From bf142af98dbe84bd8825352d4662cf3f79110722 Mon Sep 17 00:00:00 2001 From: Adam Amster Date: Mon, 1 Aug 2022 16:06:51 -0400 Subject: [PATCH 1/4] Fixes issue with accessing stimulus presentations table of released vcn data --- .../ecephys/ecephys_session_api/ecephys_nwb_session_api.py | 5 +++++ allensdk/test/brain_observatory/ecephys/test_write_nwb.py | 6 +++++- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/allensdk/brain_observatory/ecephys/ecephys_session_api/ecephys_nwb_session_api.py b/allensdk/brain_observatory/ecephys/ecephys_session_api/ecephys_nwb_session_api.py index 9b17ed63d..2b8788c86 100644 --- a/allensdk/brain_observatory/ecephys/ecephys_session_api/ecephys_nwb_session_api.py +++ b/allensdk/brain_observatory/ecephys/ecephys_session_api/ecephys_nwb_session_api.py @@ -84,6 +84,11 @@ def get_stimulus_presentations(self): table = table.value if "color" in table.columns: + # .loc breaks on nan values so fill with empty string + # This is backwards compatible change for older nwb files + # Newer ones encode nan value here with empty string + table['color'] = table['color'].fillna('') + # the color column actually contains two parameters. One is # coded as rgb triplets and the other as -1 or 1 if "color_triplet" not in table.columns: diff --git a/allensdk/test/brain_observatory/ecephys/test_write_nwb.py b/allensdk/test/brain_observatory/ecephys/test_write_nwb.py index 551887ed3..afd0f5906 100644 --- a/allensdk/test/brain_observatory/ecephys/test_write_nwb.py +++ b/allensdk/test/brain_observatory/ecephys/test_write_nwb.py @@ -151,7 +151,8 @@ def test_add_metadata(nwbfile, roundtripper, metadata, expected_metadata): 'movie_specific_column': [np.nan, np.nan, np.nan, 1.0, np.nan], 'start_time': [1., 2., 4., 5., 6.], 'stimulus_name': ['gabors', 'gabors', 'random', 'movie', 'gabors'], - 'stop_time': [2., 4., 5., 6., 8.] + 'stop_time': [2., 4., 5., 6., 8.], + 'color': [np.nan] + ['[1.0, 1.0, 1.0]'] * 4 }, index=pd.Index(name='stimulus_presentations_id', data=[0, 1, 2, 3, 4]))), ]) @@ -163,6 +164,9 @@ def test_add_stimulus_presentations(nwbfile, presentations, roundtripper): api = roundtripper(nwbfile, EcephysNwbSessionApi) obtained_stimulus_table = api.get_stimulus_presentations() + if 'color' in presentations.value: + presentations.value['color_triplet'] = [''] + ['[1.0, 1.0, 1.0]'] * 4 + presentations.value['color'] = '' pd.testing.assert_frame_equal( presentations.value, obtained_stimulus_table, From 9a7ce5ec40eacc99cc9df33c052ee66dcf31ff53 Mon Sep 17 00:00:00 2001 From: Adam Amster Date: Thu, 4 Aug 2022 10:31:54 -0400 Subject: [PATCH 2/4] VBN notebook updates --- ..._with_the_stimulus_and_trials_tables.ipynb | 1389 +++----- ...ual_behavior_neuropixels_data_access.ipynb | 144 +- ...ehavior_neuropixels_dataset_manifest.ipynb | 2873 +++++++++++++++ ...behavior_neuropixels_quality_metrics.ipynb | 426 ++- ...sual_behavior_neuropixels_quickstart.ipynb | 3118 +++-------------- doc_template/visual_behavior_neuropixels.rst | 7 +- 6 files changed, 4330 insertions(+), 3627 deletions(-) mode change 100644 => 100755 doc_template/examples_root/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.ipynb mode change 100644 => 100755 doc_template/examples_root/examples/nb/visual_behavior_neuropixels_data_access.ipynb create mode 100755 doc_template/examples_root/examples/nb/visual_behavior_neuropixels_dataset_manifest.ipynb mode change 100644 => 100755 doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quality_metrics.ipynb mode change 100644 => 100755 doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quickstart.ipynb diff --git a/doc_template/examples_root/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.ipynb b/doc_template/examples_root/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.ipynb old mode 100644 new mode 100755 index 6ccacedca..4d7dc04eb --- a/doc_template/examples_root/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.ipynb +++ b/doc_template/examples_root/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Aligning behavioral data to task events with the stimulus and trials tables\n", - "This notebook outlines the stimulus presentations table and the trials table and shows how you can use them to align behavioral data like running, licking and pupil info to task events. Please note that the VBN project used the same detection of change task as the Visual Behavior 2-Photon dataset. Users are encouraged to explore the [documentation](http://portal.brain-map.org/explore/circuits/visual-coding-2p) and [example notebooks](https://allensdk.readthedocs.io/en/latest/brain_observatory.html) for that project for additional context.\n", + "This notebook outlines the stimulus presentations table and the trials table and shows how you can use them to align behavioral data like running, licking and pupil info to task events. Please note that the VBN project used the same detection of change task as the Visual Behavior 2-Photon dataset. Users are encouraged to explore the [documentation](http://portal.brain-map.org/explore/circuits/visual-behavior-2p) and [example notebooks](https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html) for that project for additional context.\n", "\n", "Contents\n", "-------------\n", @@ -15,23 +15,30 @@ "* Aligning Running, Licking and Pupil Data to task events" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import the cache" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (3.0.4) doesn't match a supported version!\n", - " RequestsDependencyWarning)\n" + "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import os\n", - "\n", + "from pathlib import Path\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -41,36 +48,26 @@ " import VisualBehaviorNeuropixelsProjectCache" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the sessions table" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ecephys_session_1069193611.nwb: 100%|████████████████████████████████████████████| 2.83G/2.83G [02:10<00:00, 21.6MMB/s]\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.5.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'core' version 2.4.0 because version 2.3.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.1.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n" - ] - } - ], + "outputs": [], "source": [ - "cache_dir = r\"C:\\Users\\svc_ccg\\Desktop\\Data\\vbn_cache\"\n", + "# Update this to a valid directory in your filesystem. This is where the data will be stored.\n", + "cache_dir = Path(\"/path/to/vbn_cache\")\n", "\n", "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(\n", " cache_dir=cache_dir)\n", "\n", - "ecephys_sessions_table = cache.get_ecephys_session_table()[0]\n", - "\n", - "session_id = ecephys_sessions_table.index.values[50]\n", - "session = cache.get_ecephys_session(\n", - " ecephys_session_id=session_id)" + "ecephys_sessions_table = cache.get_ecephys_session_table()" ] }, { @@ -84,301 +81,71 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's grab a random session and look at the stimulus presentations dataframe." + "Every recording session consisted of three major visual stimulus epochs in the following order (diagrammed below):\n", + "- An active behavior session during which the mouse performed the change detection task\n", + "- Receptive field mapping and full-field flash stimuli\n", + "- 'Passive' replay of stimulus shown during active behavior, but without the lickspout so the mouse can no longer respond." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's grab a random session and look at the stimulus presentations dataframe to see how these epochs are labeled." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "ecephys_session_1063010385.nwb: 100%|████████████████████████████████████████████| 2.39G/2.39G [02:04<00:00, 19.1MMB/s]\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.5.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'core' version 2.4.0 because version 2.3.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.1.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n" + "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38\\lib\\site-packages\\hdmf\\utils.py:577: FutureWarning: DynamicTable.__init__: Using positional arguments for this method is discouraged and will be deprecated in a future major release. Please use keyword arguments to ensure future compatibility.\n", + " warnings.warn(msg, FutureWarning)\n" ] } ], "source": [ - "session_id = ecephys_sessions_table.index.values[20]\n", "session = cache.get_ecephys_session(\n", - " ecephys_session_id=session_id)" + " ecephys_session_id=1065437523)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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"execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stimulus_presentations = session.stimulus_presentations\n", - "stimulus_presentations.head()" + "stimulus_presentations.columns" ] }, { @@ -390,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -418,6 +185,7 @@ " stimulus_name\n", " active\n", " duration\n", + " start_time\n", " \n", " \n", " stimulus_presentations_id\n", @@ -425,50 +193,25 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", " 0\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " True\n", - " 0.250214\n", - " \n", - " \n", - " 1\n", - " 0\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " True\n", - " 0.250214\n", - " \n", - " \n", - " 2\n", - " 0\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " True\n", - " NaN\n", - " \n", - " \n", - " 3\n", - " 0\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", + " Natural_Images_Lum_Matched_set_ophys_G_2019\n", " True\n", - " 0.250219\n", - " \n", - " \n", - " 4\n", - " 0\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " True\n", - " 0.250199\n", + " 0.250188\n", + " 28.131464\n", " \n", " \n", " 4797\n", " 1\n", " spontaneous\n", " False\n", - " 10.008370\n", + " 10.008420\n", + " 3648.207579\n", " \n", " \n", " 4798\n", @@ -476,111 +219,31 @@ " gabor_20_deg_250ms\n", " False\n", " 0.250208\n", - " \n", - " \n", - " 4799\n", - " 2\n", - " gabor_20_deg_250ms\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 4800\n", - " 2\n", - " gabor_20_deg_250ms\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 4801\n", - " 2\n", - " gabor_20_deg_250ms\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 4802\n", - " 2\n", - " gabor_20_deg_250ms\n", - " False\n", - " 0.250207\n", + " 3658.215999\n", " \n", " \n", " 8443\n", " 3\n", " spontaneous\n", " False\n", - " 288.991592\n", + " 288.992998\n", + " 4570.232761\n", " \n", " \n", " 8444\n", " 4\n", " flash_250ms\n", " False\n", - " 0.250203\n", - " \n", - " \n", - " 8445\n", - " 4\n", - " flash_250ms\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 8446\n", - " 4\n", - " flash_250ms\n", - " False\n", - " 0.250216\n", - " \n", - " \n", - " 8447\n", - " 4\n", - " flash_250ms\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 8448\n", - " 4\n", - " flash_250ms\n", - " False\n", - " 0.250211\n", + " 0.250201\n", + " 4859.225759\n", " \n", " \n", " 8594\n", " 5\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 8595\n", - " 5\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " False\n", - " 0.250208\n", - " \n", - " \n", - " 8596\n", - " 5\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " False\n", - " 0.250212\n", - " \n", - " \n", - " 8597\n", - " 5\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", - " False\n", - " 0.250203\n", - " \n", - " \n", - " 8598\n", - " 5\n", - " Natural_Images_Lum_Matched_set_ophys_H_2019\n", + " Natural_Images_Lum_Matched_set_ophys_G_2019\n", " False\n", - " 0.250212\n", + " 0.250213\n", + " 5183.198085\n", " \n", " \n", "\n", @@ -590,86 +253,42 @@ " stimulus_block \\\n", "stimulus_presentations_id \n", "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", "4797 1 \n", "4798 2 \n", - "4799 2 \n", - "4800 2 \n", - "4801 2 \n", - "4802 2 \n", "8443 3 \n", "8444 4 \n", - "8445 4 \n", - "8446 4 \n", - "8447 4 \n", - "8448 4 \n", "8594 5 \n", - "8595 5 \n", - "8596 5 \n", - "8597 5 \n", - "8598 5 \n", "\n", " stimulus_name \\\n", "stimulus_presentations_id \n", - "0 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "1 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "2 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "3 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "4 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", + "0 Natural_Images_Lum_Matched_set_ophys_G_2019 \n", "4797 spontaneous \n", "4798 gabor_20_deg_250ms \n", - "4799 gabor_20_deg_250ms \n", - "4800 gabor_20_deg_250ms \n", - "4801 gabor_20_deg_250ms \n", - "4802 gabor_20_deg_250ms \n", "8443 spontaneous \n", "8444 flash_250ms \n", - "8445 flash_250ms \n", - "8446 flash_250ms \n", - "8447 flash_250ms \n", - "8448 flash_250ms \n", - "8594 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "8595 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "8596 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "8597 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", - "8598 Natural_Images_Lum_Matched_set_ophys_H_2019 \n", + "8594 Natural_Images_Lum_Matched_set_ophys_G_2019 \n", "\n", - " active duration \n", - "stimulus_presentations_id \n", - "0 True 0.250214 \n", - "1 True 0.250214 \n", - "2 True NaN \n", - "3 True 0.250219 \n", - "4 True 0.250199 \n", - "4797 False 10.008370 \n", - "4798 False 0.250208 \n", - "4799 False 0.250208 \n", - "4800 False 0.250208 \n", - "4801 False 0.250208 \n", - "4802 False 0.250207 \n", - "8443 False 288.991592 \n", - "8444 False 0.250203 \n", - "8445 False 0.250208 \n", - "8446 False 0.250216 \n", - "8447 False 0.250208 \n", - "8448 False 0.250211 \n", - "8594 False 0.250208 \n", - "8595 False 0.250208 \n", - "8596 False 0.250212 \n", - "8597 False 0.250203 \n", - "8598 False 0.250212 " + " active duration start_time \n", + "stimulus_presentations_id \n", + "0 True 0.250188 28.131464 \n", + "4797 False 10.008420 3648.207579 \n", + "4798 False 0.250208 3658.215999 \n", + "8443 False 288.992998 4570.232761 \n", + "8444 False 0.250201 4859.225759 \n", + "8594 False 0.250213 5183.198085 " ] }, - "execution_count": 19, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "stimulus_presentations.groupby('stimulus_block')[['stimulus_block', 'stimulus_name', 'active', 'duration']].head()" + "stimulus_presentations.groupby('stimulus_block')[['stimulus_block', \n", + " 'stimulus_name', \n", + " 'active', \n", + " 'duration', \n", + " 'start_time']].head(1)" ] }, { @@ -678,7 +297,7 @@ "source": [ "This shows us the structure of this experiment (and every experiment in this dataset). There are 5 stimuli as follows:\n", "\n", - "**block 0**: Change detection task. Natural images are flashed repeatedly and the mouse is rewarded for licking when the identity of the image changes. You can find more info about this task [here](http://portal.brain-map.org/explore/circuits/visual-behavior-neuropixels?edit&language=en).\n", + "**block 0**: Change detection task. Natural images are flashed repeatedly and the mouse is rewarded for licking when the identity of the image changes. You can find more info about this task [here](http://portal.brain-map.org/explore/circuits/visual-behavior-neuropixels?edit&language=en). Also see [here](https://www.frontiersin.org/articles/10.3389/fnbeh.2020.00104/full) for info about our general training strategy.\n", "\n", "**block 1**: Brief gray screen\n", "\n", @@ -699,51 +318,51 @@ "\n", "#### General\n", "\n", - "*active*: Boolean indicating when the change detection task (with the lick spout available to the mouse) was run. This should only be TRUE for block 0.\n", + "`active`: Boolean indicating when the change detection task (with the lick spout available to the mouse) was run. This should only be TRUE for block 0.\n", "\n", - "*stimulus_block*: Index of stimulus as described in cells above.\n", + "`stimulus_block`: Index of stimulus as described in cells above.\n", "\n", - "*stimulus_name*: Indicates the stimulus category for this stimulus presentation. \n", + "`stimulus_name`: Indicates the stimulus category for this stimulus presentation. \n", "\n", - "*contrast*: Stimulus contrast\n", + "`contrast`: Stimulus contrast as defined [here](https://www.psychopy.org/api/visual/gratingstim.html#psychopy.visual.GratingStim.contrast)\n", "\n", - "*duration*: Duration of stimulus in seconds\n", + "`duration`: Duration of stimulus in seconds\n", "\n", - "*start_time*: Experiment time when this stimulus started. This value is corrected for display lag and therefore indicates when the stimulus actually appeared on the screen.\n", + "`start_time`: Experiment time when stimulus started. This value is corrected for display lag and therefore indicates when the stimulus actually appeared on the screen.\n", "\n", - "*stop_time*: Experiment time when this stimulus ended, also corrected for display lag.\n", + "`stop_time`: Experiment time when stimulus ended, also corrected for display lag.\n", "\n", - "*start_frame*: Stimulus frame index when this stimulus started. This can be used to sync this table to the behavior trials table, for which behavioral data is collected every frame.\n", + "`start_frame`: Stimulus frame index when this stimulus started. This can be used to sync this table to the behavior trials table, for which behavioral data is collected every frame.\n", "\n", - "*end_frame*: Stimulus frame index when this stimulus ended.\n", + "`end_frame`: Stimulus frame index when this stimulus ended.\n", "\n", "#### Change detection task and Passive replay (blocks 0 and 5)\n", "\n", - "*flashes_since_change*: Relevant for the detection of change task (block 0) and the passive replay (block 5), this column indicates how many flashes of the same image have occurred since the last stimulus change.\n", + "`flashes_since_change`: Indicates how many flashes of the same image have occurred since the last stimulus change.\n", "\n", - "*image_name*: Indicates which natural image was flashed for this stimulus presentation. To see how to visualize this image, check out this tutorial [LINK TO DATA ACCESS NOTEBOOK]\n", + "`image_name`: Indicates which natural image was flashed for this stimulus presentation. To see how to visualize this image, check out [this tutorial](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_neuropixels_data_access.html).\n", "\n", - "*is_change*: Indicates whether the image identity changed for this stimulus presentation. When both this value and 'active' are TRUE, the mouse was rewarded for licking within the response window.\n", + "`is_change`: Indicates whether the image identity changed for this stimulus presentation. When both this value and 'active' are TRUE, the mouse was rewarded for licking within the response window.\n", "\n", - "*omitted*: Indicates whether the image presentation was omitted for this flash. Most image flashes had a 5% probability of being omitted (producing a gray screen). Flashes immediately preceding a change or immediately following an omission could not be omitted.\n", + "`omitted`: Indicates whether the image presentation was omitted for this flash. Most image flashes had a 5% probability of being omitted (producing a gray screen). Flashes immediately preceding a change or immediately following an omission could not be omitted.\n", "\n", - "*rewarded*: Indicates whether a reward was given after this image presentation. During the passive replay block (5), this value indicates that a reward was issued for the corresponding image presenation during the active behavior block (0). No rewards were given during passive replay.