adding twochan notebook and updating bug with badframes is None

Author: carsen-stringer

notebooks/run_twochan.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import sys\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "import suite2p\n",
+    "from suite2p import default_settings\n",
+    "from suite2p.run_s2p import logger_setup\n",
+    "\n",
+    "db = {\"data_path\": [\"/media/carsen/disk2/test_suite2p/GT1/\"], \n",
+    "      \"nplanes\": 1, # each tiff has these many planes in sequence\n",
+    "      \"nchannels\": 2, # each tiff has these many channels per plane\n",
+    "      }\n",
+    "db[\"save_path0\"] = db[\"data_path\"][0]\n",
+    "\n",
+    "# initialize logger\n",
+    "logger_setup(db[\"save_path0\"])\n",
+    "\n",
+    "# initialize settings\n",
+    "settings = default_settings()\n",
+    "settings[\"registration\"][\"align_by_chan2\"] = True # optional - use anatomical chan for reg\n",
+    "settings[\"run\"][\"do_registration\"] = 1\n",
+    "settings[\"fs\"] = 10\n",
+    "settings[\"tau\"] = 1.0 # timescale of gcamp to use for deconvolution\n",
+    "settings[\"detection\"][\"threshold_scaling\"] = 1.0\n",
+    "settings[\"detection\"][\"algorithm\"] = \"sparsery\"\n",
+    "settings[\"torch_device\"] = \"cuda\" # use mps for mac, cuda for nvidia gpu, or \"cpu\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "run suite2p with detection on functional channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "db_paths = suite2p.run_s2p(settings=settings, db=db)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "visualize green and red channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg_outputs = np.load(Path(db[\"save_path0\"]) / \"suite2p/plane0/reg_outputs.npy\", allow_pickle=True).item()\n",
+    "import matplotlib.pyplot as plt \n",
+    "from cellpose.transforms import normalize99\n",
+    "redchan = np.clip(normalize99(reg_outputs[\"meanImg_chan2\"]), 0, 1)\n",
+    "greenchan = np.clip(normalize99(reg_outputs[\"meanImg\"]), 0, 1)\n",
+    "rgb = np.stack((redchan, greenchan, redchan), axis=-1)\n",
+    "\n",
+    "plt.figure(figsize=(14,8))\n",
+    "plt.subplot(1,3,1)\n",
+    "plt.imshow(greenchan)\n",
+    "plt.title(\"green (func)\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.subplot(1,3,2)\n",
+    "plt.imshow(redchan)\n",
+    "plt.title(\"red (anat)\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.subplot(1,3,3)\n",
+    "plt.imshow(rgb)\n",
+    "plt.title(\"overlay\")\n",
+    "plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## run ROI detection in second channel \n",
+    "\n",
+    "only use this if you have **two** functional indicators, e.g. gcamp + rgeco"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from suite2p.pipeline_s2p import pipeline\n",
+    "from suite2p.io import BinaryFile\n",
+    "import torch\n",
+    "import shutil \n",
+    "\n",
+    "db0 = np.load(Path(db[\"save_path0\"]) / \"suite2p/db.npy\", allow_pickle=True).item()\n",
+    "nfolds = db0[\"nplanes\"] * db0.get(\"nrois\", 1)\n",
+    "\n",
+    "for i in range(nfolds):\n",
+    "    db_green = np.load(Path(db0[\"save_path0\"]) / f\"suite2p/plane{i}/db.npy\", allow_pickle=True).item()\n",
+    "    Ly, Lx, n_frames = db_green[\"Ly\"], db_green[\"Lx\"], db_green[\"nframes\"]\n",
+    "\n",
+    "    # swap reg files\n",
+    "    reg_file = db_green[\"reg_file_chan2\"]\n",
+    "    reg_file_chan2 = db_green[\"reg_file\"]\n",
+    "\n",
+    "    # new save path\n",
+    "    save_path = Path(db[\"save_path0\"]) / \"suite2p_red\" \n",
+    "    save_path.mkdir(exist_ok=True)\n",
+    "    (save_path / f\"plane{i}\").mkdir(exist_ok=True)\n",
+    "    \n",
+    "    # update db \n",
+    "    db_red = db_green \n",
+    "    db_red[\"reg_file\"] = reg_file\n",
+    "    db_red[\"reg_file_chan2\"] = reg_file_chan2\n",
+    "    np.save(save_path / f\"plane{i}/db.npy\", db_red)\n",
+    "    np.save(save_path / f\"plane{i}/settings.npy\", settings)\n",
+    "\n",
+    "    # swap meanImg/meanImg_chan2 in reg_outputs for gui vis \n",
+    "    reg_outputs = np.load(Path(db[\"save_path0\"]) / f\"suite2p/plane{i}/reg_outputs.npy\", allow_pickle=True).item()\n",
+    "    reg_outputs_red = reg_outputs.copy()\n",
+    "    reg_outputs_red[\"meanImg\"] = reg_outputs[\"meanImg_chan2\"]\n",
+    "    reg_outputs_red[\"meanImg_chan2\"] = reg_outputs[\"meanImg\"]\n",
+    "    np.save(save_path / f\"plane{i}/reg_outputs.npy\", reg_outputs_red)\n",
+    "        \n",
+    "    with BinaryFile(Ly=Ly, Lx=Lx, filename=reg_file, n_frames=n_frames) as f_reg, \\\n",
+    "        BinaryFile(Ly=Ly, Lx=Lx, filename=reg_file_chan2, n_frames=n_frames)  as f_reg_chan2:\n",
+    "        out = pipeline(str(save_path / f\"plane{i}\"), f_reg=f_reg, f_reg_chan2=f_reg_chan2, \n",
+    "                    run_registration=False, settings=settings,\n",
+    "                    device=torch.device(settings[\"torch_device\"]))\n",
+    "\n",
+    "    # outputs from detection on chan2 reg file \n",
+    "    (reg_outputs, detect_outputs, stat, F, Fneu, F_chan2, Fneu_chan2, \n",
+    "                spks, iscell, redcell, zcorr, plane_times) = out"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "s2p",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

suite2p/pipeline_s2p.py

@@ -144,7 +144,8 @@ def pipeline(save_path, f_reg, f_raw=None, f_reg_chan2=None, f_raw_chan2=None,
     t11 = time.time()
     if stat is None:
         bad_frames = reg_outputs["badframes"]
-        bad_frames[badframes] = True
+        if badframes is not None:
+            bad_frames[badframes] = True
         logger.info(f"Excluding {bad_frames.sum()} bad frames from detection")
         if not isinstance(settings["diameter"], (list, tuple, np.ndarray)):
             settings["diameter"] = np.array([settings["diameter"], settings["diameter"]])