From 38ec0e704e44c8cd791efc0544808a8c493c629f Mon Sep 17 00:00:00 2001
From: Georg Raiser <grg2rsr@gmail.com>
Date: Tue, 10 Dec 2024 16:57:51 +0000
Subject: [PATCH] ruffing

---
 src/examples/load_photometry_data.ipynb    |  20 +-
 src/examples/process_photometry_data.ipynb | 681 +++++----------------
 2 files changed, 166 insertions(+), 535 deletions(-)

diff --git a/src/examples/load_photometry_data.ipynb b/src/examples/load_photometry_data.ipynb
index 80539c3..c4c5a91 100644
--- a/src/examples/load_photometry_data.ipynb
+++ b/src/examples/load_photometry_data.ipynb
@@ -13,15 +13,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# imports\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import iblphotometry.io as ffio\n",
-    "import iblphotometry.plots as plots"
+    "import iblphotometry.io as ffio"
    ]
   },
   {
@@ -32,9 +29,10 @@
    "source": [
     "# ONE instantiation\n",
     "from one.api import ONE\n",
+    "\n",
     "one = ONE(base_url='https://alyx.internationalbrainlab.org', mode='remote')\n",
     "\n",
-    "lab = 'cortexlab' # for carolinas dataset or 'wittenlab' for alejandros dataset\n",
+    "lab = 'cortexlab'  # for carolinas dataset or 'wittenlab' for alejandros dataset\n",
     "eids = one.search(dataset='photometry.signal.pqt', lab=lab)\n",
     "eid = eids[2]"
    ]
@@ -452,12 +450,16 @@
     }
    ],
    "source": [
-    "raw_photometry_df = one.load_dataset(eid, dataset='photometry.signal', download_only=True)\n",
+    "raw_photometry_df = one.load_dataset(\n",
+    "    eid, dataset='photometry.signal', download_only=True\n",
+    ")\n",
     "signal_pqt_path = one.eid2path(eid) / 'alf/photometry/photometry.signal.pqt'\n",
-    "locations_df = one.load_dataset(eid, dataset='photometryROI.locations', download_only=True)\n",
+    "locations_df = one.load_dataset(\n",
+    "    eid, dataset='photometryROI.locations', download_only=True\n",
+    ")\n",
     "locations_pqt_path = one.eid2path(eid) / 'alf/photometry/photometryROI.locations.pqt'\n",
     "\n",
-    "ffio.from_ibl_pqt(signal_pqt_path, locations_pqt_path)\n"
+    "ffio.from_ibl_pqt(signal_pqt_path, locations_pqt_path)"
    ]
   }
  ],
diff --git a/src/examples/process_photometry_data.ipynb b/src/examples/process_photometry_data.ipynb
index ce2793b..05953f0 100644
--- a/src/examples/process_photometry_data.ipynb
+++ b/src/examples/process_photometry_data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,19 +13,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# imports\n",
-    "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import iblphotometry.io as ffio"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,318 +33,49 @@
     "\n",
     "one = ONE(base_url='https://alyx.internationalbrainlab.org', mode='remote')\n",
     "\n",
-    "# an example eid from alejandros dataset\n",
-    "eids = one.search(dataset='photometry.signal.pqt', lab='cortexlab')\n",
-    "eid = eids[26]\n",
-    "\n",
-    "#"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# loading data\n",
-    "raw_photometry_df = one.load_dataset(eid, dataset='photometry.signal')\n",
-    "locations_df = one.load_dataset(eid, dataset='photometryROI.locations')\n",
-    "trials = one.load_dataset(eid, '*trials.table')\n",
-    "raw_dfs = ffio.from_dataframes(raw_photometry_df, locations_df)\n",
-    "raw_dfs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %%\n",
-    "# KCENIA / isosbestic specific\n",
-    "from one.api import ONE\n",
-    "\n",
-    "one = ONE(cache_dir='/mnt/h0/kb/data/one')\n",
-    "data_loader = loaders.KceniaLoader(one, verbose=True)\n",
-    "df = pd.read_csv('/home/georg/code/ibl-photometry/src/local/website.csv')\n",
-    "eids = list(df['eid'])\n",
-    "\n",
-    "raw_dfs = data_loader.load_photometry_data(eids[0])\n"
+    "# an example eid\n",
+    "lab = 'wittenlab'\n",
+    "eids = one.search(dataset='photometry.signal.pqt', lab=lab)\n",
+    "eid = eids[26]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
    "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>intervals_0</th>\n",
-       "      <th>intervals_1</th>\n",
-       "      <th>goCue_times</th>\n",