\n", + "`rewarded`: Indicates whether a reward was given after this image presentation. During the passive replay block (5), this value indicates that a reward was issued for the corresponding image presentation during the active behavior block (0). No rewards were given during passive replay.\n", "\n", "#### Receptive field mapping gabor stimulus (block 2)\n", "\n", - "*orientation*: Orientation of gabor. \n", + "`orientation`: Orientation of gabor. \n", "\n", - "*position_x*: Position of the gabor along azimuth. The units are in degrees relative to the center of the screen (negative values are nasal).\n", + "`position_x`: Position of the gabor along azimuth. The units are in degrees relative to the center of the screen (negative values are nasal).\n", "\n", - "*position_y*: Position of the gabor along elevation. Negative values are lower elevation.\n", + "`position_y`: Position of the gabor along elevation. Negative values are lower elevation.\n", "\n", - "*spatial_frequency*: Spatial frequency of gabor in cycles per degree.\n", + "`spatial_frequency`: Spatial frequency of gabor in cycles per degree.\n", "\n", - "*temporal_frequency*: Temporal frequency of gabor in Hz.\n", + "`temporal_frequency`: Temporal frequency of gabor in Hz.\n", "\n", "#### Full field flashes (block 4)\n", "\n", - "*color*: Color of the full-field flash stimuli. \"1\" is white and \"-1\" is black.\n" + "`color`: Color of the full-field flash stimuli. \"1\" is white and \"-1\" is black.\n" ] }, { @@ -755,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -764,7 +383,7 @@ "True" ] }, - "execution_count": 37, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -775,6 +394,113 @@ "np.all(active_image_presentations['image_name'].values == passive_image_presentations['image_name'].values )" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking block 0 as an example, let's look at the timing of the stimuli. As a reminder, each flash was presented for 250 ms with 500 ms interflash intervals. In addition, flashes were occasionally omitted to investigate expectation signals:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's check the timing of our flashes and see how it compares to our intended timing:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#get the active behavior part of the stim table\n", + "active_behavior = stimulus_presentations[stimulus_presentations['active']==True]\n", + "\n", + "#for now, let's leave out the omitted stimuli\n", + "active_behavior_no_omissions = active_behavior[active_behavior['omitted']==False]\n", + "\n", + "#plot histogram of the stimulus durations\n", + "fig, axes = plt.subplots(1, 2)\n", + "fig.set_size_inches([10,4])\n", + "_ = axes[0].hist(active_behavior_no_omissions.duration, 50)\n", + "axes[0].set_xlabel('Flash Duration (s)')\n", + "axes[0].set_ylabel('Count')\n", + "\n", + "inter_flash = active_behavior_no_omissions['start_time'].diff()\n", + "_ = axes[1].hist(inter_flash, np.arange(0.7, 1.6, 0.05))\n", + "axes[1].set_xlabel('Inter-flash interval (s)')\n", + "axes[1].set_xticks(np.arange(0.75, 1.6, 0.25))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like the flash duration and interflash intervals are generally what we expect. Note though that a number of inter-flash intervals are twice as long as expected (1.5 s). This is because a small percentage of flashes are omitted. The chance of omitting a flash is nominally 5%, but we've also added two extra criteria:\n", + "\n", + "- two omissions can't happen in a row\n", + "- an omission can't directly precede the change\n", + "\n", + "This makes the actual chances of an omission a bit less than 5%:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.03418803418803419" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#look at the percentage of flashes that were omissions\n", + "np.sum(active_behavior.omitted)/len(active_behavior)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -791,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -822,12 +548,12 @@ " initial_image_name\n", " change_image_name\n", " stimulus_change\n", - " change_time\n", + " change_time_no_display_delay\n", " go\n", " catch\n", " lick_times\n", " response_time\n", - " ...\n", + " reward_time\n", " reward_volume\n", " hit\n", " false_alarm\n", @@ -837,7 +563,6 @@ " auto_rewarded\n", " change_frame\n", " trial_length\n", - " change_time_with_display_delay\n", " \n", " \n", " trials_id\n", @@ -861,24 +586,23 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " 0\n", - " 25.66618\n", - " 26.98348\n", - " im104_r\n", - " im104_r\n", + " 28.08763\n", + " 29.05453\n", + " im036_r\n", + " im036_r\n", " False\n", " NaN\n", " False\n", " False\n", - " [26.36584, 26.51607, 26.61606, 26.98348]\n", + " [28.55387, 28.73684, 29.30404]\n", " NaN\n", - " ...\n", - " 0.0\n", + " NaN\n", + " 0.000\n", " False\n", " False\n", " False\n", @@ -886,47 +610,45 @@ " True\n", " False\n", " NaN\n", - " 1.31730\n", - " NaN\n", + " 0.96690\n", " \n", " \n", " 1\n", - " 27.16692\n", - " 28.81836\n", - " im104_r\n", - " im104_r\n", + " 29.58829\n", + " 36.86108\n", + " im036_r\n", + " im078_r\n", + " True\n", + " 32.59106\n", " False\n", - " NaN\n", " False\n", + " [33.04048, 33.20773, 33.30745, 33.3908, 33.507...\n", + " 33.04048\n", + " 32.74138\n", + " 0.005\n", " False\n", - " [28.46755]\n", - " NaN\n", - " ...\n", - " 0.0\n", " False\n", " False\n", " False\n", " False\n", " True\n", - " False\n", - " NaN\n", - " 1.65144\n", - " NaN\n", + " 330.0\n", + " 7.27279\n", " \n", " \n", " 2\n", - " 29.41882\n", - " 31.25379\n", - " im104_r\n", - " im104_r\n", + " 37.09446\n", + " 40.78107\n", + " im078_r\n", + " im078_r\n", " False\n", " NaN\n", " False\n", " False\n", - " [30.63653, 30.76949, 30.90293]\n", + " [40.48052]\n", " NaN\n", - " ...\n", - " 0.0\n", + " NaN\n", + " 0.000\n", " False\n", " False\n", " False\n", @@ -934,47 +656,45 @@ " True\n", " False\n", " NaN\n", - " 1.83497\n", - " NaN\n", + " 3.68661\n", " \n", " \n", " 3\n", - " 31.67111\n", - " 33.23862\n", - " im104_r\n", - " im104_r\n", + " 40.84754\n", + " 50.37230\n", + " im078_r\n", + " im111_r\n", + " True\n", + " 46.10256\n", " False\n", - " NaN\n", " False\n", + " [46.73531, 46.83539, 46.95218, 47.06898, 47.20...\n", + " 46.73531\n", + " 46.25277\n", + " 0.005\n", " False\n", - " [32.88816, 33.22146, 33.33829, 33.42177, 33.52...\n", - " NaN\n", - " ...\n", - " 0.0\n", " False\n", " False\n", " False\n", " False\n", " True\n", - " False\n", - " NaN\n", - " 1.56751\n", - " NaN\n", + " 1140.0\n", + " 9.52476\n", " \n", " \n", " 4\n", - " 33.92263\n", - " 36.45841\n", - " im104_r\n", - " im104_r\n", + " 50.60569\n", + " 51.75679\n", + " im111_r\n", + " im111_r\n", " False\n", " NaN\n", " False\n", " False\n", - " [35.87392, 36.02384, 36.10722]\n", + " [51.43985]\n", " NaN\n", - " ...\n", - " 0.0\n", + " NaN\n", + " 0.000\n", " False\n", " False\n", " False\n", @@ -982,67 +702,55 @@ " True\n", " False\n", " NaN\n", - " 2.53578\n", - " NaN\n", + " 1.15110\n", " \n", " \n", "\n", - "

5 rows × 22 columns

\n", "" ], "text/plain": [ " start_time stop_time initial_image_name change_image_name \\\n", "trials_id \n", - "0 25.66618 26.98348 im104_r im104_r \n", - "1 27.16692 28.81836 im104_r im104_r \n", - "2 29.41882 31.25379 im104_r im104_r \n", - "3 31.67111 33.23862 im104_r im104_r \n", - "4 33.92263 36.45841 im104_r im104_r \n", + "0 28.08763 29.05453 im036_r im036_r \n", + "1 29.58829 36.86108 im036_r im078_r \n", + "2 37.09446 40.78107 im078_r im078_r \n", + "3 40.84754 50.37230 im078_r im111_r \n", + "4 50.60569 51.75679 im111_r im111_r \n", "\n", - " stimulus_change change_time go catch \\\n", - "trials_id \n", - "0 False NaN False False \n", - "1 False NaN False False \n", - "2 False NaN False False \n", - "3 False NaN False False \n", - "4 False NaN False False \n", + " stimulus_change change_time_no_display_delay go catch \\\n", + "trials_id \n", + "0 False NaN False False \n", + "1 True 32.59106 False False \n", + "2 False NaN False False \n", + "3 True 46.10256 False False \n", + "4 False NaN False False \n", "\n", " lick_times response_time \\\n", "trials_id \n", - "0 [26.36584, 26.51607, 26.61606, 26.98348] NaN \n", - "1 [28.46755] NaN \n", - "2 [30.63653, 30.76949, 30.90293] NaN \n", - "3 [32.88816, 33.22146, 33.33829, 33.42177, 33.52... NaN \n", - "4 [35.87392, 36.02384, 36.10722] NaN \n", - "\n", - " ... reward_volume hit false_alarm miss correct_reject \\\n", - "trials_id ... \n", - "0 ... 0.0 False False False False \n", - "1 ... 0.0 False False False False \n", - "2 ... 0.0 False False False False \n", - "3 ... 0.0 False False False False \n", - "4 ... 0.0 False False False False \n", + "0 [28.55387, 28.73684, 29.30404] NaN \n", + "1 [33.04048, 33.20773, 33.30745, 33.3908, 33.507... 33.04048 \n", + "2 [40.48052] NaN \n", + "3 [46.73531, 46.83539, 46.95218, 47.06898, 47.20... 46.73531 \n", + "4 [51.43985] NaN \n", "\n", - " aborted auto_rewarded change_frame trial_length \\\n", - "trials_id \n", - "0 True False NaN 1.31730 \n", - "1 True False NaN 1.65144 \n", - "2 True False NaN 1.83497 \n", - "3 True False NaN 1.56751 \n", - "4 True False NaN 2.53578 \n", + " reward_time reward_volume hit false_alarm miss \\\n", + "trials_id \n", + "0 NaN 0.000 False False False \n", + "1 32.74138 0.005 False False False \n", + "2 NaN 0.000 False False False \n", + "3 46.25277 0.005 False False False \n", + "4 NaN 0.000 False False False \n", "\n", - " change_time_with_display_delay \n", - "trials_id \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - "[5 rows x 22 columns]" + " correct_reject aborted auto_rewarded change_frame trial_length \n", + "trials_id \n", + "0 False True False NaN 0.96690 \n", + "1 False False True 330.0 7.27279 \n", + "2 False True False NaN 3.68661 \n", + "3 False False True 1140.0 9.52476 \n", + "4 False True False NaN 1.15110 " ] }, - "execution_count": 170, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1056,47 +764,47 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here you can see that unlike the stimulus presentations table in which every row corresponded to a visual stimulus presentation, for the behavior trials table every row corresponds to one trial of the change detection task. Here is a quick summary of the columns:\n", + "Unlike the stimulus presentations table in which every row corresponded to a visual stimulus presentation, for the behavior trials table every row corresponds to one trial of the change detection task. Here is a quick summary of the columns:\n", "\n", - "*start_time*: Experiment time when this trial began in seconds.\n", + "`start_time`: Experiment time when this trial began in seconds.\n", "\n", - "*stop_time*: Experiment time when this trial ended.\n", + "`stop_time`: Experiment time when this trial ended.\n", "\n", - "*initial_image_name*: Indicates which image was shown before the change (or sham change) for this trial\n", + "`initial_image_name`: Indicates which image was shown before the change (or sham change) for this trial\n", "\n", - "*change_image_name*: Indicates which image was scheduled to be the change image for this trial. Note that if the trial is aborted, a new trial will begin before this change occurs.\n", + "`change_image_name`: Indicates which image was scheduled to be the change image for this trial. Note that if the trial is aborted, a new trial will begin before this change occurs.\n", "\n", - "*stimulus_change*: Indicates whether an image change occurred for this trial. \n", + "`stimulus_change`: Indicates whether an image change occurred for this trial. \n", "\n", - "*change_time_no_display_delay*: Experiment time when the task-control computer commanded an image change. This change time is used to determine the response window during which a lick will trigger a reward. Note that due to display lag, this is not the time when the change image actually appears on the screen. To get this time, you need the stimulus_presentations table (more about this below).\n", + "`change_time_no_display_delay`: Experiment time when the task-control computer commanded an image change. This change time is used to determine the response window during which a lick will trigger a reward. Note that due to display lag, this is not the time when the change image actually appears on the screen. To get this time, you need the stimulus_presentations table (more about this below).\n", "\n", - "*go*: Indicates whether this trial was a 'go' trial. To qualify as a go trial, an image change must occur and the trial cannot be autorewarded.\n", + "`go`: Indicates whether this trial was a 'go' trial. To qualify as a go trial, an image change must occur and the trial cannot be autorewarded.\n", "\n", - "*catch*: Indicates whether this trial was a 'catch' trial. To qualify as a catch trial, a 'sham' change must occur during which the image identity does not change. These sham changes are drawn to match the timing distribution of real changes and can be used to calculate the false alarm rate.\n", + "`catch`: Indicates whether this trial was a 'catch' trial. To qualify as a catch trial, a 'sham' change must occur during which the image identity does not change. These sham changes are drawn to match the timing distribution of real changes and can be used to calculate the false alarm rate.\n", "\n", - "*lick_times*: A list indicating when the behavioral control software recognized a lick. Note that this is not identical to the lick times from the licks dataframe, which record when the licks were registered by the lick sensor. The licks dataframe should generally be used for analysis of the licking behavior rather than these times.\n", + "`lick_times`: A list indicating when the behavioral control software recognized a lick. Note that this is not identical to the lick times from the licks dataframe, which record when the licks were registered by the lick sensor. The licks dataframe should generally be used for analysis of the licking behavior rather than these times.\n", "\n", - "*response_time*: Indicates the time when the first lick was registered by the task control software for trials that were not aborted (go or catch). NaN for aborted trials. For a more accurate measure of response time, the licks dataframe should be used.\n", + "`response_time`: Indicates the time when the first lick was registered by the task control software for trials that were not aborted (go or catch). NaN for aborted trials. For a more accurate measure of response time, the licks dataframe should be used.\n", "\n", - "*reward_time*: Indicates when the reward command was triggered for hit trials. NaN for other trial types. \n", + "`reward_time`: Indicates when the reward command was triggered for hit trials. NaN for other trial types. \n", "\n", - "*reward_volume*: Indicates the volume of water dispensed as reward for this trial. \n", + "`reward_volume`: Indicates the volume of water dispensed as reward for this trial. \n", "\n", - "*hit*: Indicates whether this trial was a 'hit' trial. To qualify as a hit, the trial must be a go trial during which the stimulus changed and the mouse licked within the reward window (150-750 ms after the change time).\n", + "`hit`: Indicates whether this trial was a 'hit' trial. To qualify as a hit, the trial must be a go trial during which the stimulus changed and the mouse licked within the reward window (150-750 ms after the change time).\n", "\n", - "*false_alarm*: Indicates whether this trial was a 'false alarm' trial. To qualify as a false alarm, the trial must be a catch trial during which a sham change occurred and the mouse licked during the reward window.\n", + "`false_alarm`: Indicates whether this trial was a 'false alarm' trial. To qualify as a false alarm, the trial must be a catch trial during which a sham change occurred and the mouse licked during the reward window.\n", "\n", - "*miss*: To qualify as a miss trial, the trial must be a go trial during which the stimulus changed but the mouse did not lick within the response window.\n", + "`miss`: To qualify as a miss trial, the trial must be a go trial during which the stimulus changed but the mouse did not lick within the response window.\n", "\n", - "*correct_reject*: To qualify as a correct reject trial, the trial must be a catch trial during which a sham change occurred and the mouse withheld licking.\n", + "`correct_reject`: To qualify as a correct reject trial, the trial must be a catch trial during which a sham change occurred and the mouse withheld licking.\n", "\n", - "*aborted*: A trial is aborted when the mouse licks before the scheduled change or sham change.\n", + "`aborted`: A trial is aborted when the mouse licks before the scheduled change or sham change.\n", "\n", - "*auto_rewarded*: During autorewarded trials, the reward is automatically triggered after the change regardless of whether the mouse licked within the response window. These always come at the beginning of the session to help engage the mouse in behavior.\n", + "`auto_rewarded`: During autorewarded trials, the reward is automatically triggered after the change regardless of whether the mouse licked within the response window. These always come at the beginning of the session to help engage the mouse in behavior.\n", "\n", - "*change_frame*: Indicates the stimulus frame index when the change occurred. This column can be used to link the trials table with the stimulus presentations table, as shown below.\n", + "`change_frame`: Indicates the stimulus frame index when the change (on go trials) or sham change (on catch trials) occurred. This column can be used to link the trials table with the stimulus presentations table, as shown below.\n", "\n", - "*trial_length*: Duration of the trial in seconds." + "`trial_length`: Duration of the trial in seconds." ] }, { @@ -1110,24 +818,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's combine info from both of these tables to calculate response latency for this session. Note that the change time in the trials table is not corrected for display lag. This is the time that the task control computer uses to determine the response window. However, to calculate response latency, we want to use the display lag *corrected* change times from the stimulus presentations table. Below, we will grab these corrected times and add them to the trials table under the new column label 'change_time_with_display_delay'." + "Let's combine info from both of these tables to calculate response latency for this session. Note that the change time in the trials table is not corrected for display lag. This is the time that the task control computer used to determine the response window. However, to calculate response latency, we want to use the display lag *corrected* change times from the stimulus presentations table. Below, we will grab these corrected times and add them to the trials table under the new column label `change_time_with_display_delay`." ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "from functools import partial\n", - "\n", - "def get_change_time_from_stim_table(row, table):\n", + "def get_change_time_from_stim_table(row):\n", " '''\n", " Given a particular row in the trials table,\n", " find the corresponding change time in the \n", " stimulus presentations table\n", " '''\n", - " \n", + " table = stimulus_presentations\n", " change_frame = row['change_frame']\n", " if np.isnan(change_frame):\n", " return np.nan\n", @@ -1137,8 +843,7 @@ " \n", " return change_time\n", "\n", - "get_change_times = partial(get_change_time_from_stim_table, table=stimulus_presentations)\n", - "change_times = trials.apply(get_change_times, axis=1)\n", + "change_times = trials.apply(get_change_time_from_stim_table, axis=1)\n", "trials['change_time_with_display_delay'] = change_times" ] }, @@ -1146,60 +851,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can use this new column to calculate the response latency on 'hit' trials." + "Now we can use this new column to calculate the response latency on 'hit' trials. First, we'll need to get the lick times for this session:" ] }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Trial count')" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": 24, + "metadata": {}, + "outputs": [], "source": [ - "hit_trials = trials[trials['hit']]\n", - "response_latencies = hit_trials['response_time'] - hit_trials['change_time_with_display_delay']\n", - "fig, ax = plt.subplots()\n", - "fig.suptitle('Response Latency Histogram for Hit trials')\n", - "ax.hist(response_latencies, bins=np.linspace(-0.1, 0.8, 50))\n", - "ax.set_xlabel('Time from change (s)')\n", - "ax.set_ylabel('Trial count')\n" + "# get the licks table\n", + "licks = session.licks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that there is one trial with a negative response latency. This happens when a lick immediately precedes the change, and the task control software doesn't have time to abort the trial. To restrict ourselves to only those licks that occur during the response window, we can do the following:" + "Then we'll use these to get the response latency:" ] }, { "cell_type": "code", - "execution_count": 75, - "metadata": {}, + "execution_count": 35, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1207,13 +884,13 @@ "Text(0, 0.5, 'Trial count')" ] }, - "execution_count": 75, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1225,14 +902,15 @@ } ], "source": [ - "response_window_lick_times = []\n", - "for it, trial in hit_trials.iterrows():\n", - " lick_times = trial['lick_times']\n", - " response_window_start = trial['change_time'] + 0.15\n", - " response_window_lick_time = lick_times[lick_times>response_window_start][0]\n", - " response_window_lick_times.append(response_window_lick_time)\n", - " \n", - "response_latencies = response_window_lick_times - hit_trials['change_time_with_display_delay'].values\n", + "# filter for the hit trials\n", + "hit_trials = trials[trials['hit']]\n", + "\n", + "# find the time of the first lick after each change\n", + "lick_indices = np.searchsorted(licks.timestamps, hit_trials['change_time_with_display_delay'])\n", + "first_lick_times = licks.timestamps.values[lick_indices]\n", + "response_latencies = first_lick_times - hit_trials['change_time_with_display_delay']\n", + "\n", + "# plot the latencies\n", "fig, ax = plt.subplots()\n", "fig.suptitle('Response Latency Histogram for Hit trials')\n", "ax.hist(response_latencies, bins=np.linspace(-0.1, 0.8, 50))\n", @@ -1256,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1274,7 +952,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1348,7 +1026,7 @@ " \n", " \n", " 0\n", - " 1.34174\n", + " 1.98179\n", " NaN\n", " NaN\n", " NaN\n", @@ -1372,7 +1050,7 @@ " \n", " \n", " 1\n", - " 1.35840\n", + " 1.99846\n", " NaN\n", " NaN\n", " NaN\n", @@ -1396,7 +1074,7 @@ " \n", " \n", " 2\n", - " 1.37507\n", + " 2.01512\n", " NaN\n", " NaN\n", " NaN\n", @@ -1420,7 +1098,7 @@ " \n", " \n", " 3\n", - " 1.39174\n", + " 2.03179\n", " NaN\n", " NaN\n", " NaN\n", @@ -1444,7 +1122,7 @@ " \n", " \n", " 4\n", - " 1.40840\n", + " 2.04846\n", " NaN\n", " NaN\n", " NaN\n", @@ -1474,11 +1152,11 @@ "text/plain": [ " timestamps cr_area eye_area pupil_area likely_blink \\\n", "frame \n", - "0 1.34174 NaN NaN NaN True \n", - "1 1.35840 NaN NaN NaN True \n", - "2 1.37507 NaN NaN NaN True \n", - "3 1.39174 NaN NaN NaN True \n", - "4 1.40840 NaN NaN NaN True \n", + "0 1.98179 NaN NaN NaN True \n", + "1 1.99846 NaN NaN NaN True \n", + "2 2.01512 NaN NaN NaN True \n", + "3 2.03179 NaN NaN NaN True \n", + "4 2.04846 NaN NaN NaN True \n", "\n", " pupil_area_raw cr_area_raw eye_area_raw cr_center_x cr_center_y \\\n", "frame \n", @@ -1507,7 +1185,7 @@ "[5 rows x 23 columns]" ] }, - "execution_count": 84, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1525,7 +1203,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1598,124 +1276,124 @@ " \n", " \n", " \n", + " 16\n", + " 2.69844\n", + " 143.319910\n", + " 64100.607811\n", + " 18344.446003\n", + " False\n", + " 18344.446003\n", + " 143.319910\n", + " 64100.607811\n", + " 307.495920\n", + " 253.050384\n", + " ...\n", + " 293.215171\n", + " 252.429377\n", + " 154.298080\n", + " 132.236624\n", + " -0.045388\n", + " 296.119363\n", + " 245.965454\n", + " 76.414779\n", + " 75.552596\n", + " 0.186599\n", + " \n", + " \n", " 17\n", - " 1.65840\n", - " 132.486115\n", - " 70625.134735\n", - " 18292.276485\n", - " False\n", - " 18292.276485\n", - " 132.486115\n", - " 70625.134735\n", - " 349.237369\n", - " 283.728001\n", + " 2.71512\n", + " 144.637343\n", + " 64128.814923\n", + " 18500.396156\n", + " False\n", + " 18500.396156\n", + " 144.637343\n", + " 64128.814923\n", + " 307.703577\n", + " 252.995883\n", " ...\n", - " 329.009419\n", - " 275.387574\n", - " 163.572471\n", - " 137.435587\n", - " 0.021028\n", - " 349.972230\n", - " 270.666912\n", - " 74.207993\n", - " 76.306045\n", - " -0.371412\n", + " 293.465116\n", + " 252.383047\n", + " 154.610779\n", + " 132.027248\n", + " -0.047398\n", + " 296.156537\n", + " 246.179364\n", + " 76.738901\n", + " 75.066741\n", + " 0.071866\n", " \n", " \n", " 18\n", - " 1.67508\n", - " 133.196091\n", - " 70636.186432\n", - " 18187.494820\n", - " False\n", - " 18187.494820\n", - " 133.196091\n", - " 70636.186432\n", - " 349.287138\n", - " 284.020121\n", + " 2.73178\n", + " 138.430143\n", + " 64228.144166\n", + " 18768.526187\n", + " False\n", + " 18768.526187\n", + " 138.430143\n", + " 64228.144166\n", + " 307.929085\n", + " 253.125091\n", " ...\n", - " 329.213380\n", - " 275.846254\n", - " 163.453674\n", - " 137.556997\n", - " 0.027826\n", - " 350.034210\n", - " 270.571270\n", - " 74.254708\n", - " 76.087183\n", - " -0.527341\n", + " 293.563823\n", + " 252.333781\n", + " 154.851436\n", + " 132.026242\n", + " -0.049470\n", + " 296.457157\n", + " 245.953752\n", + " 77.292997\n", + " 75.030102\n", + " 0.000100\n", " \n", " \n", " 19\n", - " 1.69174\n", - " 142.640850\n", - " 70558.979068\n", - " 18200.499170\n", - " False\n", - " 18200.499170\n", - " 142.640850\n", - " 70558.979068\n", - " 349.460331\n", - " 284.599632\n", + " 2.74845\n", + " 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1.72507\n", - " 143.348989\n", - " 70690.285097\n", - " 18107.560769\n", - " False\n", - " 18107.560769\n", - " 143.348989\n", - " 70690.285097\n", - " 349.244917\n", - " 284.382902\n", + " 2.76511\n", + " 153.408346\n", + " 64446.974420\n", + " 18824.413215\n", + " False\n", + " 18824.413215\n", + " 153.408346\n", + " 64446.974420\n", + " 307.556380\n", + " 253.339529\n", " ...\n", - " 329.352378\n", - " 276.858677\n", - " 163.326985\n", - " 137.769130\n", - " 0.032516\n", - " 350.052768\n", - " 271.331949\n", - " 74.116918\n", - " 75.919797\n", - " -0.415238\n", + " 293.251817\n", + " 252.265215\n", + " 154.720970\n", + " 132.587775\n", + " -0.040922\n", + " 296.259293\n", + " 246.003218\n", + " 77.407989\n", + " 75.440085\n", + " -0.036076\n", " \n", " \n", "\n", @@ -1725,40 +1403,40 @@ "text/plain": [ " timestamps cr_area eye_area pupil_area likely_blink \\\n", "frame \n", - "17 1.65840 132.486115 70625.134735 18292.276485 False \n", - "18 1.67508 133.196091 70636.186432 18187.494820 False \n", - "19 1.69174 142.640850 70558.979068 18200.499170 False \n", - "20 1.70841 145.368885 70492.200841 18144.662960 False \n", - "21 1.72507 143.348989 70690.285097 18107.560769 False \n", + "16 2.69844 143.319910 64100.607811 18344.446003 False \n", + "17 2.71512 144.637343 64128.814923 18500.396156 False \n", + "18 2.73178 138.430143 64228.144166 18768.526187 False \n", + "19 2.74845 140.384821 64356.597629 18790.177151 False \n", + "20 2.76511 153.408346 64446.974420 18824.413215 False \n", "\n", " pupil_area_raw cr_area_raw eye_area_raw cr_center_x cr_center_y \\\n", "frame \n", - "17 18292.276485 132.486115 70625.134735 349.237369 283.728001 \n", - "18 18187.494820 133.196091 70636.186432 349.287138 284.020121 \n", - "19 18200.499170 142.640850 70558.979068 349.460331 284.599632 \n", - "20 18144.662960 145.368885 70492.200841 349.463427 284.728674 \n", - "21 18107.560769 143.348989 70690.285097 349.244917 284.382902 \n", + "16 18344.446003 143.319910 64100.607811 307.495920 253.050384 \n", + "17 18500.396156 144.637343 64128.814923 307.703577 252.995883 \n", + "18 18768.526187 138.430143 64228.144166 307.929085 253.125091 \n", + "19 18790.177151 140.384821 64356.597629 307.710460 253.179979 \n", + "20 18824.413215 153.408346 64446.974420 307.556380 253.339529 \n", "\n", " ... eye_center_x eye_center_y eye_width eye_height eye_phi \\\n", "frame ... \n", - "17 ... 329.009419 275.387574 163.572471 137.435587 0.021028 \n", - "18 ... 329.213380 275.846254 163.453674 137.556997 0.027826 \n", - "19 ... 329.696022 277.131676 163.398004 137.453457 0.014976 \n", - "20 ... 330.251743 277.005598 163.079554 137.591524 0.033417 \n", - "21 ... 329.352378 276.858677 163.326985 137.769130 0.032516 \n", + "16 ... 293.215171 252.429377 154.298080 132.236624 -0.045388 \n", + "17 ... 293.465116 252.383047 154.610779 132.027248 -0.047398 \n", + "18 ... 293.563823 252.333781 154.851436 132.026242 -0.049470 \n", + "19 ... 293.503738 252.582299 154.705897 132.414741 -0.049801 \n", + "20 ... 293.251817 252.265215 154.720970 132.587775 -0.040922 \n", "\n", " pupil_center_x pupil_center_y pupil_width pupil_height pupil_phi \n", "frame \n", - "17 349.972230 270.666912 74.207993 76.306045 -0.371412 \n", - "18 350.034210 270.571270 74.254708 76.087183 -0.527341 \n", - "19 350.428484 271.797634 74.268068 76.114380 -0.428505 \n", - "20 350.709697 271.863534 74.039046 75.997537 -0.416278 \n", - "21 350.052768 271.331949 74.116918 75.919797 -0.415238 \n", + "16 296.119363 245.965454 76.414779 75.552596 0.186599 \n", + "17 296.156537 246.179364 76.738901 75.066741 0.071866 \n", + "18 296.457157 245.953752 77.292997 75.030102 0.000100 \n", + "19 296.389647 246.022947 77.337566 75.251635 0.004288 \n", + "20 296.259293 246.003218 77.407989 75.440085 -0.036076 \n", "\n", "[5 rows x 23 columns]" ] }, - "execution_count": 167, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1777,7 +1455,7 @@ }, { "cell_type": "code", - 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+1625,12 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1966,7 +1644,7 @@ "source": [ "time_before = 3.0 #how much time to plot before the reward\n", "time_after = 3.0 #how much time to plot after the reward\n", - "reward_time = session.rewards.iloc[10]['timestamps'] #get the time of the 10th reward\n", + "reward_time = session.rewards.iloc[15]['timestamps'] #get a random reward time\n", "\n", "#Get running data aligned to this reward\n", "trial_running = running_speed.query('timestamps >= {} and timestamps <= {} '.\n", @@ -1978,8 +1656,8 @@ "\n", "#Get stimulus presentations around this reward\n", "behavior_presentations = stimulus_presentations[stimulus_presentations['active']]\n", - "behavior_presentations.at[:,'omitted'] = behavior_presentations['omitted'].astype('bool')\n", - "trial_stimuli = behavior_presentations.query('stop_time >= {} and start_time <= {} and not omitted'.\n", + "behavior_presentations = behavior_presentations[(behavior_presentations['omitted']==False)]\n", + "trial_stimuli = behavior_presentations.query('stop_time >= {} and start_time <= {}'.\n", " format(reward_time-time_before, reward_time+time_after))\n", "\n", "#Get licking aligned to this reward\n", @@ -2016,13 +1694,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we can see that just after the stimulus change (a little past 202 seconds), the mouse abruptly stops running and begins licking. The reward is delivered shortly after the first lick. We can also see that before the change the pupil and the running become entrained to the image flashes." + "Here we can see that just after the stimulus change (a little past 449 seconds), the mouse abruptly stops running and begins licking. The reward is delivered shortly after the first lick. We can also begin to see that before the change the pupil and running become entrained to the image flashes." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2036,7 +1714,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "37dddc7a9b48c904247bafdbf077e38e92e263740137f62b55c265f377f81e70" + } } }, "nbformat": 4, diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_data_access.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_data_access.ipynb old mode 100644 new mode 100755 index 58402b551..5cdd5c632 --- a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_data_access.ipynb +++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_data_access.ipynb @@ -19,7 +19,7 @@ "\n", "This data release will not have a web interface for browsing through the released data, as with the [two-photon imaging Visual Coding dataset](http://observatory.brain-map.org/visualcoding). Instead, the data must be retrieved through the AllenSDK (Python 3.6+) or via requests sent to the **Amazon Web Services (AWS)** **Simple Storage Service (S3)** bucket (name: [visual-behavior-neuropixels-data](https://s3.console.aws.amazon.com/s3/buckets/visual-behavior-neuropixels-data)) for this project.\n", "\n", - "Functions related to data analysis as well as descriptions of metadata table columns will be covered in other tutorials. For a full list of available tutorials for this project, see the [SDK documentation](https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html)." + "Functions related to data analysis as well as descriptions of metadata table columns will be covered in other tutorials. For a full list of available tutorials for this project, see the [SDK documentation](https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html)." ] }, { @@ -61,7 +61,7 @@ "id": "f13f8c95", "metadata": {}, "source": [ - "## Instal AllenSDK into your local environment" + "## Install AllenSDK into your local environment" ] }, { @@ -74,10 +74,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "abd813fe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: allensdk in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (2.13.5)\n", + "Requirement already satisfied: boto3==1.17.21 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (1.17.21)\n", + "Requirement already satisfied: pynwb in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (2.1.0)\n", + "Requirement already satisfied: nest-asyncio in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (1.5.5)\n", + "Requirement already satisfied: sqlalchemy in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (1.4.39)\n", + "Requirement already satisfied: requests-toolbelt<1.0.0 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (0.9.1)\n", + "Requirement already satisfied: h5py in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (3.7.0)\n", + "Requirement already satisfied: numpy in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (1.22.4)\n", + "Requirement already satisfied: aiohttp==3.7.4 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (3.7.4)\n", + "Requirement already satisfied: cachetools<5.0.0,>=4.2.1 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (4.2.4)\n", + "Requirement already satisfied: statsmodels<=0.13.0 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (0.13.0)\n", + "Requirement already satisfied: ndx-events<=0.2.0 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (0.2.0)\n", + "Requirement already satisfied: psycopg2-binary<3.0.0,>=2.7 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (2.9.3)\n", + "Requirement already satisfied: pandas>=1.1.5 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (1.4.3)\n", + "Requirement already satisfied: xarray<0.16.0 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from allensdk) (0.15.1)\n", + "Requirement already satisfied: hdmf>=2.5.8 in c:\\users\\corbettb\\anaconda3\\envs\\allensdk_38\\lib\\site-packages (from 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"execution_count": 1, + "execution_count": 2, "id": "9d2f1f6d", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\corbettb\\Anaconda3\\envs\\allensdk_38\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -155,25 +244,34 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ca3f1f01", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "ecephys_sessions.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 63.5k/63.5k [00:00<00:00, 146kMB/s]\n", - "behavior_sessions.csv: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:00<00:00, 959kMB/s]\n", - "units.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 132M/132M [00:14<00:00, 8.87MMB/s]\n", - "probes.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 127k/127k [00:00<00:00, 614kMB/s]\n", - "channels.csv: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 27.9M/27.9M [00:09<00:00, 3.01MMB/s]\n" + "ename": "PermissionError", + "evalue": "[WinError 5] Access is denied: '\\\\allen'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPermissionError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Update this to a valid directory in your filesystem. This is where the data will be stored.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m data_storage_directory \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mallen\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mprograms\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mmindscope\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mworkgroups\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mp-behavior\u001b[39m\u001b[38;5;130;01m\\v\u001b[39;00m\u001b[38;5;124mbn_data_release\u001b[39m\u001b[38;5;130;01m\\v\u001b[39;00m\u001b[38;5;124mbn_s3_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m cache \u001b[38;5;241m=\u001b[39m \u001b[43mVisualBehaviorNeuropixelsProjectCache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_s3_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_storage_directory\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\site-packages\\allensdk\\brain_observatory\\behavior\\behavior_project_cache\\project_cache_base.py:76\u001b[0m, in \u001b[0;36mProjectCacheBase.from_s3_cache\u001b[1;34m(cls, cache_dir)\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_s3_cache\u001b[39m(\u001b[38;5;28mcls\u001b[39m, cache_dir: Union[\u001b[38;5;28mstr\u001b[39m, Path]\n\u001b[0;32m 52\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProjectCacheBase\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124;03m\"\"\"instantiates this object with a connection to an s3 bucket and/or\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \u001b[38;5;124;03m a local cache related to that bucket.