diff --git a/behav/loading.py b/behav/loading.py
index 5de2687..433c775 100644
--- a/behav/loading.py
+++ b/behav/loading.py
@@ -63,11 +63,11 @@ def load_data_pandas(subjects, data_folder, force_boolean=['reward']):
                 dt_maker = _make_dt_maker(year)
                 df['date'] = df.apply(dt_maker, axis=1)
                 df.set_index('date', inplace=True)
-                df['type_'] = df['old_type'].map(lambda(x): ['correction','normal'][x])
-                df['response'] = df['old_response'].map(lambda(x): ['none', 'L', 'R'][x])
-                df['correct'] = df['old_correct'].map(lambda(x): [False, True, float('nan')][x])
-                df['reward'] = df.apply(lambda(x): x['reinforcement'] == 1 and x['correct'] == True, axis=1)
-                df['class_'] = df['old_class'].map(lambda(x): ['none', 'L', 'R'][x])
+                df['type_'] = df['old_type'].map(lambda x: ['correction','normal'][x])
+                df['response'] = df['old_response'].map(lambda x: ['none', 'L', 'R'][x])
+                df['correct'] = df['old_correct'].map(lambda x: [False, True, float('nan')][x])
+                df['reward'] = df.apply(lambda x: x['reinforcement'] == 1 and x['correct'] == True, axis=1)
+                df['class_'] = df['old_class'].map(lambda x: ['none', 'L', 'R'][x])
                 df['data_file'] = data_f
                 df_set.append(df)
 
@@ -95,11 +95,11 @@ def load_data_pandas(subjects, data_folder, force_boolean=['reward']):
                 dt_maker = _make_dt_maker(year)
                 df['date'] = df.apply(dt_maker, axis=1)
                 df.set_index('date', inplace=True)
-                df['type_'] = df['old_type'].map(lambda(x): ['correction','normal'][x])
-                df['response'] = df['old_response'].map(lambda(x): ['none', 'C'][x])
-                df['correct'] = df['old_correct'].map(lambda(x): [False, True, float('nan')][x])
-                df['reward'] = df.apply(lambda(x): x['reinforcement'] == 1 and x['correct'] == True, axis=1)
-                df['class_'] = df['old_class'].map(lambda(x): ['none', 'GO', 'NOGO'][x])
+                df['type_'] = df['old_type'].map(lambda x: ['correction','normal'][x])
+                df['response'] = df['old_response'].map(lambda x: ['none', 'C'][x])
+                df['correct'] = df['old_correct'].map(lambda x: [False, True, float('nan')][x])
+                df['reward'] = df.apply(lambda x: x['reinforcement'] == 1 and x['correct'] == True, axis=1)
+                df['class_'] = df['old_class'].map(lambda x: ['none', 'GO', 'NOGO'][x])
                 df['data_file'] = data_f
                 df_set.append(df)
                         
@@ -122,12 +122,12 @@ def _parse(datestr, timestr):
                                  )
                 df.rename(columns=col_map, inplace=True)
                 df.set_index('date',inplace=True)
-                df['type_'] = df['Correction'].map(lambda(x): {0:'normal',1:'correction',243:'error',-1:None}[x])
-                df['correct'] = df['ResponseAccuracy'].map(lambda(x): [False, True, float('nan')][x])
-                df['reward'] = df.apply(lambda(x): x['Reinforced'] == 1 and x['correct'] == True, axis=1)
-                df['punish'] = df.apply(lambda(x): x['Reinforced'] == 1 and x['correct'] == False, axis=1)
-                df['class_'] = df['StimClass'].map(lambda(x): {0:'none',1:'L',2:'R',243:'error',-1:None}[x])
-                df['response'] = df['ResponseSelection'].map(lambda(x): ['none', 'L', 'R'][x])
+                df['type_'] = df['Correction'].map(lambda x: {0:'normal',1:'correction',243:'error',-1:None}[x])
+                df['correct'] = df['ResponseAccuracy'].map(lambda x: [False, True, float('nan')][x])
+                df['reward'] = df.apply(lambda x: x['Reinforced'] == 1 and x['correct'] == True, axis=1)
+                df['punish'] = df.apply(lambda x: x['Reinforced'] == 1 and x['correct'] == False, axis=1)
+                df['class_'] = df['StimClass'].map(lambda x: {0:'none',1:'L',2:'R',243:'error',-1:None}[x])
+                df['response'] = df['ResponseSelection'].map(lambda x: ['none', 'L', 'R'][x])
                 df['data_file'] = data_f
 
                 is_behave = df['BehavioralRecording'] > 0
@@ -141,12 +141,12 @@ def _parse(datestr, timestr):
         if df_set:
             behav_data[subj] = pd.concat(df_set).sort_index()
         else:
-            print 'data not found for %s' % (subj)
+            print("data not found for %s" % (subj))
     if force_boolean:
         for subj in subjects:
             if subj in behav_data:
                 for forced in force_boolean:
-                    behav_data[subj][forced] = behav_data[subj][forced].map(lambda(x): x in [True, 'True', 'true', 1, '1'])
+                    behav_data[subj][forced] = behav_data[subj][forced].map(lambda x: x in [True, 'True', 'true', 1, '1'])
     return behav_data
 
 def _make_dt_maker(year):
diff --git a/behav/plotting.py b/behav/plotting.py
index 6da5359..5482ba2 100644
--- a/behav/plotting.py
+++ b/behav/plotting.py
@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 import pandas as pd
-import utils
+import behav.utils as utils