-       "      <th>response_times</th>\n",
-       "      <th>choice</th>\n",
-       "      <th>stimOn_times</th>\n",
-       "      <th>contrastLeft</th>\n",
-       "      <th>contrastRight</th>\n",
-       "      <th>feedback_times</th>\n",
-       "      <th>feedbackType</th>\n",
-       "      <th>rewardVolume</th>\n",
-       "      <th>probabilityLeft</th>\n",
-       "      <th>firstMovement_times</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>336.2580</td>\n",
-       "      <td>344.912302</td>\n",
-       "      <td>342.1655</td>\n",
-       "      <td>342.3122</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>342.3126</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>342.142571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>344.9815</td>\n",
-       "      <td>351.972502</td>\n",
-       "      <td>345.6175</td>\n",
-       "      <td>349.4200</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>345.6171</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>349.4204</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>349.254571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>352.0047</td>\n",
-       "      <td>361.072802</td>\n",
-       "      <td>352.4799</td>\n",
-       "      <td>358.5122</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>352.4795</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>358.5126</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>358.145571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>361.1070</td>\n",
-       "      <td>375.322402</td>\n",
-       "      <td>362.6443</td>\n",
-       "      <td>372.7733</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>362.6439</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>372.7737</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>368.030571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>375.3645</td>\n",
-       "      <td>384.112002</td>\n",
-       "      <td>375.8813</td>\n",
-       "      <td>381.5560</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>375.8809</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>381.5564</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>380.960571</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>175</th>\n",
-       "      <td>2739.1365</td>\n",
-       "      <td>2759.594002</td>\n",
-       "      <td>2739.8028</td>\n",
-       "      <td>2757.0250</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2739.8024</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>2757.0254</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>2756.606571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>2759.6563</td>\n",
-       "      <td>2813.842102</td>\n",
-       "      <td>2760.1854</td>\n",
-       "      <td>2812.2935</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2760.1850</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2812.2936</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>2796.438571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>177</th>\n",
-       "      <td>2813.9142</td>\n",
-       "      <td>2836.631002</td>\n",
-       "      <td>2816.5519</td>\n",
-       "      <td>2835.0659</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2816.5515</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2835.0660</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>2824.281571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>2836.6774</td>\n",
-       "      <td>2843.883602</td>\n",
-       "      <td>2837.4179</td>\n",
-       "      <td>2841.3183</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>2837.4175</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2841.3187</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>2840.504571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>2843.9201</td>\n",
-       "      <td>2909.008002</td>\n",
-       "      <td>2846.4580</td>\n",
-       "      <td>2906.5080</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2906.5083</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>180 rows × 13 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "     intervals_0  intervals_1  goCue_times  response_times  choice  \\\n",