\u001b[39;00m\n\u001b[0;32m 55\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 73\u001b[0m \n\u001b[0;32m 74\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m fetch_api \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcloud_api_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_s3_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 77\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 78\u001b[0m \u001b[43m \u001b[49m\u001b[43mbucket_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBUCKET_NAME\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 79\u001b[0m \u001b[43m \u001b[49m\u001b[43mproject_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPROJECT_NAME\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 80\u001b[0m \u001b[43m \u001b[49m\u001b[43mui_class_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(fetch_api\u001b[38;5;241m=\u001b[39mfetch_api)\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\site-packages\\allensdk\\brain_observatory\\behavior\\behavior_project_cache\\project_apis\\data_io\\project_cloud_api_base.py:108\u001b[0m, in \u001b[0;36mProjectCloudApiBase.from_s3_cache\u001b[1;34m(cls, cache_dir, bucket_name, project_name, ui_class_name)\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_s3_cache\u001b[39m(\u001b[38;5;28mcls\u001b[39m, cache_dir: Union[\u001b[38;5;28mstr\u001b[39m, Path],\n\u001b[0;32m 80\u001b[0m bucket_name: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m 81\u001b[0m project_name: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m 82\u001b[0m ui_class_name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProjectCloudApiBase\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 83\u001b[0m \u001b[38;5;124;03m\"\"\"instantiates this object with a connection to an s3 bucket and/or\u001b[39;00m\n\u001b[0;32m 84\u001b[0m \u001b[38;5;124;03m a local cache related to that bucket.\u001b[39;00m\n\u001b[0;32m 85\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 106\u001b[0m \n\u001b[0;32m 107\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m cache \u001b[38;5;241m=\u001b[39m \u001b[43mS3CloudCache\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mbucket_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43mproject_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 111\u001b[0m \u001b[43m \u001b[49m\u001b[43mui_class_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mui_class_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(cache)\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\site-packages\\allensdk\\api\\cloud_cache\\cloud_cache.py:1040\u001b[0m, in \u001b[0;36mS3CloudCache.__init__\u001b[1;34m(self, cache_dir, bucket_name, project_name, ui_class_name)\u001b[0m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manifest \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1038\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bucket_name \u001b[38;5;241m=\u001b[39m bucket_name\n\u001b[1;32m-> 1040\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproject_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproject_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1041\u001b[0m \u001b[43m \u001b[49m\u001b[43mui_class_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mui_class_name\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\site-packages\\allensdk\\api\\cloud_cache\\cloud_cache.py:365\u001b[0m, in \u001b[0;36mCloudCacheBase.__init__\u001b[1;34m(self, cache_dir, project_name, ui_class_name)\u001b[0m\n\u001b[0;32m 364\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, cache_dir, project_name, ui_class_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 365\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproject_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproject_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 366\u001b[0m \u001b[43m \u001b[49m\u001b[43mui_class_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mui_class_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 368\u001b[0m \u001b[38;5;66;03m# what latest_manifest was the last time an OutdatedManifestWarning\u001b[39;00m\n\u001b[0;32m 369\u001b[0m \u001b[38;5;66;03m# was emitted\u001b[39;00m\n\u001b[0;32m 370\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manifest_last_warned_on \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\site-packages\\allensdk\\api\\cloud_cache\\cloud_cache.py:63\u001b[0m, in \u001b[0;36mBasicLocalCache.__init__\u001b[1;34m(self, cache_dir, project_name, ui_class_name)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 59\u001b[0m cache_dir: Union[\u001b[38;5;28mstr\u001b[39m, Path],\n\u001b[0;32m 60\u001b[0m project_name: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m 61\u001b[0m ui_class_name: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 62\u001b[0m ):\n\u001b[1;32m---> 63\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 65\u001b[0m \u001b[38;5;66;03m# the class users are actually interacting with\u001b[39;00m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;66;03m# (for warning message purposes)\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ui_class_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[0;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[0;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[0;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32m~\\Anaconda3\\envs\\allensdk_38\\lib\\os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 223\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m 225\u001b[0m \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[0;32m 226\u001b[0m \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[0;32m 227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n", + "\u001b[1;31mPermissionError\u001b[0m: [WinError 5] Access is denied: '\\\\allen'" ] } ], "source": [ - "# Update this to a valid directory in your filesystem\n", - "data_storage_directory = Path(\"/tmp/vbn_cache\")\n", + "# Update this to a valid directory in your filesystem. This is where the data will be stored.\n", + "data_storage_directory = Path(\"\\\\allen\\programs\\mindscope\\workgroups\\np-behavior\\vbn_data_release\\vbn_s3_cache\")\n", "\n", "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(cache_dir=data_storage_directory)" ] @@ -346,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "88d47216", "metadata": {}, "outputs": [ @@ -356,7 +454,7 @@ "'visual-behavior-neuropixels_project_manifest_v0.3.0.json'" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -495,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "663a4a46", "metadata": {}, "outputs": [ @@ -776,7 +874,7 @@ "[5 rows x 21 columns]" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -796,7 +894,7 @@ "source": [ "The `ecephys_session_table` DataFrame provides a high-level overview for ecephys sessions in the Visual Behavior Neurpoixels dataset. The index column (ecephys_session_id) is a unique ID, which serves as a key for access behavior data for each session. To get additional information about this data table (and other tables) please visit [this example notebook](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_neuropixels_quickstart.html).\n", "\n", - "Sharp eyed readers may be wondering why the number of behavior session (103) in this table does not match up with the number of NWB files in the data release (153). Some of the session being released had obvious abnormalities in either electrophysiological activity or histology. By default `get_ecephys_session_table()` does not return the metadata for these abnormal sessions. To see all 153 sessions, run `get_ecephys_session_table(filter_abnormalities=False)`\n" + "Sharp eyed readers may be wondering why the number of behavior session (103) in this table does not match up with the number of NWB files in the data release (153). Some of the sessions being released had obvious abnormalities in either electrophysiological activity or histology. By default `get_ecephys_session_table()` does not return the metadata for these abnormal sessions. To see all 153 sessions, run `get_ecephys_session_table(filter_abnormalities=False)`\n" ] }, { @@ -1457,7 +1555,7 @@ "id": "a3a3aa5b", "metadata": {}, "source": [ - "This table records metadata for each individual channel on the neuropixels probes encompassed in this release. Channels are identified by a unique identifier (`ecephys_channel_id`) and can be linked to probes via the `ecephys_probe_id` column and to sessiosn via the `ecephys_session_id` column." + "This table records metadata for each individual channel on the neuropixels probes encompassed in this release. Channels are identified by a unique identifier (`ecephys_channel_id`) and can be linked to probes via the `ecephys_probe_id` column and to sessions via the `ecephys_session_id` column." ] }, { @@ -2243,7 +2341,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.0" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_dataset_manifest.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_dataset_manifest.ipynb new file mode 100755 index 000000000..6baf886c0 --- /dev/null +++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_dataset_manifest.ipynb @@ -0,0 +1,2873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0e1962d7", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Identifying experiments and sessions of interest using the data manifest" + ] + }, + { + "cell_type": "markdown", + "id": "ec4a1f9b", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "This Jupyter notebook illustrates what data is available as part of the Visual Behavior Neuropixels dataset, and provides a brief description of the experimental design and dimensions of the dataset. The notebook will demonstrate how to identify experiments and sessions that you may be interested in analyzing using the data manifests provided by the `VisualBehaviorNeuropixelsProjectCache`, and explore the metadata columns that describe the experimental conditions including transgenic lines, targeted areas, and session types. \n", + "\n", + "Contents\n", + "-------------\n", + "* Introduction to the metadata tables\n", + "* Ecephys Sessions Table\n", + "* Behavior Sessions Table\n", + "* Units, Probes and Channels Table\n", + "\n", + "\n", + "We will first install allensdk into your environment by running the appropriate commands below. " + ] + }, + { + "cell_type": "markdown", + "id": "6cb44c62", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Install AllenSDK into your local environment" + ] + }, + { + "cell_type": "markdown", + "id": "865ff65c", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "You can install AllenSDK locally with:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1e1a8fa1", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: allensdk in /Users/corbettb/Documents/GitHub/AllenSDK (2.13.5)\n", + "Requirement already satisfied: psycopg2-binary<3.0.0,>=2.7 in /opt/anaconda3/envs/allensdk_38/lib/python3.8/site-packages (from allensdk) (2.9.3)\n", + "Requirement already satisfied: hdmf>=2.5.8 in /opt/anaconda3/envs/allensdk_38/lib/python3.8/site-packages (from allensdk) (3.3.1)\n", + "Requirement already 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+ } + }, + "source": [ + "## Install AllenSDK into your notebook environment (good for Google Colab)" + ] + }, + { + "cell_type": "markdown", + "id": "ea941074", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "You can install AllenSDK into your notebook environment by executing the cell below.\n", + "\n", + "If using Google Colab, click on the RESTART RUNTIME button that appears at the end of the output when this cell is complete,. Note that running this cell will produce a long list of outputs and some error messages. Clicking RESTART RUNTIME at the end will resolve these issues.\n", + "You can minimize the cell after you are done to hide the output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bdcd29f", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!pip install --upgrade pip\n", + "!pip install allensdk" + ] + }, + { + "cell_type": "markdown", + "id": "ea33bb7f", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9c36b30f", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "\n", + "import allensdk\n", + "from allensdk.brain_observatory.behavior.behavior_project_cache.\\\n", + " behavior_neuropixels_project_cache \\\n", + " import VisualBehaviorNeuropixelsProjectCache" + ] + }, + { + "cell_type": "markdown", + "id": "33a70aec", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## First, load the project cache - your access point for all tables and data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6c802471", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Update this to a valid directory in your filesystem. This is where the data will be stored.\n", + "cache_dir = Path(\"/path/to/vbn_cache\")\n", + "\n", + "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(\n", + " cache_dir=cache_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "dc633e51", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Introduction to the metadata tables" + ] + }, + { + "cell_type": "markdown", + "id": "4e80df4a", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The data manifest is comprised of 5 types of tables: \n", + "\n", + "1. `ecephys_sessions_table` (64 kB)\n", + "2. `behavior_sessions_table` (531 kB)\n", + "3. `units_table` (130 MB)\n", + "4. `probes_table` (127 kB)\n", + "5. `channels_table` (28 MB)\n", + "\n", + "The `ecephys_sessions_table` contains metadata for every Neuropixels recording session in the dataset. We attempted to insert 6 probes for each experiment, but occasionally individual insertions failed. The `probe_count` column tells you how many probes were inserted for a given session. The `structure_acronyms` column indicates which brain areas were targeted. For the majority of mice, there are two recording sessions. These were run on consecutive days with two different image sets, `G` and `H`. The `experience_level` column tells you whether the image set used for a particular recording was the same as the training image set (`Familiar`), or different from the training image set (`Novel`).\n", + "\n", + "The `behavior_sessions_table` contains metadata for each behavior session. Some behavior sessions have Neuropixels data associated with them, while others took place during training in the behavior facility. The different training stages that mice progressed through are described by the `session_type`. \n", + "\n", + "The `units_table` contains metadata for every unit in the release. Each unit can be linked to the corresponding recording session, probe and channel by the `ecephys_session_id`, `ecephys_probe_id` and `ecephys_channel_id` columns. This table also contains a number of helpful quality metrics, which can be used to filter out contaminated units before analysis. For more guidance on how to use these metrics, check out [this tutorial](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_neuropixels_quality_metrics.html).\n", + "\n", + "The `probes_table` contains metadata for each probe insertion.\n", + "\n", + "The `channels_table` contains metadata for each channel recorded during an ephys session. This table provides useful info about where a particular channel is located in the Allen Common Coordinate Framework as well as it's relative position on the probe.\n", + "\n", + "Now let's look at a few of these tables in more detail to get a better sense of the dataset." + ] + }, + { + "cell_type": "markdown", + "id": "1588f714", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Ecephys Sessions Table" + ] + }, + { + "cell_type": "markdown", + "id": "663288ba", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Load the `ecephys_sessions_table` from the cache\n", + "\n", + "First let's just look at the columns to see what metadata is provided for each session:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "05728d58", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['behavior_session_id', 'date_of_acquisition', 'equipment_name',\n", + " 'session_type', 'mouse_id', 'genotype', 'sex', 'project_code',\n", + " 'age_in_days', 'unit_count', 'probe_count', 'channel_count',\n", + " 'structure_acronyms', 'image_set', 'prior_exposures_to_image_set',\n", + " 'session_number', 'experience_level', 'prior_exposures_to_omissions',\n", + " 'file_id', 'abnormal_histology', 'abnormal_activity'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecephys_sessions_table = cache.get_ecephys_session_table()\n", + "ecephys_sessions_table.columns" + ] + }, + { + "cell_type": "markdown", + "id": "678e592b", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "This table gives us lots of useful metadata about each recording session, including the genotype, sex and age of the mouse that was run, what brain areas were recorded and some important info about the stimulus. \n", + "\n", + "To demystify a few of these columns, let's briefly review the experimental design. Each mouse was trained with one of two image sets (`G` or `H`). For the majority of mice, we recorded two sessions: one with the trained 'familiar' image set and one with a 'novel' image set. Note that two of the eight images were shared across these two image sets as diagrammed below for an example mouse. For this mouse, image set `G` (images on blue and purple backgrounds) was used in training and was therefore 'familiar', while image set `H` (the two holdover images on purple background plus six novel images on red background) was 'novel'. " + ] + }, + { + "cell_type": "markdown", + "id": "aaa41acf", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "68f9d7b7", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "So, each recording session can be defined by a few parameters, including the `image_set` used (G or H), the `experience_level` of the mouse (indicating whether the mouse had seen the image set in previous training sessions) and the `session_number` (indicating whether it was the first or second recording day for the mouse). Let's look at how many sessions we have of each type:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5fe793fc", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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image_setGH
session_numberexperience_level
1Familiar38.010.0
NovelNaN3.0
2Familiar3.0NaN
Novel10.039.0
\n", + "
" + ], + "text/plain": [ + "image_set G H\n", + "session_number experience_level \n", + "1 Familiar 38.0 10.0\n", + " Novel NaN 3.0\n", + "2 Familiar 3.0 NaN\n", + " Novel 10.0 39.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sessions_by_imageset_experience_day = ecephys_sessions_table.pivot_table(index=['session_number', 'experience_level'], \n", + " columns=['image_set'], \n", + " values='behavior_session_id', aggfunc=len)\n", + "display(sessions_by_imageset_experience_day)" + ] + }, + { + "cell_type": "markdown", + "id": "f5ef42fc", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "From this table we can see that for most mice, image set `G` was used for training (and therefore `Familiar`) and was also shown during the first recording session (38 sessions in the left cell of the first row above). For these mice, image set `H` was `novel` and shown on the second recording day (right cell of last row). Then there were 3 mice for which image set `G` was used in training, but the novel image set `H` was shown on the first recording day and `G` on the second (rows 2 and 3). Finally, there were 10 mice trained on the `H` image set. All 10 of these saw `H` on their first recording day and `G` on their second (rows 1 and 4).\n", + "\n", + "Keep in mind that though we aimed to record for 2 sessions from each mouse, not every recording session passed quality control. Thus for some mice, only one of the two recording days are represented in the table. This explains the discrepancy between the first and last rows of the table above.\n", + "\n", + "Also note that the probes are retracted after every recording (these are **acute recordings**). Moreover, we move the probes around 100 microns between session 1 and session 2, so it's impossible to map neurons across the two recording days (and it's unlikely that we are recording from the same neurons). " + ] + }, + { + "cell_type": "markdown", + "id": "ab328eee", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "#### The transgenic line determines which neurons are labeled with ChR2 for identification by optotagging. \n", + "\n", + "We can also use the `ecephys_sessions` table to find the genotype of the mouse used for each recording session. Across the dataset, we used mice of three genotypes: \n", + "\n", + "- C57Bl6J (wt)\n", + "- Sst-IRES-Cre;Ai32 to optotag putative Sst-expressing interneurons\n", + "- Vip-IRES-Cre;Ai32 to optotag putative Vip-expressing interneurons\n", + "\n", + "Somatostatin-positive neurons (Sst) and Vasoactive Intestinal Polypeptide neurons (Vip) constitute [two of the three major cortical inhibitory cell classes](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556905/#!po=8.92857), and by crossing these lines to the Ai32 reporter line, we can identify these neurons by [optotagging](https://allensdk--2471.org.readthedocs.build/en/2471/_static/examples/nb/visual_behavior_neuropixels_quickstart.html#Optotagging). \n", + "\n", + "We also record from C57Bl6J `wt/wt` mice." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e7aa0b22", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the different transgenic lines included in this dataset are:\n", + "\n", + "['Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt'\n", + " 'Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt' 'wt/wt']\n" + ] + } + ], + "source": [ + "print('the different transgenic lines included in this dataset are:\\n')\n", + "print(np.sort(ecephys_sessions_table.genotype.unique()))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fcf49171", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sexFM