 import scipy as sp
 from scipy import ndimage
 
@@ -214,7 +214,7 @@ def plot_accuracy_bias(subj, df, x_axis='time', smoothing='exponential', trial_l
     else:
         raise Exception('invalid value for x_axis')
     
-    datas = (df['correct'], df['response']=='L', df['response']=='R')
+    datas = (df['correct'], df['response'].isin(['L','left']), df['response'].isin(['R','right']))
     plot_smoothed_mask = (plt_correct_smoothed, plt_L_response_smoothed, plt_R_response_smoothed)
     plot_shaded_mask = (plt_correct_shade, plt_L_response_shade, plt_R_response_shade)
     plot_line_mask = (plt_correct_line, plt_L_response_line, plt_R_response_line)
diff --git a/behav/utils.py b/behav/utils.py
index 326bd08..89227a4 100644
--- a/behav/utils.py
+++ b/behav/utils.py
@@ -113,7 +113,7 @@ def filter_normal_trials(df):
     '''
     filters dataframe, df, to only include normal (non-correction) trials that got a response.
     '''
-    return df[(df.response<>'none')&(df.type_=='normal')]
+    return df[(df.response!='none')&(df.type_=='normal')]
 
 def filter_recent_days(df, num_days):
     '''
diff --git a/notebooks/recent_behavior_analysis.ipynb b/notebooks/recent_behavior_analysis.ipynb
deleted file mode 100644
index 0049cba..0000000
--- a/notebooks/recent_behavior_analysis.ipynb
+++ /dev/null
@@ -1,159 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%pylab inline\n",
-    "\n",
-    "import behav\n",
-    "from behav import plotting, utils, loading\n",
-    "data_path = '/mnt/cube/RawData/Zog/'\n",
-    "\n",
-    "subjects = (\n",
-    "            'B1109',\n",
-    "            'B1222'\n",
-    "            )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loads Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "behav_data = behav.loading.load_data_pandas(subjects,data_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## plot performance over past two weeks in calendar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "for subj,data in behav_data.iteritems():\n",
-    "    behav.plotting.plot_filtered_performance_calendar(subj,data,num_days=20)    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##plot accuracy per stim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "figsize(16.0, 4.0)\n",
-    "for subj,df in behav_data.items():\n",
-    "    behav.plotting.plot_filtered_accperstim(subj, df, extract_stim_names=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot percent correct by block with confidence bounds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "for subj,df in behav_data.items():\n",
-    "    behav.plotting.plot_ci_accuracy(subj, df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##plot accuracy today"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "for subj,df in behav_data.items():\n",
-    "    behav.plotting.plot_daily_accuracy(subj, df, x_axis='trial_num')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plot number of trials and feeds for past week"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "behav.plotting.plot_trial_feeds(behav_data)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/recent_behavior_analysis.ipynb b/recent_behavior_analysis.ipynb
new file mode 100644
index 0000000..f813bab
--- /dev/null
+++ b/recent_behavior_analysis.ipynb
@@ -0,0 +1,584 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "\n",
+    "import behav\n",
+    "import behav.utils as utils\n",
+    "from behav import plotting, loading\n",
+    "data_path = '/mnt/cube/RawData/Magpi/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subjects = [\n",
+    "    \"B1176\", # doesn't work\n",
+    "    \"B1440\", # doesn't work\n",
+    "    \"B1170\", # doesn't work\n",
+    "    \"B1426\",\n",
+    "    \"B1432\"\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loads Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/AD/acmai/anaconda3/lib/python3.6/site-packages/IPython/core/magic.py:187: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  call = lambda f, *a, **k: f(*a, **k)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 37.4 s, sys: 2.36 s, total: 39.8 s\n",
+      "Wall time: 28.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "behav_data = behav.loading.load_data_pandas(subjects,data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for subj, data in behav_data.items():\n",
+    "    behav.plotting.plot_ci_accuracy(subj, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Int64Index([12, 12, 13, 13, 13, 13, 13, 13, 13, 13,\n",
+      "            ...\n",
+      "            13, 13, 13, 13, 13, 13, 13, 13, 13, 13],\n",
+      "           dtype='int64', name='time', length=193357)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for subj, data in behav_data.items():\n",
+    "    print(data.index.hour)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "B1176 ['none' 'R' 'L' 'right' 'left'] ['L' 'R']\n",
+      "B1440 ['none' 'left' 'right'] ['L' 'R']\n",
+      "B1170 ['none' 'R' 'L' 'right' 'left'] ['L' 'R']\n",
+      "B1426 ['none' 'R' 'L'] ['R' 'L']\n",
+      "B1432 ['none' 'R' 'L'] ['R' 'L']\n"
+     ]
+    }
+   ],
+   "source": [
+    "for subj, data in behav_data.items():\n",