-       "0       336.2580   344.912302     342.1655        342.3122      -1   \n",
-       "1       344.9815   351.972502     345.6175        349.4200      -1   \n",
-       "2       352.0047   361.072802     352.4799        358.5122      -1   \n",
-       "3       361.1070   375.322402     362.6443        372.7733      -1   \n",
-       "4       375.3645   384.112002     375.8813        381.5560      -1   \n",
-       "..           ...          ...          ...             ...     ...   \n",
-       "175    2739.1365  2759.594002    2739.8028       2757.0250       1   \n",
-       "176    2759.6563  2813.842102    2760.1854       2812.2935       1   \n",
-       "177    2813.9142  2836.631002    2816.5519       2835.0659       1   \n",
-       "178    2836.6774  2843.883602    2837.4179       2841.3183      -1   \n",
-       "179    2843.9201  2909.008002    2846.4580       2906.5080       0   \n",
-       "\n",
-       "     stimOn_times  contrastLeft  contrastRight  feedback_times  feedbackType  \\\n",
-       "0             NaN           1.0            NaN        342.3126            -1   \n",
-       "1        345.6171           1.0            NaN        349.4204            -1   \n",
-       "2        352.4795           1.0            NaN        358.5126            -1   \n",
-       "3        362.6439           1.0            NaN        372.7737            -1   \n",
-       "4        375.8809           1.0            NaN        381.5564            -1   \n",
-       "..            ...           ...            ...             ...           ...   \n",
-       "175     2739.8024           NaN            0.5       2757.0254            -1   \n",
-       "176     2760.1850           0.5            NaN       2812.2936             1   \n",
-       "177     2816.5515           1.0            NaN       2835.0660             1   \n",
-       "178     2837.4175           0.5            NaN       2841.3187            -1   \n",
-       "179           NaN           0.5            NaN       2906.5083            -1   \n",
-       "\n",
-       "     rewardVolume  probabilityLeft  firstMovement_times  \n",
-       "0             0.0              0.5           342.142571  \n",
-       "1             0.0              0.5           349.254571  \n",
-       "2             0.0              0.5           358.145571  \n",
-       "3             0.0              0.5           368.030571  \n",
-       "4             0.0              0.5           380.960571  \n",
-       "..            ...              ...                  ...  \n",
-       "175           0.0              0.5          2756.606571  \n",
-       "176           3.0              0.5          2796.438571  \n",
-       "177           3.0              0.5          2824.281571  \n",
-       "178           0.0              0.5          2840.504571  \n",
-       "179           0.0              0.5                  NaN  \n",
-       "\n",
-       "[180 rows x 13 columns]"
+       "{'GCaMP':                    DMS       DLS      NAcc\n",
+       " times                                     \n",
+       " -26.104571    0.003983  0.006191  0.003983\n",
+       " -26.084607    0.003922  0.004078  0.003922\n",
+       " -26.064644    0.004022  0.005881  0.003977\n",
+       " -26.044680    0.004055  0.005767  0.003976\n",
+       " -26.024717    0.004078  0.005803  0.003976\n",
+       " ...                ...       ...       ...\n",
+       "  3877.629817  0.003965  0.005286  0.003927\n",
+       "  3877.649819  0.003958  0.005281  0.003944\n",
+       "  3877.669853  0.003970  0.005352  0.003936\n",
+       "  3877.689887  0.003962  0.005330  0.003934\n",
+       "  3877.709921  0.003968  0.005397  0.003939\n",
+       " \n",
+       " [195198 rows x 3 columns]}"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trials"
+    "# loading data\n",
+    "raw_photometry_df = one.load_dataset(eid, dataset='photometry.signal')\n",
+    "locations_df = one.load_dataset(eid, dataset='photometryROI.locations')\n",
+    "trials = one.load_dataset(eid, '*trials.table')\n",