genotype
Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt2419
Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt418
wt/wt1523
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" + ], + "text/plain": [ + "sex F M\n", + "genotype \n", + "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 24 19\n", + "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 4 18\n", + "wt/wt 15 23" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Number of sessions per genotype/sex\n", + "sessions_by_genotype_sex = ecephys_sessions_table.pivot_table(values='session_number', index='genotype', \n", + " columns='sex', aggfunc=len)\n", + "display(sessions_by_genotype_sex.rename(columns={'session_number': 'session_count'}))" + ] + }, + { + "cell_type": "markdown", + "id": "4269a1a0", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now let's see how many **mice** we have for each genotype/sex. The column `mouse_id` gives us a unique identifier for each mouse in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "947173dc", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sexFM
genotype
Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt1211
Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt29
wt/wt812
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" + ], + "text/plain": [ + "sex F M\n", + "genotype \n", + "Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 12 11\n", + "Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 2 9\n", + "wt/wt 8 12" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Number of mice per genotype/sex\n", + "mice_by_genotype_sex = ecephys_sessions_table.pivot_table(values='mouse_id', index='genotype', \n", + " columns='sex', aggfunc=lambda x: len(np.unique(x)))\n", + "display(mice_by_genotype_sex)" + ] + }, + { + "cell_type": "markdown", + "id": "cebbbd02", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Lastly, there are two columns to flag potential abnormalities in brain tissue or electrical activity. Since there are many analyses which may not be affected by these issues, we've decided to go ahead and release this data. But by default, the ecephys_sessions_table will not return these sessions. You can get all of the sessions with this call: " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "08ff8130", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number sessions returned by default: 103\n", + "Number of sessions returned without filtering abnormalities: 153\n" + ] + } + ], + "source": [ + "ecephys_sessions_no_filter = cache.get_ecephys_session_table(filter_abnormalities=False)\n", + "print(f'Number sessions returned by default: {len(ecephys_sessions_table)}')\n", + "print(f'Number of sessions returned without filtering abnormalities: {len(ecephys_sessions_no_filter)}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "5c2d1ba6", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The `abnormal_histology` column indicates where for each mouse we noted possible bleeding or insertion damage. This will be a list of brain regions. \n", + "The `abnormal_activity` column indicates when during the session we noted possible epileptiform activity. This will be a list of times in seconds.\n", + "\n", + "Let's grab one of these 'abnormal' sessions to see what these columns look like:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "619f21b9", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "abnormal_histology ['Thalamus']\n", + "abnormal_activity [132]\n", + "Name: 1052530003, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# grab a session that was flagged for both tissue damage and epileptiform activity\n", + "ecephys_sessions_no_filter[['abnormal_histology', 'abnormal_activity']]\\\n", + " [~ecephys_sessions_no_filter['abnormal_histology'].isnull() & \n", + " ~ecephys_sessions_no_filter['abnormal_activity'].isnull()].iloc[0]" + ] + }, + { + "cell_type": "markdown", + "id": "af76ead9", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "For this example session, it looks like we annotated potential damage in the Thalamus, and irregular firing activity 132 seconds into the session. For more details about how these abnormalities are flagged, check out the technical white paper." + ] + }, + { + "cell_type": "markdown", + "id": "6d598799", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Behavior Sessions Table" + ] + }, + { + "cell_type": "markdown", + "id": "3a758357", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In this dataset, mice are trained on a visual change detection task. This task involves a continuous stream of stimuli, and mice learn to lick in response to a change in the stimulus identity to earn a water reward. There are different stages of training in this task, described below. The metadata for each behavior session in the dataset can be found in the `behavior_sessions_table` and can be used to build a training history for each mouse." + ] + }, + { + "cell_type": "markdown", + "id": "272ec051", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Load the `behavior_sessions_table` from the cache" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6e09370b", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of behavior sessions: 3424\n" + ] + }, + { + "data": { + "text/html": [ + "
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equipment_namegenotypemouse_idsexage_in_dayssession_numberprior_exposures_to_session_typeprior_exposures_to_image_setprior_exposures_to_omissionsecephys_session_iddate_of_acquisitionsession_typeimage_set
behavior_session_id
1051333618BEH.G-Box2Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt540536M8510NaN0.0NaN2020-09-18 10:02:30.869000TRAINING_0_gratings_autorewards_15min_0uL_rewardNaN
1052301754BEH.G-Box2Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt540536M9042NaN0.0NaN2020-09-23 09:43:25.595000TRAINING_1_gratings_10uL_rewardNaN
1052374521NP.1Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt530862M14844032.00.01.052342e+092020-09-23 15:34:18.179000EPHYS_1_images_G_3uL_rewardG
1051860415BEH.G-Box4wt/wt533539F127903.00.0NaN2020-09-21 09:57:23.650000TRAINING_4_images_G_training_7uL_rewardG
1052132182BEH.F-Box5Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt536480M112811.00.0NaN2020-09-22 12:04:46.304000TRAINING_3_images_G_10uL_rewardG
\n", + "
" + ], + "text/plain": [ + " equipment_name \\\n", + "behavior_session_id \n", + "1051333618 BEH.G-Box2 \n", + "1052301754 BEH.G-Box2 \n", + "1052374521 NP.1 \n", + "1051860415 BEH.G-Box4 \n", + "1052132182 BEH.F-Box5 \n", + "\n", + " genotype mouse_id \\\n", + "behavior_session_id \n", + "1051333618 Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 540536 \n", + "1052301754 Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 540536 \n", + "1052374521 Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 530862 \n", + "1051860415 wt/wt 533539 \n", + "1052132182 Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt 536480 \n", + "\n", + " sex age_in_days session_number \\\n", + "behavior_session_id \n", + "1051333618 M 85 1 \n", + "1052301754 M 90 4 \n", + "1052374521 M 148 44 \n", + "1051860415 F 127 9 \n", + "1052132182 M 112 8 \n", + "\n", + " prior_exposures_to_session_type \\\n", + "behavior_session_id \n", + "1051333618 0 \n", + "1052301754 2 \n", + "1052374521 0 \n", + "1051860415 0 \n", + "1052132182 1 \n", + "\n", + " prior_exposures_to_image_set \\\n", + "behavior_session_id \n", + "1051333618 NaN \n", + "1052301754 NaN \n", + "1052374521 32.0 \n", + "1051860415 3.0 \n", + "1052132182 1.0 \n", + "\n", + " prior_exposures_to_omissions ecephys_session_id \\\n", + "behavior_session_id \n", + "1051333618 0.0 NaN \n", + "1052301754 0.0 NaN \n", + "1052374521 0.0 1.052342e+09 \n", + "1051860415 0.0 NaN \n", + "1052132182 0.0 NaN \n", + "\n", + " date_of_acquisition \\\n", + "behavior_session_id \n", + "1051333618 2020-09-18 10:02:30.869000 \n", + "1052301754 2020-09-23 09:43:25.595000 \n", + "1052374521 2020-09-23 15:34:18.179000 \n", + "1051860415 2020-09-21 09:57:23.650000 \n", + "1052132182 2020-09-22 12:04:46.304000 \n", + "\n", + " session_type \\\n", + "behavior_session_id \n", + "1051333618 TRAINING_0_gratings_autorewards_15min_0uL_reward \n", + "1052301754 TRAINING_1_gratings_10uL_reward \n", + "1052374521 EPHYS_1_images_G_3uL_reward \n", + "1051860415 TRAINING_4_images_G_training_7uL_reward \n", + "1052132182 TRAINING_3_images_G_10uL_reward \n", + "\n", + " image_set \n", + "behavior_session_id \n", + "1051333618 NaN \n", + "1052301754 NaN \n", + "1052374521 G \n", + "1051860415 G \n", + "1052132182 G " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "behavior_sessions = cache.get_behavior_session_table()\n", + "\n", + "print(f\"Total number of behavior sessions: {len(behavior_sessions)}\")\n", + "\n", + "behavior_sessions.head()" + ] + }, + { + "cell_type": "markdown", + "id": "60400416", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### What columns does the behavior_session table have and what values can they take?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c658c9d5", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['equipment_name', 'genotype', 'mouse_id', 'sex', 'age_in_days',\n", + " 'session_number', 'prior_exposures_to_session_type',\n", + " 'prior_exposures_to_image_set', 'prior_exposures_to_omissions',\n", + " 'ecephys_session_id', 'date_of_acquisition', 'session_type',\n", + " 'image_set'],\n", + " dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "behavior_sessions.columns" + ] + }, + { + "cell_type": "markdown", + "id": "2046f8d8", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Behavior sessions can take place on different experimental systems" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "561c286b", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "behavior data could be recorded on these experimental systems:\n", + "\n", + "['BEH.B-Box1' 'BEH.B-Box2' 'BEH.B-Box3' 'BEH.B-Box4' 'BEH.B-Box5'\n", + " 'BEH.B-Box6' 'BEH.D-Box1' 'BEH.D-Box2' 'BEH.D-Box3' 'BEH.D-Box4'\n", + " 'BEH.D-Box5' 'BEH.D-Box6' 'BEH.F-Box1' 'BEH.F-Box2' 'BEH.F-Box3'\n", + " 'BEH.F-Box4' 'BEH.F-Box5' 'BEH.F-Box6' 'BEH.G-Box1' 'BEH.G-Box2'\n", + " 'BEH.G-Box3' 'BEH.G-Box4' 'BEH.G-Box5' 'BEH.G-Box6' 'NP.0' 'NP.1']\n" + ] + } + ], + "source": [ + "print('behavior data could be recorded on these experimental systems:\\n')\n", + "print(np.sort(behavior_sessions.equipment_name.unique()))" + ] + }, + { + "cell_type": "markdown", + "id": "479bbbc3", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "`equipment_name` values starting with 'BEH' indicate behavioral training in the behavior facility, while values starting with 'NP' indicate behavior sessions that took place on an experimental Neuropixels rig." + ] + }, + { + "cell_type": "markdown", + "id": "6ee0b810", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The `mouse_id` is a 6-digit unique identifier for each experimental animal in the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3f965810", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "there are 81 mice in the dataset\n" + ] + } + ], + "source": [ + "print('there are', len(behavior_sessions.mouse_id.unique()), 'mice in the dataset')" + ] + }, + { + "cell_type": "markdown", + "id": "ef5d3676", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Other mouse specific metadata includes `sex`, `age_in_days` and `genotype`." + ] + }, + { + "cell_type": "markdown", + "id": "635c4a2c", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Session Type - a very important piece of information" + ] + }, + { + "cell_type": "markdown", + "id": "192b7566", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The `session_type` for each behavior session indicates the behavioral training stage or Neuropixels experiment conditions for that particular session. This determines what stimuli were shown and what task parameters were used. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "88bffb4b", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the session_types available in this dataset are:\n", + "\n", + "['EPHYS_1_images_G_3uL_reward' 'EPHYS_1_images_G_5uL_reward'\n", + " 'EPHYS_1_images_H_3uL_reward' 'EPHYS_1_images_H_5uL_reward'\n", + " 'HABITUATION_5_images_G_handoff_ready_3uL_reward'\n", + " 'HABITUATION_5_images_G_handoff_ready_5uL_reward'\n", + " 'HABITUATION_5_images_H_handoff_ready_3uL_reward'\n", + " 'HABITUATION_5_images_H_handoff_ready_5uL_reward'\n", + " 'TRAINING_0_gratings_autorewards_15min'\n", + " 'TRAINING_0_gratings_autorewards_15min_0uL_reward' 'TRAINING_1_gratings'\n", + " 'TRAINING_1_gratings_10uL_reward' 'TRAINING_2_gratings_flashed'\n", + " 'TRAINING_2_gratings_flashed_10uL_reward'\n", + " 'TRAINING_3_images_G_10uL_reward' 'TRAINING_3_images_H_10uL_reward'\n", + " 'TRAINING_4_images_G_training' 'TRAINING_4_images_G_training_7uL_reward'\n", + " 'TRAINING_4_images_H_training_7uL_reward' 'TRAINING_5_images_G_epilogue'\n", + " 'TRAINING_5_images_G_epilogue_5uL_reward'\n", + " 'TRAINING_5_images_G_handoff_lapsed_5uL_reward'\n", + " 'TRAINING_5_images_G_handoff_ready_5uL_reward'\n", + " 'TRAINING_5_images_H_epilogue_5uL_reward'\n", + " 'TRAINING_5_images_H_handoff_lapsed_5uL_reward'\n", + " 'TRAINING_5_images_H_handoff_ready_5uL_reward']\n" + ] + } + ], + "source": [ + "print('the session_types available in this dataset are:\\n')\n", + "print(np.sort(behavior_sessions.session_type.unique()))" + ] + }, + { + "cell_type": "markdown", + "id": "18214de4", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "You can see that each session type is prepended with an indicator of when this session was run in the training sequence (for example `TRAINING_0` or `TRAINING_1`). Mice progress through a series of training stages to shape their behavior prior to recording. Mice are automatically advanced between stages depending on their behavioral performance. For a detailed description of the change detection task and advancement criteria, please see the technical whitepaper." + ] + }, + { + "cell_type": "markdown", + "id": "9b04f9ff", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "55b589e5", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Training with the change detection task begins with simple static grating stimuli, changing between 0 and 90 degrees in orientation. On the very first day, mice are automatically given a water reward when the orientation of the stimulus changes (`TRAINING_0_gratings_autorewards_15min`). On subsequent days, mice must lick following the change in order to receive a water reward (`TRAINING_1_gratings`). In the next stage, stimuli are flashed, with a 500 ms inter stimulus interval of mean luminance gray screen (`TRAINING_2_gratings_flashed`). " + ] + }, + { + "cell_type": "markdown", + "id": "45d3fefd", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Once mice perform the task well with gratings, they are transitioned to natural image stimuli. Different groups of mice are trained with different sets of images, image set `G` or `H` (described above). In the following description, we use `X` as a placeholder for `G` or `H` in the `session_type` name. Training with images begins with a 10ul water reward volume (`TRAINING_3...`), which is then decreased to 7ul once mice perform the task consistently with images (`TRAINING_4...`). If mice continue to perform well, they are advanced to `TRAINING_5_images_X_epilogue_5uL_reward`, during which they are exposed to the receptive field mapping stimulus that will be used during Neuropixels recordings and the reward is further reduced to 5 ul. When mice have reached criterion to be transferred to the Neuropixels portion of the experiment, they are labeled as 'handoff_ready' (`TRAINING_5_images_X_handoff_ready_5uL_reward`.) If behavior performance returns to below criterion level before they are handed off, they are labeled as 'handoff_lapsed'(`TRAINING_5_images_X_handoff_lapsed_5uL_reward`). You may notice inconsistencies with the suffix for a few of these stage names, this reflects a minor change we made early on during data collection to reduce the reward volume from 7ul for `TRAINING_5` to 5ul. After that, we added the volume explicitly to the stage name. \n", + "\n", + "So now, let's look at the training history for 1 mouse to see how this unfolds:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4b3db0a5", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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session_typeequipment_namedate_of_acquisitionprior_exposures_to_image_setprior_exposures_to_omissions
behavior_session_id
1079461839TRAINING_0_gratings_autorewards_15min_0uL_rewardBEH.G-Box52021-01-29 11:20:57.848000NaN0.0
1080309252TRAINING_1_gratings_10uL_rewardBEH.G-Box52021-02-01 10:30:41.010000NaN0.0
1080567296TRAINING_1_gratings_10uL_rewardBEH.G-Box52021-02-02 10:41:01.736000NaN0.0
1081055727TRAINING_1_gratings_10uL_rewardBEH.G-Box52021-02-03 10:08:22.073000NaN0.0
1081407988TRAINING_1_gratings_10uL_rewardBEH.G-Box52021-02-04 10:57:59.323000NaN0.0
1081665901TRAINING_2_gratings_flashed_10uL_rewardBEH.G-Box52021-02-05 10:17:01.333000NaN0.0
1082287921TRAINING_3_images_G_10uL_rewardBEH.G-Box52021-02-08 10:26:15.2600000.00.0
1082721365TRAINING_3_images_G_10uL_rewardBEH.G-Box52021-02-09 10:11:40.9920001.00.0
1082978971TRAINING_3_images_G_10uL_rewardBEH.G-Box52021-02-10 10:36:09.1690002.00.0
1083179250TRAINING_4_images_G_training_7uL_rewardBEH.G-Box52021-02-11 09:55:53.7640003.00.0
1083988326TRAINING_5_images_G_epilogue_5uL_rewardBEH.G-Box52021-02-15 11:08:10.8710004.00.0
1084214262TRAINING_5_images_G_epilogue_5uL_rewardBEH.G-Box52021-02-16 10:40:55.9390005.00.0
1084416013TRAINING_5_images_G_epilogue_5uL_rewardBEH.G-Box52021-02-17 10:05:26.6480006.00.0
1084925549TRAINING_5_images_G_handoff_ready_5uL_rewardBEH.G-Box52021-02-18 10:30:15.6690007.00.0