+    "    print(subj, data.response.unique(), data.class_.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'B1440':                             session  index       type_  \\\n",
+      "time                                                     \n",
+      "2019-05-22 10:51:48.724176        1      0      normal   \n",
+      "2019-05-23 19:11:09.937490        1      0      normal   \n",
+      "2019-05-23 19:12:58.481264        1      1      normal   \n",
+      "2019-05-23 19:13:53.686248        1      2      normal   \n",
+      "2019-05-23 19:14:05.065187        1      3      normal   \n",
+      "2019-05-23 19:14:16.616046        1      4      normal   \n",
+      "2019-05-23 19:14:40.286511        1      5      normal   \n",
+      "2019-05-23 19:15:02.012866        1      6      normal   \n",
+      "2019-05-23 19:15:55.458638        1      7  correction   \n",
+      "2019-05-23 19:16:32.209649        1      8  correction   \n",
+      "2019-05-23 19:16:42.943857        1      9  correction   \n",
+      "2019-05-23 19:16:52.529075        1     10      normal   \n",
+      "2019-05-23 19:17:04.998088        1     11      normal   \n",
+      "2019-05-23 19:17:19.641813        1     12      normal   \n",
+      "2019-05-23 19:17:33.481574        1     13      normal   \n",
+      "2019-05-23 19:17:47.750435        1     14      normal   \n",
+      "2019-05-23 19:17:58.786424        1     15      normal   \n",
+      "2019-05-23 19:18:45.499087        1     16  correction   \n",
+      "2019-05-23 19:18:53.578644        1     17      normal   \n",
+      "2019-05-23 19:19:05.661503        1     18      normal   \n",
+      "2019-05-23 19:19:19.087675        1     19      normal   \n",
+      "2019-05-23 19:19:29.635573        1     20      normal   \n",
+      "2019-05-23 19:19:42.597254        1     21      normal   \n",
+      "2019-05-23 19:19:59.878003        1     22      normal   \n",
+      "2019-05-23 19:20:11.629763        1     23      normal   \n",
+      "2019-05-23 19:21:10.547959        1     24  correction   \n",
+      "2019-05-23 19:22:03.676662        1     25  correction   \n",
+      "2019-05-23 19:23:03.871920        1     26  correction   \n",
+      "2019-05-23 19:23:10.865147        1     27      normal   \n",
+      "2019-05-23 19:23:21.665532        1     28      normal   \n",
+      "...                             ...    ...         ...   \n",
+      "2019-10-03 14:22:48.991910      484      4  correction   \n",
+      "2019-10-03 14:22:56.220199      484      5      normal   \n",
+      "2019-10-03 14:23:07.072970      484      6      normal   \n",
+      "2019-10-03 14:23:18.024804      484      7      normal   \n",
+      "2019-10-03 14:24:01.664194      484      8  correction   \n",
+      "2019-10-03 14:24:09.690272      484      9      normal   \n",
+      "2019-10-03 14:24:21.585044      484     10      normal   \n",
+      "2019-10-03 14:24:35.059178      484     11      normal   \n",
+      "2019-10-03 14:25:40.527248      484     12  correction   \n",
+      "2019-10-03 14:26:08.098211      484     13  correction   \n",
+      "2019-10-03 14:26:16.791744      484     14      normal   \n",
+      "2019-10-03 14:26:29.286273      484     15      normal   \n",
+      "2019-10-03 14:26:40.343471      484     16      normal   \n",
+      "2019-10-03 14:26:51.544024      484     17      normal   \n",
+      "2019-10-03 14:27:02.163420      484     18      normal   \n",
+      "2019-10-03 14:27:13.013733      484     19      normal   \n",
+      "2019-10-03 14:27:23.893861      484     20      normal   \n",
+      "2019-10-03 14:27:36.295170      484     21      normal   \n",
+      "2019-10-03 14:27:48.134132      484     22      normal   \n",
+      "2019-10-03 14:48:26.326204      484     23  correction   \n",
+      "2019-10-03 14:48:34.768266      484     24      normal   \n",
+      "2019-10-03 14:48:49.080226      484     25      normal   \n",
+      "2019-10-03 14:49:00.624629      484     26      normal   \n",
+      "2019-10-03 14:49:11.364248      484     27      normal   \n",
+      "2019-10-03 14:49:23.075273      484     28      normal   \n",
+      "2019-10-03 14:49:34.562699      484     29      normal   \n",
+      "2019-10-03 14:49:45.213070      484     30      normal   \n",
+      "2019-10-03 14:49:55.904851      484     31      normal   \n",
+      "2019-10-03 14:50:07.374423      484     32      normal   \n",
+      "2019-10-03 14:50:18.404101      484     33      normal   \n",
+      "\n",
+      "                                                                     stimulus  \\\n",
+      "time                                                                            \n",
+      "2019-05-22 10:51:48.724176  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:11:09.937490  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-05-23 19:12:58.481264  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-05-23 19:13:53.686248  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:14:05.065187  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-05-23 19:14:16.616046  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:14:40.286511  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:15:02.012866  /home/bird/opdat/B1440/stims/Generated_Songs/C...   \n",