+    "raw_dfs = ffio.from_ibl_dataframes(raw_photometry_df, locations_df)\n",
+    "raw_dfs"
    ]
   },
   {
@@ -357,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -381,89 +111,101 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>SI</th>\n",
-       "      <th>PPT</th>\n",
+       "      <th>DMS</th>\n",
+       "      <th>DLS</th>\n",
+       "      <th>NAcc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>326.280652</th>\n",
-       "      <td>0.345371</td>\n",
-       "      <td>1.718132</td>\n",
+       "      <th>-9.994494</th>\n",
+       "      <td>0.502134</td>\n",
+       "      <td>0.472474</td>\n",
+       "      <td>0.072365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.313995</th>\n",
-       "      <td>0.688156</td>\n",
-       "      <td>1.748219</td>\n",
+       "      <th>-9.974530</th>\n",
+       "      <td>-0.270563</td>\n",
+       "      <td>0.416623</td>\n",
+       "      <td>0.282271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.347307</th>\n",
-       "      <td>0.816332</td>\n",
-       "      <td>1.120588</td>\n",
+       "      <th>-9.954567</th>\n",
+       "      <td>-0.054550</td>\n",
+       "      <td>0.349578</td>\n",
+       "      <td>0.406404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.380650</th>\n",
-       "      <td>0.454725</td>\n",
-       "      <td>1.762858</td>\n",
+       "      <th>-9.934604</th>\n",
+       "      <td>-0.870414</td>\n",
+       "      <td>0.247359</td>\n",
+       "      <td>0.034610</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.413994</th>\n",
-       "      <td>1.019088</td>\n",
-       "      <td>1.167394</td>\n",
+       "      <th>-9.914640</th>\n",
+       "      <td>-0.998413</td>\n",
+       "      <td>0.149933</td>\n",
+       "      <td>0.044276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.846743</th>\n",
-       "      <td>-0.811561</td>\n",
-       "      <td>-0.689368</td>\n",
+       "      <th>3877.629817</th>\n",
+       "      <td>-0.307573</td>\n",
+       "      <td>-0.209454</td>\n",
+       "      <td>-0.947464</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.880087</th>\n",
-       "      <td>0.517880</td>\n",
-       "      <td>-0.331999</td>\n",
+       "      <th>3877.649819</th>\n",
+       "      <td>-0.449528</td>\n",
+       "      <td>-0.220531</td>\n",
+       "      <td>0.437115</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.913430</th>\n",
-       "      <td>-0.128376</td>\n",
-       "      <td>0.336124</td>\n",
+       "      <th>3877.669853</th>\n",
+       "      <td>-0.218852</td>\n",
+       "      <td>-0.074683</td>\n",
+       "      <td>-0.255174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.946741</th>\n",
-       "      <td>-3.064226</td>\n",
-       "      <td>0.786719</td>\n",
+       "      <th>3877.689887</th>\n",
+       "      <td>-0.378551</td>\n",
+       "      <td>-0.118991</td>\n",
+       "      <td>-0.409016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.980085</th>\n",
-       "      <td>0.074733</td>\n",
-       "      <td>0.149671</td>\n",
+       "      <th>3877.709921</th>\n",
+       "      <td>-0.254340</td>\n",
+       "      <td>0.017626</td>\n",
+       "      <td>-0.024411</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>77786 rows × 2 columns</p>\n",
+       "<p>194391 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                   SI       PPT\n",
-       "326.280652   0.345371  1.718132\n",
-       "326.313995   0.688156  1.748219\n",
-       "326.347307   0.816332  1.120588\n",
-       "326.380650   0.454725  1.762858\n",
-       "326.413994   1.019088  1.167394\n",
-       "...               ...       ...\n",
-       "2918.846743 -0.811561 -0.689368\n",
-       "2918.880087  0.517880 -0.331999\n",
-       "2918.913430 -0.128376  0.336124\n",
-       "2918.946741 -3.064226  0.786719\n",
-       "2918.980085  0.074733  0.149671\n",
+       "                   DMS       DLS      NAcc\n",
+       "-9.994494     0.502134  0.472474  0.072365\n",
+       "-9.974530    -0.270563  0.416623  0.282271\n",
+       "-9.954567    -0.054550  0.349578  0.406404\n",