1085100426TRAINING_5_images_G_handoff_ready_5uL_rewardBEH.G-Box52021-02-19 10:22:15.6450008.00.0
1085697640HABITUATION_5_images_G_handoff_ready_5uL_rewardNP.02021-02-22 10:35:13.3670009.00.0
1085945525HABITUATION_5_images_G_handoff_ready_5uL_rewardNP.02021-02-23 09:55:39.73800010.00.0
1086167535HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-02-24 10:03:34.43900011.00.0
1086376321HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-02-25 09:28:17.67700012.00.0
1086799944HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-02-26 10:16:30.47900013.00.0
1087320527HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-01 10:01:49.98400014.00.0
1087522745HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-02 11:24:41.07300015.00.0
1087696440HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-03 09:40:18.91300016.00.0
1087922676HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-04 09:47:42.84000017.00.0
1088237976HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-05 10:26:29.59400018.00.0
1088815471HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-08 12:57:46.52300019.00.0
1089049814HABITUATION_5_images_G_handoff_ready_3uL_rewardNP.02021-03-09 12:56:02.89900020.00.0
1089343256EPHYS_1_images_G_3uL_rewardNP.02021-03-10 14:24:06.84100021.00.0
1089636567EPHYS_1_images_H_3uL_rewardNP.02021-03-11 15:14:20.6190000.01.0
\n", + "
" + ], + "text/plain": [ + " session_type \\\n", + "behavior_session_id \n", + "1079461839 TRAINING_0_gratings_autorewards_15min_0uL_reward \n", + "1080309252 TRAINING_1_gratings_10uL_reward \n", + "1080567296 TRAINING_1_gratings_10uL_reward \n", + "1081055727 TRAINING_1_gratings_10uL_reward \n", + "1081407988 TRAINING_1_gratings_10uL_reward \n", + "1081665901 TRAINING_2_gratings_flashed_10uL_reward \n", + "1082287921 TRAINING_3_images_G_10uL_reward \n", + "1082721365 TRAINING_3_images_G_10uL_reward \n", + "1082978971 TRAINING_3_images_G_10uL_reward \n", + "1083179250 TRAINING_4_images_G_training_7uL_reward \n", + "1083988326 TRAINING_5_images_G_epilogue_5uL_reward \n", + "1084214262 TRAINING_5_images_G_epilogue_5uL_reward \n", + "1084416013 TRAINING_5_images_G_epilogue_5uL_reward \n", + "1084925549 TRAINING_5_images_G_handoff_ready_5uL_reward \n", + "1085100426 TRAINING_5_images_G_handoff_ready_5uL_reward \n", + "1085697640 HABITUATION_5_images_G_handoff_ready_5uL_reward \n", + "1085945525 HABITUATION_5_images_G_handoff_ready_5uL_reward \n", + "1086167535 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1086376321 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1086799944 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1087320527 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1087522745 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1087696440 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1087922676 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1088237976 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1088815471 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1089049814 HABITUATION_5_images_G_handoff_ready_3uL_reward \n", + "1089343256 EPHYS_1_images_G_3uL_reward \n", + "1089636567 EPHYS_1_images_H_3uL_reward \n", + "\n", + " equipment_name date_of_acquisition \\\n", + "behavior_session_id \n", + "1079461839 BEH.G-Box5 2021-01-29 11:20:57.848000 \n", + "1080309252 BEH.G-Box5 2021-02-01 10:30:41.010000 \n", + "1080567296 BEH.G-Box5 2021-02-02 10:41:01.736000 \n", + "1081055727 BEH.G-Box5 2021-02-03 10:08:22.073000 \n", + "1081407988 BEH.G-Box5 2021-02-04 10:57:59.323000 \n", + "1081665901 BEH.G-Box5 2021-02-05 10:17:01.333000 \n", + "1082287921 BEH.G-Box5 2021-02-08 10:26:15.260000 \n", + "1082721365 BEH.G-Box5 2021-02-09 10:11:40.992000 \n", + "1082978971 BEH.G-Box5 2021-02-10 10:36:09.169000 \n", + "1083179250 BEH.G-Box5 2021-02-11 09:55:53.764000 \n", + "1083988326 BEH.G-Box5 2021-02-15 11:08:10.871000 \n", + "1084214262 BEH.G-Box5 2021-02-16 10:40:55.939000 \n", + "1084416013 BEH.G-Box5 2021-02-17 10:05:26.648000 \n", + "1084925549 BEH.G-Box5 2021-02-18 10:30:15.669000 \n", + "1085100426 BEH.G-Box5 2021-02-19 10:22:15.645000 \n", + "1085697640 NP.0 2021-02-22 10:35:13.367000 \n", + "1085945525 NP.0 2021-02-23 09:55:39.738000 \n", + "1086167535 NP.0 2021-02-24 10:03:34.439000 \n", + "1086376321 NP.0 2021-02-25 09:28:17.677000 \n", + "1086799944 NP.0 2021-02-26 10:16:30.479000 \n", + "1087320527 NP.0 2021-03-01 10:01:49.984000 \n", + "1087522745 NP.0 2021-03-02 11:24:41.073000 \n", + "1087696440 NP.0 2021-03-03 09:40:18.913000 \n", + "1087922676 NP.0 2021-03-04 09:47:42.840000 \n", + "1088237976 NP.0 2021-03-05 10:26:29.594000 \n", + "1088815471 NP.0 2021-03-08 12:57:46.523000 \n", + "1089049814 NP.0 2021-03-09 12:56:02.899000 \n", + "1089343256 NP.0 2021-03-10 14:24:06.841000 \n", + "1089636567 NP.0 2021-03-11 15:14:20.619000 \n", + "\n", + " prior_exposures_to_image_set \\\n", + "behavior_session_id \n", + "1079461839 NaN \n", + "1080309252 NaN \n", + "1080567296 NaN \n", + "1081055727 NaN \n", + "1081407988 NaN \n", + "1081665901 NaN \n", + "1082287921 0.0 \n", + "1082721365 1.0 \n", + "1082978971 2.0 \n", + "1083179250 3.0 \n", + "1083988326 4.0 \n", + "1084214262 5.0 \n", + "1084416013 6.0 \n", + "1084925549 7.0 \n", + "1085100426 8.0 \n", + "1085697640 9.0 \n", + "1085945525 10.0 \n", + "1086167535 11.0 \n", + "1086376321 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"execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "training_history = behavior_sessions[behavior_sessions['mouse_id']==556016]\n", + "training_history = training_history.sort_values(by='date_of_acquisition')\n", + "training_history[['session_type', 'equipment_name', 'date_of_acquisition', 'prior_exposures_to_image_set', 'prior_exposures_to_omissions']]" + ] + }, + { + "cell_type": "markdown", + "id": "09b425b3", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "This table shows that mouse 556016 was trained for 29 days, starting with non-contingent rewards for changes in grating orientation during `TRAINING_0`, and ending with two Neuropixels recording sessions running the change detection task with flashing images (the `EPHYS` stages). All sessions before the `HABITUATION` stage were run in behavior boxes. From `HABITUATION` on, sessions were run on the experimental Neuropixels rig `NP.0`.\n", + "\n", + "The `prior_exposures_to_image_set` column indicates how many times the mouse had seen the image set used in a particular session. For example, by the time the mouse above reached its first recording day (`EPHYS_1_images_G_3uL_reward`), it had already seen the `G` image set in 21 previous sessions. On the second recording day, it was exposed to the `H` image set for the first time.\n", + "\n", + "The `EPHYS` sessions run during Neuropixels recordings are the first time the mouse encounters omitted stimuli. During these sessions, we omit a little under 5% of the stimulus flashes to investigate temporal expectation signals. The `prior_exposures_to_omissions` column indicates whether the mouse has encountered omissions in a previous recording session. Note that it is '0' for all but the second recording day." + ] + }, + { + "cell_type": "markdown", + "id": "6095bedb", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Units, Probes and Channels Tables" + ] + }, + { + "cell_type": "markdown", + "id": "f04b8154", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now let's look at the units, probes and channels tables in a bit more detail. We'll start with the units table, which contains info about every unit recorded in this dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3e3adfe6", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This dataset contains 319013 total units\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ecephys_channel_id ecephys_probe_id ecephys_session_id \\\n", + "unit_id \n", + "1157005856 1157001834 1046469925 1046166369 \n", + "1157005853 1157001834 1046469925 1046166369 \n", + "1157005720 1157001786 1046469925 1046166369 \n", + "1157006074 1157001929 1046469925 1046166369 \n", + "1157006072 1157001929 1046469925 1046166369 \n", + "\n", + " amplitude_cutoff anterior_posterior_ccf_coordinate \\\n", + "unit_id \n", + "1157005856 0.500000 8453.0 \n", + "1157005853 0.323927 8453.0 \n", + "1157005720 0.044133 8575.0 \n", + "1157006074 0.000583 8212.0 \n", + "1157006072 0.500000 8212.0 \n", + "\n", + " dorsal_ventral_ccf_coordinate left_right_ccf_coordinate \\\n", + "unit_id \n", + "1157005856 3353.0 6719.0 \n", + "1157005853 3353.0 6719.0 \n", + "1157005720 3842.0 6590.0 \n", + "1157006074 2477.0 6992.0 \n", + "1157006072 2477.0 6992.0 \n", + "\n", + " cumulative_drift d_prime structure_acronym ... valid_data \\\n", + "unit_id ... \n", + "1157005856 140.32 6.088133 MB ... True \n", + "1157005853 239.76 4.635583 MB ... True \n", + "1157005720 263.32 5.691955 MRN ... True \n", + "1157006074 154.64 6.049284 NOT ... True \n", + "1157006072 242.58 4.745499 NOT ... True \n", + "\n", + " amplitude waveform_duration waveform_halfwidth PT_ratio \\\n", + "unit_id \n", + "1157005856 286.132665 0.151089 0.096147 0.310791 \n", + "1157005853 181.418835 0.357119 0.192295 0.531490 \n", + "1157005720 180.866205 0.521943 0.178559 0.612217 \n", + "1157006074 574.984215 0.343384 0.192295 0.470194 \n", + "1157006072 315.794115 0.329648 0.164824 0.488276 \n", + "\n", + " recovery_slope repolarization_slope spread velocity_above \\\n", + "unit_id \n", + "1157005856 -0.227726 0.961313 20.0 -0.457845 \n", + "1157005853 -0.150522 0.732741 30.0 2.060302 \n", + "1157005720 -0.024239 0.539687 80.0 0.000000 \n", + "1157006074 -0.356670 2.258649 40.0 1.373534 \n", + "1157006072 -0.210010 1.320270 70.0 0.412060 \n", + "\n", + " velocity_below \n", + "unit_id \n", + "1157005856 NaN \n", + "1157005853 -2.060302 \n", + "1157005720 0.863364 \n", + "1157006074 0.000000 \n", + "1157006072 0.343384 \n", + "\n", + "[5 rows x 34 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "units = cache.get_unit_table()\n", + "print(f'This dataset contains {len(units)} total units')\n", + "\n", + "units.head()" + ] + }, + { + "cell_type": "markdown", + "id": "28976005", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "For more information about many of the metrics included in this table and how to use them to guide your analysis, see our [quality metrics tutorial](https://allensdk--2471.org.readthedocs.build/en/2471/_static/examples/nb/visual_behavior_neuropixels_quality_metrics.html). For now, here's a brief description of each column:\n", + "\n", + "\n", + "**General Metadata** \n", + "\n", + "`ecephys_channel_id`: unique ID of channel on which unit's peak waveform occurred \n", + "`ecephys_probe_id`: unique ID for probe on which unit was recorded \n", + "`ecephys_session_id`: unique ID for session during which unit was recorded \n", + "`anterior_posterior_ccf_coordinate`: CCF coord in the AP axis \n", + "`dorsal_ventral_ccf_coordinate`: CCF coord in the DV axis \n", + "`left_right_ccf_coordinate`: CCF coord in the left/right axis \n", + "`structure_acronym`: CCF acronym for area to which unit was assigned \n", + "`structure_id`: CCF structure ID for the area to which unit was assigned \n", + "`probe_horizontal_position`: Horizontal (perpindicular to shank) probe position of each unit's peak channel in microns \n", + "`probe_vertical_position`: Vertical (along shank) probe position of each unit's peak channel in microns\n", + "\n", + "\n", + "**Waveform metrics**: Look [here](https://github.com/AllenInstitute/ecephys_spike_sorting/tree/master/ecephys_spike_sorting/modules/mean_waveforms) for more detail on these metrics and the code that computes them. For the below descriptions the '1D waveform' is defined as the waveform on the peak channel. The '2D waveform' is the waveform across channels centered on the peak channel.\n", + "\n", + "`amplitude`: Peak to trough amplitude for mean 1D waveform in microvolts \n", + "`waveform_duration`: Time from trough to peak for 1D waveform in milliseconds \n", + "`waveform_halfwidth`: Width of 1D waveform at half-amplitude in milliseconds \n", + "`PT_ratio`: Ratio of the max (peak) to the min (trough) amplitudes for 1D waveform \n", + "`recovery_slope`: Slope of recovery of 1D waveform to baseline after repolarization (coming down from peak) \n", + "`repolarization_slope`: Slope of repolarization of 1D waveform to baseline after trough \n", + "`spread`: Range of channels for which the spike amplitude was above 12% of the peak channel amplitude \n", + "`velocity_above`: Slope of spike propagation velocity traveling in dorsal direction from soma (note to avoid infinite values, this is actaully the inverse of velocity: ms/mm) \n", + "`velocity_below`: Slope of spike propagation velocity traveling in ventral direction from soma (note to avoid infinite values, this is actually the inverse of velocity: ms/mm) \n", + "`snr`: signal-to-noise ratio for 1D waveform \n", + "\n", + "\n", + "**Quality metrics**: Look [here](https://github.com/AllenInstitute/ecephys_spike_sorting/tree/7e567a6fc3fd2fc0eedef750b83b8b8a0d469544/ecephys_spike_sorting/modules/quality_metrics) for more detail on these metrics and the code that computes them.\n", + "\n", + "`amplitude_cutoff`: estimate of miss rate based on amplitude histogram (ie fraction of spikes estimated to have been below detection threshold) \n", + "`cumulative_drift`: cumulative change in spike depth along probe throughout the recording \n", + "`d_prime`: classification accuracy based on LDA \n", + "`firing_rate`: Mean firing rate over entire recording \n", + "`isi_violations`: Ratio of refractory violation rate to total spike rate \n", + "`isolation_distance`: Distance to nearest cluster in Mahalanobis space \n", + "`l_ratio`: The Mahalanobis distance and chi-squared inverse cdf are used to find the probability of cluster membership for each spike. \n", + "`max_drift`: Maximum change in unit depth across recording \n", + "`nn_hit_rate`: Fraction of nearest neighbors in PCA space for spikes in unit cluster that are also in unit cluster \n", + "`nn_miss_rate`: Fraction of nearest neighbors for spikes outside unit cluster than are in unit cluster \n", + "`presence_ratio`: Fraction of time during session for which a unit was spiking \n", + "`silhouette_score`: Standard metric for cluster overlap, computed in PCA space \n", + "`quality`: Label assigned based on waveform shape as described [here](https://github.com/AllenInstitute/ecephys_spike_sorting/tree/7e567a6fc3fd2fc0eedef750b83b8b8a0d469544/ecephys_spike_sorting/modules/noise_templates). Either 'good' for physiological waveforms or 'noise' for artifactual waveforms.\n", + "\n", + "\n", + "\n", + "Note that each unit can be traced to an experiment (`ecephys_session_id`), probe (`ecephys_probe_id`) and channel (`ecephys_channel_id`). Let's filter this table to see all of the units recorded for one ecephys_session from our ecephys_sessions_table:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "626e581a", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 34 columns

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" + ], + "text/plain": [ + " ecephys_channel_id ecephys_probe_id ecephys_session_id \\\n", + "unit_id \n", + "1055691078 1055688702 1051284115 1051155866 \n", + "1055691190 1055688884 1051284115 1051155866 \n", + "1055691057 1055688685 1051284115 1051155866 \n", + "1055691088 1055688710 1051284115 1051155866 \n", + "1055691262 1055688926 1051284115 1051155866 \n", + "\n", + " amplitude_cutoff anterior_posterior_ccf_coordinate \\\n", + "unit_id \n", + "1055691078 0.014377 8116.0 \n", + "1055691190 0.099036 8608.0 \n", + "1055691057 0.192007 8077.0 \n", + "1055691088 0.500000 8137.0 \n", + "1055691262 0.207075 8778.0 \n", + "\n", + " dorsal_ventral_ccf_coordinate left_right_ccf_coordinate \\\n", + "unit_id \n", + "1055691078 4256.0 8158.0 \n", + "1055691190 2590.0 9042.0 \n", + "1055691057 4364.0 8090.0 \n", + "1055691088 4196.0 8191.0 \n", + "1055691262 2177.0 9318.0 \n", + "\n", + " cumulative_drift d_prime structure_acronym ... valid_data \\\n", + "unit_id ... \n", + "1055691078 0.00 3.591360 TH ... True \n", + "1055691190 439.38 3.359853 CA1 ... True \n", + "1055691057 152.40 2.785917 ZI ... True \n", + "1055691088 285.13 4.937366 LGv ... True \n", + "1055691262 247.35 3.570518 VISl ... True \n", + "\n", + " amplitude waveform_duration waveform_halfwidth PT_ratio \\\n", + "unit_id \n", + "1055691078 147.680000 1.593300 0.700503 1.166523 \n", + "1055691190 198.552705 0.590620 0.315913 0.376350 \n", + "1055691057 205.294635 0.247236 0.164824 0.873837 \n", + "1055691088 175.207695 0.288442 0.151089 0.374370 \n", + "1055691262 161.840055 0.398325 0.151089 0.466350 \n", + "\n", + " recovery_slope repolarization_slope spread velocity_above \\\n", + "unit_id \n", + "1055691078 -0.076320 0.230221 170.0 2.506700 \n", + "1055691190 -0.020334 0.382136 120.0 -1.053043 \n", + "1055691057 -0.225462 0.927786 70.0 0.343384 \n", + "1055691088 -0.071037 0.480095 70.0 -0.137353 \n", + "1055691262 -0.016731 0.422141 50.0 0.686767 \n", + "\n", + " velocity_below \n", + "unit_id \n", + "1055691078 -1.327750 \n", + "1055691190 -1.579564 \n", + "1055691057 -0.824121 \n", + "1055691088 -0.755444 \n", + "1055691262 0.000000 \n", + "\n", + "[5 rows x 34 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#grab the ecephys session id for one experiment; these session ids are the indices of the ecephys_sessions_table\n", + "session_id = ecephys_sessions_table.index.values[1]\n", + "session_units = units[units['ecephys_session_id']==session_id]\n", + "session_units.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "587c3318", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We recorded from 6 probes this session\n" + ] + } + ], + "source": [ + "# Looks like we inserted all 6 probes during this experiment\n", + "session_probes_from_units_table = np.sort(session_units.ecephys_probe_id.unique())\n", + "print(f'We recorded from {len(session_probes_from_units_table)} probes this session')" + ] + }, + { + "cell_type": "markdown", + "id": "62301ba4", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Let's grab the probes table and check that when we filter by this ecephys session id, we get the same probes as above:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bf06bf01", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probes = cache.get_probe_table()\n", + "session_probes = probes[probes.ecephys_session_id==session_id].index.values\n", + "np.all(session_probes_from_units_table==session_probes)" + ] + }, + { + "cell_type": "markdown", + "id": "b80ff1ce", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The probes table has a bit more metadata about the probe type (Neuropixels 1.0), the areas that each probe passed through, and the unit count and sampling rates:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7738431e", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecephys_session_idnamesampling_ratelfp_sampling_ratephasehas_lfp_dataunit_countchannel_countstructure_acronyms
ecephys_probe_id
10445069331044385384probeB30000.1784022500.0148671.0True701384['CA1', 'DG', 'LP', 'POL', 'PoT', 'VISpm', 'ro...
10445069341044385384probeC30000.0498522500.0041541.0True307384['MB', 'MRN', 'POST', 'SCig', 'VISp', 'root']
10445069351044385384probeD30000.0291152500.0024261.0True521384['CA1', 'CA3', 'DG', 'LGv', 'MB', 'TH', 'VISl'...
10445069361044385384probeE30000.0758512500.0063211.0True282384['CA1', 'DG', 'MB', 'MGd', 'MGm', 'MRN', 'SGN'...
10445069371044385384probeF29999.9595782499.9966311.0True368384['CA1', 'DG', 'LP', 'MRN', 'POL', 'PoT', 'SGN'...