+      "2019-05-23 19:15:55.458638  /home/bird/opdat/B1440/stims/Generated_Songs/C...   \n",
+      "2019-05-23 19:16:32.209649  /home/bird/opdat/B1440/stims/Generated_Songs/C...   \n",
+      "2019-05-23 19:16:42.943857  /home/bird/opdat/B1440/stims/Generated_Songs/C...   \n",
+      "2019-05-23 19:16:52.529075  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:17:04.998088  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:17:19.641813  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:17:33.481574  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-05-23 19:17:47.750435  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:17:58.786424  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:18:45.499087  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:18:53.578644  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:19:05.661503  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:19:19.087675  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:19:29.635573  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:19:42.597254  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-05-23 19:19:59.878003  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-05-23 19:20:11.629763  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:21:10.547959  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:22:03.676662  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:23:03.871920  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:23:10.865147  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-05-23 19:23:21.665532  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "...                                                                       ...   \n",
+      "2019-10-03 14:22:48.991910  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:22:56.220199  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:23:07.072970  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:23:18.024804  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:24:01.664194  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:24:09.690272  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:24:21.585044  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:24:35.059178  /home/bird/opdat/B1440/stims/Generated_Songs/D...   \n",
+      "2019-10-03 14:25:40.527248  /home/bird/opdat/B1440/stims/Generated_Songs/D...   \n",
+      "2019-10-03 14:26:08.098211  /home/bird/opdat/B1440/stims/Generated_Songs/D...   \n",
+      "2019-10-03 14:26:16.791744  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:26:29.286273  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:26:40.343471  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:26:51.544024  /home/bird/opdat/B1440/stims/Generated_Songs/C...   \n",
+      "2019-10-03 14:27:02.163420  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:27:13.013733  /home/bird/opdat/B1440/stims/Generated_Songs/C...   \n",
+      "2019-10-03 14:27:23.893861  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:27:36.295170  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:27:48.134132  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:48:26.326204  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:48:34.768266  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:48:49.080226  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:49:00.624629  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:49:11.364248  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:49:23.075273  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:49:34.562699  /home/bird/opdat/B1440/stims/Generated_Songs/D...   \n",
+      "2019-10-03 14:49:45.213070  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "2019-10-03 14:49:55.904851  /home/bird/opdat/B1440/stims/Generated_Songs/D...   \n",
+      "2019-10-03 14:50:07.374423  /home/bird/opdat/B1440/stims/Generated_Songs/B...   \n",
+      "2019-10-03 14:50:18.404101  /home/bird/opdat/B1440/stims/Generated_Songs/A...   \n",
+      "\n",
+      "                           class_ response correct        rt  reward  punish  \\\n",
+      "time                                                                           \n",
+      "2019-05-22 10:51:48.724176      L     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:11:09.937490      R     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:12:58.481264      R     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:13:53.686248      L     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:14:05.065187      R     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:14:16.616046      L     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:14:40.286511      L     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:15:02.012866      R     left   False  2.430778   False    True   \n",
+      "2019-05-23 19:15:55.458638      R     left   False  0.780528   False    True   \n",
+      "2019-05-23 19:16:32.209649      R     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:16:42.943857      R    right    True  0.903088   False   False   \n",