+       "-9.934604    -0.870414  0.247359  0.034610\n",
+       "-9.914640    -0.998413  0.149933  0.044276\n",
+       "...                ...       ...       ...\n",
+       " 3877.629817 -0.307573 -0.209454 -0.947464\n",
+       " 3877.649819 -0.449528 -0.220531  0.437115\n",
+       " 3877.669853 -0.218852 -0.074683 -0.255174\n",
+       " 3877.689887 -0.378551 -0.118991 -0.409016\n",
+       " 3877.709921 -0.254340  0.017626 -0.024411\n",
        "\n",
-       "[77786 rows x 2 columns]"
+       "[194391 rows x 3 columns]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -519,89 +261,101 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>SI</th>\n",
-       "      <th>PPT</th>\n",
+       "      <th>DMS</th>\n",
+       "      <th>DLS</th>\n",
+       "      <th>NAcc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>326.280652</th>\n",
-       "      <td>0.423091</td>\n",
-       "      <td>2.972447</td>\n",
+       "      <th>-9.994494</th>\n",
+       "      <td>0.588976</td>\n",
+       "      <td>0.461625</td>\n",
+       "      <td>-0.300992</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.313995</th>\n",
-       "      <td>0.842051</td>\n",
-       "      <td>3.023712</td>\n",
+       "      <th>-9.974530</th>\n",
+       "      <td>-0.501257</td>\n",
+       "      <td>0.408888</td>\n",
+       "      <td>0.213176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.347307</th>\n",
-       "      <td>0.998616</td>\n",
-       "      <td>1.933368</td>\n",
+       "      <th>-9.954567</th>\n",
+       "      <td>-0.196467</td>\n",
+       "      <td>0.345582</td>\n",
+       "      <td>0.517156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.380650</th>\n",
-       "      <td>0.556590</td>\n",
-       "      <td>3.047135</td>\n",
+       "      <th>-9.934604</th>\n",
+       "      <td>-1.347421</td>\n",
+       "      <td>0.249063</td>\n",
+       "      <td>-0.393311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>326.413994</th>\n",
-       "      <td>1.246152</td>\n",
-       "      <td>2.013288</td>\n",
+       "      <th>-9.914640</th>\n",
+       "      <td>-1.527899</td>\n",
+       "      <td>0.157071</td>\n",
+       "      <td>-0.369591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.846743</th>\n",
-       "      <td>-0.854700</td>\n",
-       "      <td>-0.583010</td>\n",
+       "      <th>3877.629817</th>\n",
+       "      <td>-0.258024</td>\n",
+       "      <td>-0.168823</td>\n",
+       "      <td>-0.202499</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.880087</th>\n",
-       "      <td>0.545802</td>\n",
-       "      <td>-0.280588</td>\n",
+       "      <th>3877.649819</th>\n",
+       "      <td>-0.384000</td>\n",
+       "      <td>-0.178980</td>\n",
+       "      <td>0.231208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.913430</th>\n",
-       "      <td>-0.134998</td>\n",
-       "      <td>0.284811</td>\n",
+       "      <th>3877.669853</th>\n",
+       "      <td>-0.179288</td>\n",
+       "      <td>-0.045244</td>\n",
+       "      <td>0.014355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.946741</th>\n",
-       "      <td>-3.227773</td>\n",
-       "      <td>0.666126</td>\n",
+       "      <th>3877.689887</th>\n",
+       "      <td>-0.321012</td>\n",
+       "      <td>-0.085873</td>\n",
+       "      <td>-0.033835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2918.980085</th>\n",
-       "      <td>0.078968</td>\n",
-       "      <td>0.127025</td>\n",
+       "      <th>3877.709921</th>\n",
+       "      <td>-0.210782</td>\n",
+       "      <td>0.039399</td>\n",
+       "      <td>0.086639</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>77786 rows × 2 columns</p>\n",
+       "<p>194391 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                   SI       PPT\n",
-       "326.280652   0.423091  2.972447\n",
-       "326.313995   0.842051  3.023712\n",
-       "326.347307   0.998616  1.933368\n",
-       "326.380650   0.556590  3.047135\n",
-       "326.413994   1.246152  2.013288\n",
-       "...               ...       ...\n",
