b_dists = np.linalg.norm(np.array(positions[2]) - b_positions, axis=1)\n",
+ " b_closest_ind = np.argmin(b_dists)\n",
+ "\n",
+ " b_closest_contour_id = int(list(bdf['contour_id'])[b_closest_ind])\n",
+ " b_distance = b_dists[b_closest_ind]\n",
+ "\n",
+ " else:\n",
+ " b_closest_contour_id = np.nan\n",
+ " b_distance = np.nan\n",
+ "\n",
+ " ms = pd.DataFrame({\n",
+ " 'distance_1_2': [d12],\n",
+ " 'distance_2_3': [d23],\n",
+ " 'angle_21_23': [and_deg],\n",
+ " 'distance_to_closest_break': [b_distance],\n",
+ " 'closest_break_contour_id': pd.array([b_closest_contour_id], dtype=pd.Int64Dtype()),\n",
+ " \n",
+ " })\n",
+ "\n",
+ " return ms\n",
+ " \n",
+ "\n",
+ "mdf = df.groupby(['ds', 'tomo_id', 'object_id']).apply(measure)\n",
+ "mdf.to_csv(os.path.join(output_dir, 't3ss_geometry.csv'))\n",
+ "open(os.path.join(output_dir, 't3ss_geometry.log'), 'w').write('\\n'.join(log_msgs))\n",
+ "mdf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 5) distances between 2\n",
+ "\n",
+ "tdf = df[df.type=='T3SS']\n",
+ "for ds in np.unique(tdf.ds):\n",
+ " stdf = tdf[tdf.ds==ds]\n",
+ " for tomo_id in np.unique(stdf.tomo_id):\n",
+ " tstdf = stdf[stdf.tomo_id==tomo_id]\n",
+ " tstdf2 = tstdf[tstdf.contour_id==1]\n",
+ " tstdf2 = tstdf2.sort_values(by=['object_id'])\n",
+ " poss = np.array([tstdf2.x, tstdf2.y, tstdf2.z]).T\n",
+ " d = spatial.distance_matrix(poss, poss)\n",
+ " xd = xr.DataArray(d, dims=['object_id', 'object_id'], coords={'object_id': tstdf2.object_id})\n",
+ " xd.to_pandas().to_csv(os.path.join(output_dir, f\"t3ss_distances_ds_{tstdf2['ds'].values[0]}_tomo-id_{tstdf2['tomo_id'].values[0]}.csv\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "pmdf = mdf.reset_index()\n",
+ "\n",
+ "xcol = 'ds'\n",
+ "for ycol in ['distance_1_2', 'distance_2_3', 'angle_21_23']:\n",
+ " plt.figure()\n",
+ " ax = sns.boxplot(data=pmdf, x='ds', y=ycol, fliersize=0)\n",
+ " ax2 = sns.stripplot(data=pmdf, x='ds', y=ycol, size=5)\n",
+ "\n",
+ " nobs = pmdf[xcol].value_counts().values\n",
+ "\n",
+ " pos = range(len(nobs))\n",
+ " labels = [ax.get_xticklabels()[i].get_text() for i in pos]\n",
+ " labels = [l + '\\n(N=%s)'%nobs[i] for i, l in enumerate(labels)]\n",
+ " ax.set_xticks(pos)\n",
+ " ax.set_xticklabels(labels)\n",
+ "\n",
+ " plt.tight_layout()\n",
+ "\n",
+ " plt.savefig(os.path.join(output_dir, 't3ss_geometry_{}.pdf'.format(ycol)), dpi=300)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "pyclem",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}