\n", + "
" + ], + "text/plain": [ + " ecephys_session_id name sampling_rate \\\n", + "ecephys_probe_id \n", + "1044506933 1044385384 probeB 30000.178402 \n", + "1044506934 1044385384 probeC 30000.049852 \n", + "1044506935 1044385384 probeD 30000.029115 \n", + "1044506936 1044385384 probeE 30000.075851 \n", + "1044506937 1044385384 probeF 29999.959578 \n", + "\n", + " lfp_sampling_rate phase has_lfp_data unit_count \\\n", + "ecephys_probe_id \n", + "1044506933 2500.014867 1.0 True 701 \n", + "1044506934 2500.004154 1.0 True 307 \n", + "1044506935 2500.002426 1.0 True 521 \n", + "1044506936 2500.006321 1.0 True 282 \n", + "1044506937 2499.996631 1.0 True 368 \n", + "\n", + " channel_count \\\n", + "ecephys_probe_id \n", + "1044506933 384 \n", + "1044506934 384 \n", + "1044506935 384 \n", + "1044506936 384 \n", + "1044506937 384 \n", + "\n", + " structure_acronyms \n", + "ecephys_probe_id \n", + "1044506933 ['CA1', 'DG', 'LP', 'POL', 'PoT', 'VISpm', 'ro... \n", + "1044506934 ['MB', 'MRN', 'POST', 'SCig', 'VISp', 'root'] \n", + "1044506935 ['CA1', 'CA3', 'DG', 'LGv', 'MB', 'TH', 'VISl'... \n", + "1044506936 ['CA1', 'DG', 'MB', 'MGd', 'MGm', 'MRN', 'SGN'... \n", + "1044506937 ['CA1', 'DG', 'LP', 'MRN', 'POL', 'PoT', 'SGN'... " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probes.head()" + ] + }, + { + "cell_type": "markdown", + "id": "1407441d", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now let's get the channels table:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "85bab3db", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'cache' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [2]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m channels \u001B[38;5;241m=\u001B[39m \u001B[43mcache\u001B[49m\u001B[38;5;241m.\u001B[39mget_channel_table()\n\u001B[0;32m 2\u001B[0m channels\u001B[38;5;241m.\u001B[39mhead()\n", + "\u001B[1;31mNameError\u001B[0m: name 'cache' is not defined" + ] + } + ], + "source": [ + "channels = cache.get_channel_table()\n", + "channels.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cae076df", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'channels' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [1]\u001B[0m, in \u001B[0;36m\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mchannels\u001B[49m\u001B[38;5;241m.\u001B[39mprobe_channel_number\u001B[38;5;241m.\u001B[39mmax()\n", + "\u001B[1;31mNameError\u001B[0m: name 'channels' is not defined" + ] + } + ], + "source": [ + "channels.probe_channel_number.max()" + ] + }, + { + "cell_type": "markdown", + "id": "f18c393f", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can join the channels and units tables to get full CCF info about every unit." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7ba05a92", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "#first let's merge the units and channels tables\n", + "session_units_channels = session_units.merge(channels, left_on='ecephys_channel_id', right_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a605812c", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now let's use this info to plot each unit's CCF position grouped by probe for our example session:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2ccff2b8", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D \n", + "\n", + "fig = plt.figure()\n", + "fig.set_size_inches([14,8])\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "def plot_probe_coords(probe_group):\n", + " ax.scatter(probe_group['left_right_ccf_coordinate_x'],\n", + " probe_group['anterior_posterior_ccf_coordinate_x'],\n", + " -probe_group['dorsal_ventral_ccf_coordinate_x'], #reverse the z coord so that down is into the brain\n", + " )\n", + " return probe_group['ecephys_probe_id_x'].values[0]\n", + "\n", + "probe_ids = session_units_channels.groupby('ecephys_probe_id_x').apply(plot_probe_coords)\n", + "\n", + "ax.set_zlabel('D/V')\n", + "ax.set_xlabel('Left/Right')\n", + "ax.set_ylabel('A/P')\n", + "ax.legend(probe_ids)\n", + "ax.view_init(elev=55, azim=70)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb235c88", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "You can see that these probe trajectories wiggle a bit. That's because we're plotting them in CCF space. When we warp the brains into this space, the probe trajectories can bend." + ] + } + ], + "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": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "cb38a8e3bd5a58a98deb62ad06df3bd0c14518dbf2de9867be7d36b03e6e3aaa" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quality_metrics.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quality_metrics.ipynb old mode 100644 new mode 100755 index b409e86da..c3e61a3f5 --- a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quality_metrics.ipynb +++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quality_metrics.ipynb @@ -12,9 +12,9 @@ "\n", "To help you avoid these pitfalls, this tutorial will explore how these metrics are calculated, how they can be biased, and how they should be applied to specific use cases. It's important to keep in mind that none of these metrics are perfect, and that the use of unit quality metrics for filtering ephys data is still an evolving area of research. More work is required in order to establish general-purpose best practices and standards in this domain.\n", "\n", - "This tutorial assumes you've already created a data cache, or are working with the files on AWS. If you haven't reached that step yet, we recommend going through the [data access tutorial](./visual_behavior_neuropixels_data_access.ipynb) first.\n", + "This tutorial assumes you've already created a data cache, or are working with the files on AWS (Simple Storage Service (S3) bucket: [visual-behavior-neuropixels-data](https://s3.console.aws.amazon.com/s3/buckets/visual-behavior-neuropixels-data)). If you haven't reached that step yet, we recommend going through the [data access tutorial](./visual_behavior_neuropixels_data_access.ipynb) first.\n", "\n", - "Functions related to data analysis will be covered in other tutorials. For a full list of available tutorials, see the [SDK documentation] (****NEED LINK)." + "Functions related to data analysis will be covered in other tutorials. For a full list of available tutorials, see the [SDK documentation](https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html)." ] }, { @@ -37,7 +37,7 @@ "\n", "These mistakes can occur even in units that appear to be extremely well isolated. It's misleading to conceive of units as existing in two distinct categories, one with perfectly clean \"single units\" and one with impure \"multiunits.\" Instead, there's a gradient of qualities, with mostly complete, uncontaminated units at one end, and incomplete, highly contaminated units at the other.\n", "\n", - "Despite the fact that there's not a clear division between single-unit and multi-unit activity, we still have to make a binary decision in every analysis we carry out: should this unit be included or not? Ideally this decision should be based on objective metrics that will not bias the end results. By default, the AllenSDK uses three quality metrics, `isi_violations`, `amplitude_cutoff`, and `presence_ratio`, to filter out units that are likely to be highly contaminated or missing lots of spikes. However, the default values of these filters may not be appropriate for your analysis, or you may want to disable them entirely. Reading through this tutorial will give you a better understanding of how these (and other) metrics should be applied, so you can apply them effectively throughout your own explorations of this dataset.\n", + "Despite the fact that there's not a clear division between single-unit and multi-unit activity, we still have to make a binary decision in every analysis we carry out: should this unit be included or not? Ideally this decision should be based on objective metrics that will not bias the end results. Reading through this tutorial will give you a better understanding of how these (and other) metrics should be applied, so you can apply them effectively throughout your own explorations of this dataset.\n", "\n", "Metrics covered in this tutorial:\n", "* Firing rate (`firing_rate`)\n", @@ -76,12 +76,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import os\n", - "\n", "import numpy as np\n", "import pandas as pd\n", "from pathlib import Path\n", @@ -93,19 +101,19 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# # Example cache directory path, it determines where downloaded data will be stored\n", - "data_storage_directory = Path(\"/Users/scott.daniel/Pika/garage/vbn_cache\")\n", + "# Update this to a valid directory in your filesystem. This is where the data will be stored.\n", + "cache_dir = Path(\"/path/to/vbn_cache\")\n", "\n", - "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(cache_dir=data_storage_directory)" + "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(cache_dir=cache_dir)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -114,7 +122,7 @@ "319013" ] }, - "execution_count": 5, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -135,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -176,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -208,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -240,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -272,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -349,16 +357,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -401,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -410,7 +418,7 @@ "0.8" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -453,16 +461,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, @@ -507,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -516,7 +524,7 @@ "0.2" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -559,16 +567,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -609,16 +617,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -665,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -674,7 +682,7 @@ "0.17" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -718,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -791,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -850,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -908,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -966,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1030,18 +1038,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the Neuropixels Visual Coding Dataset (TO DO: ADD LINK) the default quality metric filters were set as follows:\n", + "In the Neuropixels [Visual Coding Dataset](https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels) the default quality metric filters were set as follows:\n", "\n", "- `isi_violations` < 0.5\n", "- `amplitude_cutoff` < 0.1\n", "- `presence_ratio` > 0.9\n", "\n", + "\n", "We can filter the Visual Behavior Dataset using these same criteria and find that 120,139 units pass this criteria. " ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -1050,13 +1059,15 @@ "120139" ] }, - "execution_count": 23, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "units_filt1 = units[(units.isi_violations<0.5) & (units.amplitude_cutoff<0.1) & (units.presence_ratio>0.9)]\n", + "units_filt1 = units[(units.isi_violations<0.5) \n", + " & (units.amplitude_cutoff<0.1) \n", + " & (units.presence_ratio>0.9)]\n", "len(units_filt1)" ] }, @@ -1074,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1083,7 +1094,7 @@ "258764" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1093,6 +1104,329 @@ "len(units_filt2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these metrics to filter units will require careful consideration of the sort of errors your analysis can tolerate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Waveform metrics\n", + "\n", + "The shape of the extracellularly recorded spike for a given neuron depends on a number of biophysical and morphological properties. We have included several pre-computed metrics summarizing the shape of the mean waveform for each unit, which may provide useful clues about cell-class identity (for example, see [this paper](https://journals.physiology.org/doi/full/10.1152/jn.00680.2018)).\n", + "\n", + "Look [here](https://github.com/AllenInstitute/ecephys_spike_sorting/tree/master/ecephys_spike_sorting/modules/mean_waveforms) for more detail on these metrics and the code that computes them. For the below descriptions, the '1D waveform' is defined as the waveform on the peak channel. The '2D waveform' is the waveform across channels centered on the peak channel.\n", + "\n", + "`amplitude`: Peak to trough amplitude for mean 1D waveform in microvolts \n", + "`waveform_duration`: Time from trough to peak for 1D waveform in milliseconds \n", + "`waveform_halfwidth`: Width of 1D waveform at half-amplitude in milliseconds \n", + "`PT_ratio`: Ratio of the max (peak) to the min (trough) amplitudes for 1D waveform \n", + "`recovery_slope`: Slope of recovery of 1D waveform to baseline after repolarization (coming down from peak) \n", + "`repolarization_slope`: Slope of repolarization of 1D waveform to baseline after trough \n", + "`spread`: Range of channels for which the spike amplitude was above 12% of the peak channel amplitude \n", + "`velocity_above`: Slope of spike propagation velocity traveling in dorsal direction from soma (note to avoid infinite values, this is actaully the inverse of velocity: ms/mm) \n", + "`velocity_below`: Slope of spike propagation velocity traveling in ventral direction from soma (note to avoid infinite values, this is actually the inverse of velocity: ms/mm) \n", + "`snr`: signal-to-noise ratio for 1D waveform \n", + "`quality`: Label assigned based on waveform shape as described [here](https://github.com/AllenInstitute/ecephys_spike_sorting/tree/7e567a6fc3fd2fc0eedef750b83b8b8a0d469544/ecephys_spike_sorting/modules/noise_templates). Either 'good' for physiological waveforms or 'noise' for artifactual waveforms.\n", + "\n", + "Now let's grab a session and plot the 2D waveform for a couple of units with disparate waveform features." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38\\lib\\site-packages\\hdmf\\utils.py:577: FutureWarning: DynamicTable.__init__: Using positional arguments for this method is discouraged and will be deprecated in a future major release. Please use keyword arguments to ensure future compatibility.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + } + ], + "source": [ + "session = cache.get_ecephys_session(\n", + " ecephys_session_id=1065437523)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "units = session.get_units()\n", + "channels = session.get_channels()\n", + "\n", + "#merge the units and channels tables to get full CCF/channel info for each unit\n", + "units = units.merge(channels, left_on='peak_channel_id', right_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at how a few of these metrics vary across areas:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean waveform features across areas\n" + ] + }, + { + "data": { + "text/html": [ + "
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velocity_abovevelocity_belowwaveform_duration
structure_acronym
APN0.002948-0.3528350.315640
CA10.136193-1.0297350.681104
CA30.568328-0.2534000.546954
DG-0.031349-0.7059970.757089
MRN0.025735-0.3398190.288572
PIL0.319534-0.6416320.418742
PP0.104285-0.2480050.258195
PoT0.242106-0.2419580.348878
ProS-0.177745-0.7137780.535678
SGN0.484660-0.2430370.494578
SUB-0.235398-0.9303390.547565
TH0.457104-0.3054150.434074
VISal0.5593250.1534560.565127
VISam0.7371560.1621190.624100
VISl0.7458640.0872580.578723
VISp0.6912650.1237560.584643
VISpm0.7483720.0308110.612513
VISrl0.7243090.2537670.563704
\n", + "
" + ], + "text/plain": [ + " velocity_above velocity_below waveform_duration\n", + "structure_acronym \n", + "APN 0.002948 -0.352835 0.315640\n", + "CA1 0.136193 -1.029735 0.681104\n", + "CA3 0.568328 -0.253400 0.546954\n", + "DG -0.031349 -0.705997 0.757089\n", + "MRN 0.025735 -0.339819 0.288572\n", + "PIL 0.319534 -0.641632 0.418742\n", + "PP 0.104285 -0.248005 0.258195\n", + "PoT 0.242106 -0.241958 0.348878\n", + "ProS -0.177745 -0.713778 0.535678\n", + "SGN 0.484660 -0.243037 0.494578\n", + "SUB -0.235398 -0.930339 0.547565\n", + "TH 0.457104 -0.305415 0.434074\n", + "VISal 0.559325 0.153456 0.565127\n", + "VISam 0.737156 0.162119 0.624100\n", + "VISl 0.745864 0.087258 0.578723\n", + "VISp 0.691265 0.123756 0.584643\n", + "VISpm 0.748372 0.030811 0.612513\n", + "VISrl 0.724309 0.253767 0.563704" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "area_waveform_stats = units.pivot_table(index='structure_acronym', \n", + " values=['velocity_above', 'velocity_below', 'waveform_duration'], \n", + " aggfunc=['mean', 'count'])\n", + "\n", + "print('Mean waveform features across areas')\n", + "display(area_waveform_stats[area_waveform_stats['count']['waveform_duration']>50]['mean'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can already see some interesting differences across areas. Notice for example that neurons in midbrain structures (like APN and MRN) have short duration spikes on average compared to neurons in the hippocampus or cortex. Also notice that the direction in which spikes propagate flips from cortical to hippocampal structures. This can be seen from the velocity_below metric, which quantifies how spikes propagate ventrally (into the brain; units are ms/mm). In hippocampus, this metric is negative indicating that spikes are propagating down into the brain from the soma. This agrees with the morphology of hippocampal pyramidal neurons, whose apical dendrites project ventrally. The opposite is true in visual cortex, where apical dendrites extend dorsally from the soma.\n", + "\n", + "Let's pick a couple of units with disparate waveform features and plot their 2D waveforms:" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "unit1 = units[(units['velocity_below']<0) & \n", + " (units['waveform_duration']>0.4) &\n", + " (units['structure_acronym']=='CA1')&\n", + " (units['quality']=='good')].iloc[1]\n", + "\n", + "unit2 = units[(units['velocity_below']>0) & \n", + " (units['waveform_duration']<0.3)&\n", + " (units['structure_acronym']=='MRN')&\n", + " (units['quality']=='good')].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2)\n", + "ylabels = ['probe channel', '']\n", + "for iu, u in enumerate([unit1, unit2]):\n", + " waveform = session.mean_waveforms[u.name]\n", + " peak_chan = u['probe_channel_number']\n", + " ax[iu].imshow(waveform)\n", + " ax[iu].set_ylim([peak_chan-30, peak_chan+30])\n", + " ax[iu].set_xticks([0, 30, 60])\n", + " ax[iu].set_xticklabels([0, 1, 2])\n", + " ax[iu].set_ylabel(ylabels[iu])\n", + " ax[iu].set_xlabel('time (ms)')\n", + " ax[iu].set_title(u.structure_acronym)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quickstart.ipynb b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quickstart.ipynb old mode 100644 new mode 100755 index 77902061e..18f438cb1 --- a/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quickstart.ipynb +++ b/doc_template/examples_root/examples/nb/visual_behavior_neuropixels_quickstart.ipynb @@ -6,9 +6,9 @@ "source": [ "# Visual Behavior Neuropixels Quickstart\n", "\n", - "A short introduction to the Visual Behavior Neuropixels data and SDK. This notebook focuses on aligning neural data to visual and optotagging stimuli. For more information about task and behavioral data, check out the 'Aligning behavioral data to task events with the stimulus and trials tables' tutorial.\n", + "A short introduction to the Visual Behavior Neuropixels data and SDK. This notebook focuses on aligning neural data to visual and optotagging stimuli. To learn more about how to access the data, see our [data access tutorial](./visual_behavior_neuropixels_data_access.ipynb). For more information about task and behavioral data, check out the [other tutorials](https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html) accompanying this dataset.\n", "\n", - "Also note that this project shares many features with the [Visual Coding Neuropixels](http://portal.brain-map.org/explore/circuits/visual-coding-neuropixels) and [Visual Behavior 2-Photon](http://portal.brain-map.org/explore/circuits/visual-coding-2p) datasets. Users are encouraged to check out the documentation for these projects for additional information and context.\n", + "Also note that this project shares many features with the [Visual Coding Neuropixels](http://portal.brain-map.org/explore/circuits/visual-coding-neuropixels) and [Visual Behavior 2-Photon](http://portal.brain-map.org/explore/circuits/visual-coding-2p) datasets. Users are encouraged to check out the documentation for those projects for additional information and context.\n", "\n", "Contents\n", "-------------\n", @@ -19,21 +19,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (3.0.4) doesn't match a supported version!\n", - " RequestsDependencyWarning)\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", - "\n", + "from pathlib import Path\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -52,26 +43,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\svc_ccg\\Documents\\GitHub\\AllenSDK\\allensdk\\api\\cloud_cache\\cloud_cache.py:521: OutdatedManifestWarning: You are loading visual-behavior-neuropixels-2022_project_manifest_v0.1.0.json. A more up to date version of the dataset -- visual-behavior-neuropixels-2022_project_manifest_v0.2.0.json -- exists online. To see the changes between the two versions of the dataset, run\n", - "VisualBehaviorNeuropixelsProjectCache.compare_manifests('visual-behavior-neuropixels-2022_project_manifest_v0.1.0.json', 'visual-behavior-neuropixels-2022_project_manifest_v0.2.0.json')\n", - "To load another version of the dataset, run\n", - "VisualBehaviorNeuropixelsProjectCache.load_manifest('visual-behavior-neuropixels-2022_project_manifest_v0.2.0.json')\n", - " warnings.warn(msg, OutdatedManifestWarning)\n" - ] - } - ], + "outputs": [], "source": [ - "# this path determines where downloaded data will be stored\n", - "cache_dir = r\"C:\\Users\\svc_ccg\\Desktop\\Data\\vbn_cache\"\n", + "# Update this to a valid directory in your filesystem. This is where the data will be stored.\n", + "cache_dir = Path(\"/path/to/vbn_cache\")\n", "\n", "cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(\n", " cache_dir=cache_dir)\n", @@ -92,12 +71,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This dataset contains ephys recording sessions from 3 genotypes (C57BL6J, VIP-IRES-CrexAi32 and SST-IRES-CrexAi32). For each mouse, two recordings were made on consecutive days. One of these sessions used the image set that was familiar to the mouse from training. The other session used a new image set for which 6 of the images were novel two were familiar. As an example, let's grab a session from an SST mouse during a novel session." + "This dataset contains ephys recording sessions from 3 genotypes (C57BL6J, VIP-IRES-CrexAi32 and SST-IRES-CrexAi32). For each mouse, two recordings were made on consecutive days. One of these sessions used the image set that was familiar to the mouse from training. The other session used a novel image set containing two familiar images from training and six new images that the mouse had never seen. As an example, let's grab a session from an SST mouse during a novel session." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -140,8 +119,8 @@ " experience_level\n", " prior_exposures_to_omissions\n", " file_id\n", - " abnormal_activity\n", " abnormal_histology\n", + " abnormal_activity\n", " \n", " \n", " ecephys_session_id\n", @@ -170,17 +149,17 @@ " \n", " \n", " \n", - " 1052530003\n", - " 1052572357\n", - " 2020-09-24 14:34:32.031\n", - " NP.0\n", + " 1053941483\n", + " 1053960987\n", + " 2020-10-01 17:03:58.362\n", + " NP.1\n", " EPHYS_1_images_H_3uL_reward\n", - " 524926\n", + " 527749\n", " Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt\n", - " F\n", + " M\n", " NeuropixelVisualBehavior\n", - " 187\n", - " 2005.0\n", + " 180\n", + " 1543.0\n", " ...\n", " 2304.0\n", " ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...\n", @@ -189,22 +168,22 @@ " 2\n", " Novel\n", " 1.0\n", - " 3\n", - " [132]\n", - " ['Thalamus']\n", + " 6\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 1053941483\n", - " 1053960987\n", - " 2020-10-01 17:03:58.362\n", + " 1064644573\n", + " 1064666428\n", + " 2020-11-19 15:18:01.372\n", " NP.1\n", " EPHYS_1_images_H_3uL_reward\n", - " 527749\n", + " 544456\n", " Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt\n", " M\n", " NeuropixelVisualBehavior\n", - " 180\n", - " 1543.0\n", + " 120\n", + " 2254.0\n", " ...\n", " 2304.0\n", " ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...\n", @@ -213,22 +192,22 @@ " 2\n", " Novel\n", " 1.0\n", - " 6\n", - " False\n", + " 27\n", + " NaN\n", " NaN\n", " \n", " \n", - " 1046581736\n", - " 1046626452\n", - " 2020-08-27 14:39:26.423\n", - " NP.0\n", - " EPHYS_1_images_H_5uL_reward\n", - " 527294\n", + " 1048189115\n", + " 1048221709\n", + " 2020-09-03 14:16:57.913\n", + " NP.1\n", + " EPHYS_1_images_H_3uL_reward\n", + " 509808\n", " Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt\n", " M\n", " NeuropixelVisualBehavior\n", - " 146\n", - " 2193.0\n", + " 264\n", + " 1925.0\n", " ...\n", " 2304.0\n", " ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...\n", @@ -237,22 +216,22 @@ " 2\n", " Novel\n", " 1.0\n", - " 23\n", - " [111]\n", - " ['Hippocampus']\n", + " 37\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 1064644573\n", - " 1064666428\n", - " 2020-11-19 15:18:01.372\n", - " NP.1\n", + " 1048196054\n", + " 1048222325\n", + " 2020-09-03 14:25:07.290\n", + " NP.0\n", " EPHYS_1_images_H_3uL_reward\n", - " 544456\n", + " 524925\n", " Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt\n", - " M\n", + " F\n", " NeuropixelVisualBehavior\n", - " 120\n", - " 2254.0\n", + " 166\n", + " 2288.0\n", " ...\n", " 2304.0\n", " ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...\n", @@ -261,22 +240,22 @@ " 2\n", " Novel\n", " 1.0\n", - " 27\n", - " False\n", + " 38\n", + " NaN\n", " NaN\n", " \n", " \n", - " 1048189115\n", - " 1048221709\n", - " 2020-09-03 14:16:57.913\n", - " NP.1\n", + " 1065905010\n", + " 1065929713\n", + " 2020-11-24 14:21:48.847\n", + " NP.0\n", " EPHYS_1_images_H_3uL_reward\n", - " 509808\n", + " 544358\n", " Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt\n", - " M\n", + " F\n", " NeuropixelVisualBehavior\n", - " 264\n", - " 1925.0\n", + " 126\n", + " 1998.0\n", " ...\n", " 2304.0\n", " ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg...\n", @@ -285,8 +264,8 @@ " 2\n", " Novel\n", " 1.0\n", - " 37\n", - " False\n", + " 39\n", + " NaN\n", " NaN\n", " \n", " \n", @@ -297,86 +276,85 @@ "text/plain": [ " behavior_session_id date_of_acquisition \\\n", "ecephys_session_id \n", - "1052530003 1052572357 2020-09-24 14:34:32.031 \n", "1053941483 1053960987 2020-10-01 17:03:58.362 \n", - "1046581736 1046626452 2020-08-27 14:39:26.423 \n", "1064644573 1064666428 2020-11-19 15:18:01.372 \n", "1048189115 1048221709 2020-09-03 14:16:57.913 \n", + "1048196054 1048222325 2020-09-03 14:25:07.290 \n", + "1065905010 1065929713 2020-11-24 14:21:48.847 \n", "\n", " equipment_name session_type mouse_id \\\n", "ecephys_session_id \n", - "1052530003 NP.0 EPHYS_1_images_H_3uL_reward 524926 \n", "1053941483 NP.1 EPHYS_1_images_H_3uL_reward 527749 \n", - "1046581736 NP.0 EPHYS_1_images_H_5uL_reward 527294 \n", "1064644573 NP.1 EPHYS_1_images_H_3uL_reward 544456 \n", "1048189115 NP.1 EPHYS_1_images_H_3uL_reward 509808 \n", + "1048196054 NP.0 EPHYS_1_images_H_3uL_reward 524925 \n", + "1065905010 NP.0 EPHYS_1_images_H_3uL_reward 544358 \n", "\n", " genotype sex \\\n", "ecephys_session_id \n", - "1052530003 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt F \n", "1053941483 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt M \n", - "1046581736 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt M \n", "1064644573 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt M \n", "1048189115 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt M \n", + "1048196054 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt F \n", + "1065905010 Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt F \n", "\n", " project_code age_in_days unit_count ... \\\n", "ecephys_session_id ... \n", - "1052530003 NeuropixelVisualBehavior 187 2005.0 ... \n", "1053941483 NeuropixelVisualBehavior 180 1543.0 ... \n", - "1046581736 NeuropixelVisualBehavior 146 2193.0 ... \n", "1064644573 NeuropixelVisualBehavior 120 2254.0 ... \n", "1048189115 NeuropixelVisualBehavior 264 1925.0 ... \n", + "1048196054 NeuropixelVisualBehavior 166 2288.0 ... \n", + "1065905010 NeuropixelVisualBehavior 126 1998.0 ... \n", "\n", " channel_count \\\n", "ecephys_session_id \n", - "1052530003 2304.0 \n", "1053941483 2304.0 \n", - "1046581736 2304.0 \n", "1064644573 2304.0 \n", "1048189115 2304.0 \n", + "1048196054 2304.0 \n", + "1065905010 2304.0 \n", "\n", " structure_acronyms \\\n", "ecephys_session_id \n", - "1052530003 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", "1053941483 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", - "1046581736 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", "1064644573 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", "1048189115 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", + "1048196054 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", + "1065905010 ['APN', 'CA1', 'CA3', 'DG-mo', 'DG-po', 'DG-sg... \n", "\n", " image_set prior_exposures_to_image_set session_number \\\n", "ecephys_session_id \n", - "1052530003 H 0.0 2 \n", "1053941483 H 0.0 2 \n", - "1046581736 H 0.0 2 \n", "1064644573 H 0.0 2 \n", "1048189115 H 0.0 2 \n", + "1048196054 H 0.0 2 \n", + "1065905010 H 0.0 2 \n", "\n", " experience_level prior_exposures_to_omissions file_id \\\n", "ecephys_session_id \n", - "1052530003 Novel 1.0 3 \n", "1053941483 Novel 1.0 6 \n", - "1046581736 Novel 1.0 23 \n", "1064644573 Novel 1.0 27 \n", "1048189115 Novel 1.0 37 \n", + "1048196054 Novel 1.0 38 \n", + "1065905010 Novel 1.0 39 \n", "\n", - " abnormal_activity abnormal_histology \n", + " abnormal_histology abnormal_activity \n", "ecephys_session_id \n", - "1052530003 [132] ['Thalamus'] \n", - "1053941483 False NaN \n", - "1046581736 [111] ['Hippocampus'] \n", - "1064644573 False NaN \n", - "1048189115 False NaN \n", + "1053941483 NaN NaN \n", + "1064644573 NaN NaN \n", + "1048189115 NaN NaN \n", + "1048196054 NaN NaN \n", + "1065905010 NaN NaN \n", "\n", "[5 rows x 21 columns]" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ecephys_sessions_table = ecephys_sessions_table[0]\n", "sst_novel_sessions = ecephys_sessions_table.loc[(ecephys_sessions_table['genotype'].str.contains('Sst')) & \n", " (ecephys_sessions_table['experience_level']=='Novel')]\n", "sst_novel_sessions.head()" @@ -391,24 +369,20 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.5.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'core' version 2.4.0 because version 2.3.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n", - "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\hdmf\\spec\\namespace.py:533: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.1.0 is already loaded.\n", - " % (ns['name'], ns['version'], self.__namespaces.get(ns['name'])['version']))\n" + "C:\\Users\\svc_ccg\\AppData\\Local\\Continuum\\anaconda3\\envs\\allensdk_38\\lib\\site-packages\\hdmf\\utils.py:577: FutureWarning: DynamicTable.__init__: Using positional arguments for this method is discouraged and will be deprecated in a future major release. Please use keyword arguments to ensure future compatibility.\n", + " warnings.warn(msg, FutureWarning)\n" ] } ], "source": [ - "session_id = sst_novel_sessions.index.values[0]\n", + "session_id = 1064644573\n", "session = cache.get_ecephys_session(\n", " ecephys_session_id=session_id)" ] @@ -422,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": { "scrolled": false }, @@ -430,23 +404,23 @@ { "data": { "text/plain": [ - "{'equipment_name': 'NP.0',\n", - " 'sex': 'F',\n", - " 'age_in_days': 187,\n", + "{'equipment_name': 'NP.1',\n", + " 'sex': 'M',\n", + " 'age_in_days': 120,\n", " 'stimulus_frame_rate': 60.0,\n", " 'session_type': 'EPHYS_1_images_H_3uL_reward',\n", - " 'date_of_acquisition': datetime.datetime(2020, 9, 24, 21, 34, 32, tzinfo=tzutc()),\n", + " 'date_of_acquisition': datetime.datetime(2020, 11, 19, 23, 18, 1, tzinfo=tzutc()),\n", " 'reporter_line': 'Ai32(RCL-ChR2(H134R)_EYFP)',\n", " 'cre_line': 'Sst-IRES-Cre',\n", " 'behavior_session_uuid': None,\n", " 'driver_line': ['Sst-IRES-Cre'],\n", - " 'mouse_id': 524926,\n", + " 'mouse_id': 544456,\n", " 'full_genotype': 'Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt',\n", - " 'behavior_session_id': 1052572357,\n", - " 'ecephys_session_id': 1052530003}" + " 'behavior_session_id': 1064666428,\n", + " 'ecephys_session_id': 1064644573}" ] }, - "execution_count": 4, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "scrolled": false }, @@ -485,50 +459,50 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "structure_acronym\n", - "APN 214\n", - "CA1 185\n", - "CA3 16\n", - "DG 71\n", - "Eth 17\n", - "FF 3\n", - "HPF 16\n", - "HY 10\n", - "LGd 38\n", - "LP 12\n", - "MB 22\n", - "MGd 32\n", - "MGm 13\n", - "MGv 88\n", - "MRN 118\n", - "NB 39\n", - "PIL 52\n", - "POST 49\n", - "PoT 10\n", - "SNr 3\n", - "TH 74\n", - "VISam 89\n", - "VISl 54\n", - "VISp 170\n", - "VISpm 97\n", - "VISrl 161\n", - "ZI 33\n", - "Name: cluster_id, dtype: int64" + "APN 304\n", + "CA1 295\n", + "DG 190\n", + "VISp 144\n", + "VISpm 134\n", + "VISl 118\n", + "VISal 106\n", + "MB 103\n", + "CA3 100\n", + "LP 99\n", + "VISam 96\n", + "VISrl 92\n", + "MGv 86\n", + "MRN 79\n", + "ProS 70\n", + "NB 56\n", + "PIL 56\n", + "POST 32\n", + "NOT 27\n", + "Eth 15\n", + "root 14\n", + "TH 11\n", + "SUB 9\n", + "HPF 9\n", + "MGd 7\n", + "POL 1\n", + "MGm 1\n", + "dtype: int64" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "unit_channels.groupby('structure_acronym')['cluster_id'].count()" + "unit_channels.value_counts('structure_acronym')" ] }, { @@ -542,12 +516,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we'll grab spike times and calculate the change response for 'good' units in V1. Note that how you filter units will depend on your analysis. Consult the unit metrics notebook for more details." + "Now we'll grab spike times and calculate the change response for 'good' units in V1. Note that how you filter units will depend on your analysis. Consult the [unit metrics notebook](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_neuropixels_quality_metrics.html) for more details." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -556,8 +530,8 @@ "\n", "#now we'll filter them\n", "good_unit_filter = ((unit_channels['snr']>1)&\n", - " (units['isi_violations']<1)&\n", - " (units['firing_rate']>0.1))\n", + " (unit_channels['isi_violations']<1)&\n", + " (unit_channels['firing_rate']>0.1))\n", "\n", "good_units = unit_channels.loc[good_unit_filter]\n", "spike_times = session.spike_times" @@ -572,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -583,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -630,1721 +604,165 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", + "text/plain": [ + "Text(0, 0.5, 'Firing Rate')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the results\n", + "fig, ax = plt.subplots(1,2)\n", + "fig.set_size_inches([12,4])\n", + "\n", + "clims = [np.percentile(area_change_responses, p) for p in (0.1,99.9)]\n", + "im = ax[0].imshow(area_change_responses, clim=clims)\n", + "ax[0].set_title('Active Change Responses for {}'.format(area_of_interest))\n", + "ax[0].set_ylabel('Unit number, sorted by depth')\n", + "ax[0].set_xlabel('Time from change (s)')\n", + "ax[0].set_xticks(np.arange(0, bins.size-1, 20))\n", + "_ = ax[0].set_xticklabels(np.round(bins[:-1:20]-time_before_change, 2))\n", + "\n", + "ax[1].plot(bins[:-1]-time_before_change, np.mean(area_change_responses, axis=0), 'k')\n", + "ax[1].set_title('{} population active change response (n={})'\\\n", + " .format(area_of_interest, area_change_responses.shape[0]))\n", + "ax[1].set_xlabel('Time from change (s)')\n", + "ax[1].set_ylabel('Firing Rate')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Receptive Fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll plot receptive fields for these same units. First we need to get stimulus presentation data for the receptive field mapping stimulus (gabors). For more information about this stimulus, consult [this tutorial](https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_receptive_fields.html) for the Visual Coding Neuropixels dataset (though note that not all the functionality in the visual coding SDK will work for this dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "rf_stim_table = stimulus_presentations[stimulus_presentations['stimulus_name'].str.contains('gabor')]\n", + "xs = np.sort(rf_stim_table.position_x.unique()) #positions of gabor along azimuth\n", + "ys = np.sort(rf_stim_table.position_y.unique()) #positions of gabor along elevation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def find_rf(spikes, xs, ys):\n", + " unit_rf = np.zeros([ys.size, xs.size])\n", + " for ix, x in enumerate(xs):\n", + " for iy, y in enumerate(ys):\n", + " stim_times = rf_stim_table[(rf_stim_table.position_x==x)\n", + " &(rf_stim_table.position_y==y)]['start_time'].values\n", + " unit_response, bins = makePSTH(spikes, \n", + " stim_times+0.01, \n", + " 0.2, binSize=0.001)\n", + " unit_rf[iy, ix] = unit_response.mean()\n", + " return unit_rf\n", + "\n", + "area_rfs = []\n", + "for iu, unit in area_units.iterrows():\n", + " unit_spike_times = spike_times[iu]\n", + " unit_rf = find_rf(unit_spike_times, xs, ys)\n", + " area_rfs.append(unit_rf)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(int(len(area_rfs)/10)+1, 10)\n", + "fig.set_size_inches(12, 8)\n", + "for irf, rf in enumerate(area_rfs):\n", + " ax_row = int(irf/10)\n", + " ax_col = irf%10\n", + " axes[ax_row][ax_col].imshow(rf, origin='lower')\n", + "for ax in axes.flat:\n", + " ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optotagging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since this is an SST mouse, we should see putative SST+ interneurons that are activated during our optotagging protocol. Let's load the optotagging stimulus table and plot PSTHs triggered on the laser onset. For more examples and useful info about optotagging, you can check out the Visual Coding Neuropixels Optagging notebook [here](https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html) (though note that not all the functionality in the visual coding SDK will work for this dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", "\n", @@ -2371,46 +789,46 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2421,11 +839,11 @@ "text/plain": [ " start_time condition level stop_time \\\n", "id \n", - "0 8803.77363 half-period of a cosine wave 0.78 8804.77363 \n", - "1 8805.63517 a single square pulse 1.00 8805.64517 \n", - "2 8807.35557 a single square pulse 1.70 8807.36557 \n", - "3 8809.04593 a single square pulse 1.00 8809.05593 \n", - "4 8811.07608 half-period of a cosine wave 0.78 8812.07608 \n", + "0 8801.01737 half-period of a cosine wave 0.950 8802.01737 \n", + "1 8803.02730 a single square pulse 0.800 8803.03730 \n", + "2 8805.12189 a single square pulse 0.800 8805.13189 \n", + "3 8806.91173 a single square pulse 0.800 8806.92173 \n", + "4 8809.01283 half-period of a cosine wave 0.685 8810.01283 \n", "\n", " stimulus_name duration \n", "id \n", @@ -2436,7 +854,7 @@ "4 raised_cosine 1.00 " ] }, - "execution_count": 173, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2450,30 +868,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you check out this table, you'll see that we use 2 different laser waveforms: a half-period cosine wave that's 1 second long and a short square pulse that's 10 ms long. We drive each at three light levels, giving us 6 total conditions. Now let's plot how cortical neurons respond to the short pulse at high power." + "If you check out this table, you'll see that we use 2 different laser waveforms: a short square pulse that's 10 ms long and a half-period cosine that's 1 second long:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We drive each at three light levels, giving us 6 total conditions. Now let's plot how cortical neurons respond to the short pulse at high power." ] }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ - "duration = opto_table.duration.min()\n", - "level = opto_table.level.max()\n", + "duration = opto_table.duration.min() #get the short pulses\n", + "level = opto_table.level.max() #and the high power trials\n", "\n", "cortical_units = good_units[good_units['structure_acronym'].str.contains('VIS')]\n", "\n", "\n", "opto_times = opto_table.loc[(opto_table['duration']==duration)&\n", " (opto_table['level']==level)]['start_time'].values\n", + "\n", + "time_before = 0.01 # seconds to take before the laser start for PSTH\n", + "duration = 0.03 # total duration of trial for PSTH in seconds\n", + "binSize = 0.001 # 1ms bin size for PSTH\n", "opto_response = []\n", "unit_id = []\n", "for iu, unit in cortical_units.iterrows():\n", " unit_spike_times = spike_times[iu]\n", " unit_response, bins = makePSTH(unit_spike_times, \n", - " opto_times-0.05, 0.1, \n", - " binSize=0.0005)\n", + " opto_times-time_before, duration, \n", + " binSize=binSize)\n", " \n", " opto_response.append(unit_response)\n", " unit_id.append(iu)\n", @@ -2483,810 +921,20 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
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08803.773638801.01737half-period of a cosine wave0.788804.773630.9508802.01737raised_cosine1.00
18805.635178803.02730a single square pulse1.008805.645170.8008803.03730pulse0.01
28807.355578805.12189a single square pulse1.708807.365570.8008805.13189pulse0.01
38809.045938806.91173a single square pulse1.008809.055930.8008806.92173pulse0.01
48811.076088809.01283half-period of a cosine wave0.788812.076080.6858810.01283raised_cosine1.00