+      "2019-05-23 19:16:52.529075      L     left    True  1.517386    True   False   \n",
+      "2019-05-23 19:17:04.998088      L     left    True  3.215603    True   False   \n",
+      "2019-05-23 19:17:19.641813      L     left    True  2.715724    True   False   \n",
+      "2019-05-23 19:17:33.481574      R    right    True  3.604548    True   False   \n",
+      "2019-05-23 19:17:47.750435      L     left    True  0.905574    True   False   \n",
+      "2019-05-23 19:17:58.786424      L    right   False  4.251913   False    True   \n",
+      "2019-05-23 19:18:45.499087      L     left    True  0.918835   False   False   \n",
+      "2019-05-23 19:18:53.578644      L     left    True  0.907167    True   False   \n",
+      "2019-05-23 19:19:05.661503      L     left    True  3.007660    True   False   \n",
+      "2019-05-23 19:19:19.087675      L     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:19:29.635573      L     left    True  1.742910    True   False   \n",
+      "2019-05-23 19:19:42.597254      R     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:19:59.878003      R    right    True  0.140945    True   False   \n",
+      "2019-05-23 19:20:11.629763      L    right   False  0.734049   False    True   \n",
+      "2019-05-23 19:21:10.547959      L    right   False  1.123794   False    True   \n",
+      "2019-05-23 19:22:03.676662      L    right   False  0.296543   False    True   \n",
+      "2019-05-23 19:23:03.871920      L     left    True  0.531675   False   False   \n",
+      "2019-05-23 19:23:10.865147      L     none     NaN       NaN   False   False   \n",
+      "2019-05-23 19:23:21.665532      L     left    True  1.556531    True   False   \n",
+      "...                           ...      ...     ...       ...     ...     ...   \n",
+      "2019-10-03 14:22:48.991910      R    right    True  0.563351   False   False   \n",
+      "2019-10-03 14:22:56.220199      R    right    True  0.422143    True   False   \n",
+      "2019-10-03 14:23:07.072970      R    right    True  0.125612    True   False   \n",
+      "2019-10-03 14:23:18.024804      L    right   False  0.687162   False    True   \n",
+      "2019-10-03 14:24:01.664194      L     left    True  0.969746   False   False   \n",
+      "2019-10-03 14:24:09.690272      L     left    True  0.423518    True   False   \n",
+      "2019-10-03 14:24:21.585044      L     left    True  0.734027   False   False   \n",
+      "2019-10-03 14:24:35.059178      R     left   False  0.234896   False    True   \n",
+      "2019-10-03 14:25:40.527248      R     none     NaN       NaN   False   False   \n",
+      "2019-10-03 14:26:08.098211      R    right    True  0.421151   False   False   \n",
+      "2019-10-03 14:26:16.791744      L     left    True  0.297687    True   False   \n",
+      "2019-10-03 14:26:29.286273      L     left    True  0.515649    True   False   \n",
+      "2019-10-03 14:26:40.343471      R    right    True  0.345284    True   False   \n",
+      "2019-10-03 14:26:51.544024      R    right    True  0.172303    True   False   \n",
+      "2019-10-03 14:27:02.163420      L     left    True  0.359715    True   False   \n",
+      "2019-10-03 14:27:13.013733      R    right    True  0.203460    True   False   \n",
+      "2019-10-03 14:27:23.893861      R    right    True  0.983561    True   False   \n",
+      "2019-10-03 14:27:36.295170      L     left    True  0.874554    True   False   \n",
+      "2019-10-03 14:27:48.134132      L    right   False  0.328438   False    True   \n",
+      "2019-10-03 14:48:26.326204      L     left    True  0.594901   False   False   \n",
+      "2019-10-03 14:48:34.768266      L     left    True  1.235494    True   False   \n",
+      "2019-10-03 14:48:49.080226      R    right    True  0.188245    True   False   \n",
+      "2019-10-03 14:49:00.624629      L     left    True  0.548681    True   False   \n",
+      "2019-10-03 14:49:11.364248      L     left    True  0.592911    True   False   \n",
+      "2019-10-03 14:49:23.075273      L     left    True  0.438100    True   False   \n",
+      "2019-10-03 14:49:34.562699      R    right    True  0.563118    True   False   \n",
+      "2019-10-03 14:49:45.213070      R    right    True  0.562306    True   False   \n",
+      "2019-10-03 14:49:55.904851      R    right    True  0.469082    True   False   \n",
+      "2019-10-03 14:50:07.374423      L     left    True  0.937375    True   False   \n",
+      "2019-10-03 14:50:18.404101      R    right    True  0.438025    True   False   \n",
+      "\n",
+      "                                                                    data_file  \n",
+      "time                                                                           \n",
+      "2019-05-22 10:51:48.724176  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:11:09.937490  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:12:58.481264  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:13:53.686248  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:14:05.065187  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:14:16.616046  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:14:40.286511  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:15:02.012866  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:15:55.458638  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:16:32.209649  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:16:42.943857  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:16:52.529075  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:17:04.998088  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:17:19.641813  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:17:33.481574  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:17:47.750435  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:17:58.786424  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:18:45.499087  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:18:53.578644  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:19:05.661503  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:19:19.087675  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:19:29.635573  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:19:42.597254  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:19:59.878003  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:20:11.629763  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:21:10.547959  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:22:03.676662  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:23:03.871920  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:23:10.865147  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-05-23 19:23:21.665532  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "...                                                                       ...  \n",
+      "2019-10-03 14:22:48.991910  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:22:56.220199  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:23:07.072970  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:23:18.024804  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:24:01.664194  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:24:09.690272  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:24:21.585044  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:24:35.059178  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:25:40.527248  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:26:08.098211  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:26:16.791744  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:26:29.286273  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:26:40.343471  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:26:51.544024  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:27:02.163420  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:27:13.013733  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:27:23.893861  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:27:36.295170  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:27:48.134132  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:48:26.326204  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:48:34.768266  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:48:49.080226  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:49:00.624629  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:49:11.364248  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:49:23.075273  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:49:34.562699  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:49:45.213070  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:49:55.904851  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:50:07.374423  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "2019-10-03 14:50:18.404101  /mnt/cube/RawData/Magpi/B1440/B1440_trialdata_...  \n",
+      "\n",
+      "[113797 rows x 11 columns]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(behav_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot performance over past two weeks in calendar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from importlib import reload\n",
+    "reload(plotting)\n",
+    "for subj,data in behav_data.items():\n",
+    "    behav.plotting.plot_filtered_performance_calendar(subj,data,num_days=20)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##plot accuracy per stim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figsize(16.0, 4.0)\n",
+    "for subj,df in behav_data.items():\n",
+    "    behav.plotting.loc(subj, df, extract_stim_names=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot percent correct by block with confidence bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for subj,df in behav_data.items():\n",
+    "    behav.plotting.plot_ci_accuracy(subj, df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##plot accuracy today"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for subj,df in behav_data.items():\n",
+    "    behav.plotting.plot_daily_accuracy(subj, df, x_axis='trial_num')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot number of trials and feeds for past week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "behav.plotting.plot_trial_feeds(behav_data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}