-       "2918.846743 -0.854700 -0.583010\n",
-       "2918.880087  0.545802 -0.280588\n",
-       "2918.913430 -0.134998  0.284811\n",
-       "2918.946741 -3.227773  0.666126\n",
-       "2918.980085  0.078968  0.127025\n",
+       "                   DMS       DLS      NAcc\n",
+       "-9.994494     0.588976  0.461625 -0.300992\n",
+       "-9.974530    -0.501257  0.408888  0.213176\n",
+       "-9.954567    -0.196467  0.345582  0.517156\n",
+       "-9.934604    -1.347421  0.249063 -0.393311\n",
+       "-9.914640    -1.527899  0.157071 -0.369591\n",
+       "...                ...       ...       ...\n",
+       " 3877.629817 -0.258024 -0.168823 -0.202499\n",
+       " 3877.649819 -0.384000 -0.178980  0.231208\n",
+       " 3877.669853 -0.179288 -0.045244  0.014355\n",
+       " 3877.689887 -0.321012 -0.085873 -0.033835\n",
+       " 3877.709921 -0.210782  0.039399  0.086639\n",
        "\n",
-       "[77786 rows x 2 columns]"
+       "[194391 rows x 3 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +369,6 @@
     "        fpp.lowpass_bleachcorrect,\n",
     "        dict(),\n",
     "    ),  # if an empty dict is passed, the default values will be used\n",
-    "    # (fpp.sliding_dFF, dict(w_len=10)),\n",
     "    (fpp.zscore, dict(mode='median')),\n",
     ")\n",
     "\n",
@@ -625,14 +378,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 640x480 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -645,133 +398,9 @@
     "cols = df_processed.columns\n",
     "fig, axes = plt.subplots(nrows=len(cols))\n",
     "for i, col in enumerate(cols):\n",
-    "    axes[i].plot(df_processed[col].loc[t_start:t_stop], label=col, alpha=0.8, lw=1)"
+    "    axes[i].plot(df_processed[col].loc[t_start:t_stop], label=col, alpha=0.8, lw=1)\n",
+    "    axes[i].legend()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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>SI</th>\n",
-       "      <th>PPT</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>326.280652</th>\n",
-       "      <td>0.423091</td>\n",
-       "      <td>2.972447</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326.313995</th>\n",
-       "      <td>0.842051</td>\n",
-       "      <td>3.023712</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326.347307</th>\n",
-       "      <td>0.998616</td>\n",
-       "      <td>1.933368</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326.380650</th>\n",
-       "      <td>0.556590</td>\n",
-       "      <td>3.047135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326.413994</th>\n",
-       "      <td>1.246152</td>\n",
-       "      <td>2.013288</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918.846743</th>\n",
-       "      <td>-0.854700</td>\n",
-       "      <td>-0.583010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918.880087</th>\n",
-       "      <td>0.545802</td>\n",
-       "      <td>-0.280588</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918.913430</th>\n",
-       "      <td>-0.134998</td>\n",
-       "      <td>0.284811</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918.946741</th>\n",
-       "      <td>-3.227773</td>\n",
-       "      <td>0.666126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918.980085</th>\n",
-       "      <td>0.078968</td>\n",
-       "      <td>0.127025</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>77786 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   SI       PPT\n",
-       "326.280652   0.423091  2.972447\n",
-       "326.313995   0.842051  3.023712\n",
-       "326.347307   0.998616  1.933368\n",
-       "326.380650   0.556590  3.047135\n",
-       "326.413994   1.246152  2.013288\n",
-       "...               ...       ...\n",
-       "2918.846743 -0.854700 -0.583010\n",
-       "2918.880087  0.545802 -0.280588\n",
-       "2918.913430 -0.134998  0.284811\n",
-       "2918.946741 -3.227773  0.666126\n",
-       "2918.980085  0.078968  0.127025\n",
-       "\n",
-       "[77786 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t_start, t_stop\n",
-    "df_processed.loc[t_start:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {