{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Brain Observatory\n",
"This notebook documents some classes and functions in the AllenSDK that help manipulate files and data structures in the Allen Brain Observatory. The main entry point in the `BrainObservatoryCache` class. This class is responsible for downloading any requested data or metadata on request and storing it in well known locations.\n",
"\n",
"Download this file in .ipynb format here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Experiment Containers\n",
"The experiment container describes a set of experiments performed with the same targeted area, imaging depth, and Cre line. The `BrainObservatoryCache` has a number of functions for figuring out what experiment containers are available at the moment."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all targeted structures: [u'VISal', u'VISam', u'VISl', u'VISp', u'VISpm', u'VISrl']\n"
]
}
],
"source": [
"from allensdk.core.brain_observatory_cache import BrainObservatoryCache\n",
"import pprint\n",
"\n",
"# This class uses a 'manifest' to keep track of downloaded data and metadata. \n",
"# All downloaded files will be stored relative to the directory holding the manifest\n",
"# file. If 'manifest_file' is a relative path (as it is below), it will be \n",
"# saved relative to your working directory. It can also be an absolute path.\n",
"boc = BrainObservatoryCache(manifest_file='boc/manifest.json')\n",
"\n",
"# Download a list of all targeted areas\n",
"targeted_structures = boc.get_all_targeted_structures()\n",
"print(\"all targeted structures: \" + str(targeted_structures))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all VISp experiment containers: 46\n"
]
}
],
"source": [
"# Download experiment containers for VISp experiments\n",
"visp_ecs = boc.get_experiment_containers(targeted_structures=['VISp'])\n",
"print(\"all VISp experiment containers: %d\" % len(visp_ecs))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all imaging depths: [175, 265, 275, 300, 320, 325, 335, 350, 365, 375]\n"
]
}
],
"source": [
"# Download a list of all imaging depths\n",
"depths = boc.get_all_imaging_depths()\n",
"print(\"all imaging depths: \" + str(depths))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all stimuli:\n",
"\n",
"['drifting_gratings',\n",
" 'locally_sparse_noise',\n",
" 'locally_sparse_noise_4deg',\n",
" 'locally_sparse_noise_8deg',\n",
" 'natural_movie_one',\n",
" 'natural_movie_three',\n",
" 'natural_movie_two',\n",
" 'natural_scenes',\n",
" 'spontaneous',\n",
" 'static_gratings']\n"
]
}
],
"source": [
"# Download a list of all stimuli\n",
"stims = boc.get_all_stimuli()\n",
"print(\"all stimuli:\\n\")\n",
"pprint.pprint(stims)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all cre lines:\n",
"\n",
"[u'Cux2-CreERT2',\n",
" u'Emx1-IRES-Cre',\n",
" u'Nr5a1-Cre',\n",
" u'Rbp4-Cre_KL100',\n",
" u'Rorb-IRES2-Cre',\n",
" u'Scnn1a-Tg3-Cre']\n"
]
}
],
"source": [
"# Download a list of all cre driver lines \n",
"cre_lines = boc.get_all_cre_lines()\n",
"print(\"all cre lines:\\n\")\n",
"pprint.pprint(cre_lines)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cux2 experiments: 62\n",
"\n",
"Example experiment container record:\n",
"{'cre_line': u'Cux2-CreERT2',\n",
" 'donor_name': u'283284',\n",
" 'failed': False,\n",
" 'id': 566759225,\n",
" 'imaging_depth': 275,\n",
" 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
" 'tags': [],\n",
" 'targeted_structure': u'VISam'}\n"
]
}
],
"source": [
"# Download experiment containers for Cux2 experiments\n",
"cux2_ecs = boc.get_experiment_containers(cre_lines=['Cux2-CreERT2'])\n",
"print(\"Cux2 experiments: %d\\n\" % len(cux2_ecs))\n",
"\n",
"print(\"Example experiment container record:\")\n",
"pprint.pprint(cux2_ecs[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Experiments for a Container\n",
"An experiment container is a group of experiments. Each experiment has a different stimulus protocol. For example, one experiment protocol contains the static gratings stimulus and another has the natural scenes stimulus. The `BrainObservatoryCache` helps you find out which experiment associated with a container has the stimuli you are interested in. First, let's see what experiments are available for a single container."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Experiments for experiment_container_id 566759225: 3\n",
"\n",
"[{'acquisition_age_days': 129,\n",
" 'cre_line': u'Cux2-CreERT2',\n",
" 'donor_name': u'283284',\n",
" 'experiment_container_id': 566759225,\n",
" 'fail_eye_tracking': False,\n",
" 'id': 569494121,\n",
" 'imaging_depth': 275,\n",
" 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
" 'session_type': u'three_session_C2',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
" 'targeted_structure': u'VISam'},\n",
" {'acquisition_age_days': 128,\n",
" 'cre_line': u'Cux2-CreERT2',\n",
" 'donor_name': u'283284',\n",
" 'experiment_container_id': 566759225,\n",
" 'fail_eye_tracking': False,\n",
" 'id': 569407590,\n",
" 'imaging_depth': 275,\n",
" 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
" 'session_type': u'three_session_B',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
" 'targeted_structure': u'VISam'},\n",
" {'acquisition_age_days': 140,\n",
" 'cre_line': u'Cux2-CreERT2',\n",
" 'donor_name': u'283284',\n",
" 'experiment_container_id': 566759225,\n",
" 'fail_eye_tracking': False,\n",
" 'id': 570305847,\n",
" 'imaging_depth': 275,\n",
" 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
" 'session_type': u'three_session_A',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-283284',\n",
" 'targeted_structure': u'VISam'}]\n"
]
}
],
"source": [
"# Find all of the experiments for an experiment container\n",
"cux2_ec_id = cux2_ecs[0]['id']\n",
"exps = boc.get_ophys_experiments(experiment_container_ids=[cux2_ec_id])\n",
"print(\"Experiments for experiment_container_id %d: %d\\n\" % (cux2_ec_id, len(exps)))\n",
"pprint.pprint(exps)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `session_type` field indicates which experimental protocol was used. If you just want to find which experiment contains the static gratings stimulus, you can do the following:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Experiment with static gratings:\n",
"{'acquisition_age_days': 101,\n",
" 'cre_line': u'Cux2-CreERT2',\n",
" 'donor_name': u'225036',\n",
" 'experiment_container_id': 511510779,\n",
" 'fail_eye_tracking': True,\n",
" 'id': 503019786,\n",
" 'imaging_depth': 275,\n",
" 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
" 'session_type': u'three_session_B',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-225036',\n",
" 'targeted_structure': u'VISp'}\n"
]
}
],
"source": [
"import allensdk.brain_observatory.stimulus_info as stim_info\n",
"\n",
"# pick one of the cux2 experiment containers\n",
"cux2_ec_id = cux2_ecs[-1]['id']\n",
"\n",
"# Find the experiment with the static static gratings stimulus\n",
"exp = boc.get_ophys_experiments(experiment_container_ids=[cux2_ec_id], \n",
" stimuli=[stim_info.STATIC_GRATINGS])[0]\n",
"print(\"Experiment with static gratings:\")\n",
"pprint.pprint(exp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can download the NWB file for this experiment."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'age_days': 101,\n",
" 'cre_line': u'Cux2-CreERT2/wt',\n",
" 'device': u'Nikon A1R-MP multiphoton microscope',\n",
" 'device_name': u'CAM2P.2',\n",
" 'excitation_lambda': u'910 nanometers',\n",
" 'experiment_container_id': 511510779,\n",
" 'fov': u'400x400 microns (512 x 512 pixels)',\n",
" 'genotype': u'Cux2-CreERT2/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/Ai93(TITL-GCaMP6f)',\n",
" 'imaging_depth_um': 275,\n",
" 'indicator': u'GCaMP6f',\n",
" 'ophys_experiment_id': 503019786,\n",
" 'pipeline_version': u'2.0',\n",
" 'session_start_time': datetime.datetime(2016, 2, 21, 17, 41, 47),\n",
" 'session_type': u'three_session_B',\n",
" 'sex': u'male',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-225036',\n",
" 'targeted_structure': u'VISp'}\n"
]
}
],
"source": [
"exp = boc.get_ophys_experiment_data(exp['id'])\n",
"\n",
"# print out the metadata available in the NWB file\n",
"pprint.pprint(exp.get_metadata())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find Cells of Interest\n",
"Another way to look for data is to search for cells with interesting tuning properties. We have pre-computed a set of cell metrics. Take a look at this page to see the full list.\n",
"\n",
"You can use these properties to filter the full set of cells down to the set your are interested in."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total cells: 37091\n",
"VISp cells: 12127\n",
"cells with sig. response to drifting gratings: 6610\n",
"direction-selective cells: 686\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Download cells for a set of experiments and convert to DataFrame\n",
"cells = boc.get_cell_specimens()\n",
"cells = pd.DataFrame.from_records(cells)\n",
"print(\"total cells: %d\" % len(cells))\n",
"\n",
"# find direction selective cells in VISp\n",
"visp_ec_ids = [ ec['id'] for ec in visp_ecs ]\n",
"visp_cells = cells[cells['experiment_container_id'].isin(visp_ec_ids)]\n",
"print(\"VISp cells: %d\" % len(visp_cells))\n",
"\n",
"# significant response to drifting gratings stimulus\n",
"sig_cells = visp_cells[visp_cells['p_dg'] < 0.05]\n",
"print(\"cells with sig. response to drifting gratings: %d\" % len(sig_cells))\n",
"\n",
"# direction selective cells\n",
"dsi_cells = sig_cells[(sig_cells['g_dsi_dg'] > 0.9)]\n",
"print(\"direction-selective cells: %d\" % len(dsi_cells))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find Experiments for Cells\n",
"Once you have found a set of cells you would like to investigate, you can use the `BrainObservatoryCache` to find the experiments for those cells that contain the relevant stimulus."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total dsi experiment containers: 45\n",
"VISp drifting gratings ophys experiments: 45\n",
"Example ophys experiment:\n",
"{'acquisition_age_days': 111,\n",
" 'cre_line': u'Cux2-CreERT2',\n",
" 'donor_name': u'244896',\n",
" 'experiment_container_id': 527550471,\n",
" 'fail_eye_tracking': True,\n",
" 'id': 527745328,\n",
" 'imaging_depth': 275,\n",
" 'reporter_line': u'Ai93(TITL-GCaMP6f)',\n",
" 'session_type': u'three_session_A',\n",
" 'specimen_name': u'Cux2-CreERT2;Camk2a-tTA;Ai93-244896',\n",
" 'targeted_structure': u'VISp'}\n"
]
}
],
"source": [
"import allensdk.brain_observatory.stimulus_info as stim_info\n",
"\n",
"# find experiment containers for those cells\n",
"dsi_ec_ids = dsi_cells['experiment_container_id'].unique()\n",
"print(\"total dsi experiment containers: %d\" % len(dsi_ec_ids))\n",
"\n",
"# Download the ophys experiments containing the drifting gratings stimulus for VISp experiment containers\n",
"dsi_exps = boc.get_ophys_experiments(experiment_container_ids=dsi_ec_ids, stimuli=[stim_info.DRIFTING_GRATINGS])\n",
"print(\"VISp drifting gratings ophys experiments: %d\" % len(dsi_exps))\n",
"\n",
"print(\"Example ophys experiment:\")\n",
"pprint.pprint(dsi_exps[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Experiment Data for a Cell\n",
"Once you have some experiments, you can download the NWB files that contain the fluorescence traces for segmented cells in those experiments."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metadata from NWB file:\n",
"{'age_days': 93,\n",
" 'cre_line': u'Scnn1a-Tg3-Cre/wt',\n",
" 'device': u'Nikon A1R-MP multiphoton microscope',\n",
" 'device_name': u'CAM2P.2',\n",
" 'excitation_lambda': u'910 nanometers',\n",
" 'experiment_container_id': 511498742,\n",
" 'fov': u'400x400 microns (512 x 512 pixels)',\n",
" 'genotype': u'Scnn1a-Tg3-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/Ai93(TITL-GCaMP6f)',\n",
" 'imaging_depth_um': 350,\n",
" 'indicator': u'GCaMP6f',\n",
" 'ophys_experiment_id': 511534603,\n",
" 'pipeline_version': u'2.0',\n",
" 'session_start_time': datetime.datetime(2016, 4, 1, 9, 12, 3),\n",
" 'session_type': u'three_session_A',\n",
" 'sex': u'female',\n",
" 'specimen_name': u'Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93-231953',\n",
" 'targeted_structure': u'VISp'}\n",
"stimuli available in this file:\n",
"[u'drifting_gratings', u'natural_movie_one', u'natural_movie_three', u'spontaneous']\n"
]
}
],
"source": [
"# pick a direction-selective cell and find its NWB file\n",
"dsi_cell = dsi_cells.iloc[0]\n",
"\n",
"# figure out which ophys experiment has the drifting gratings stimulus for that cell\n",
"cell_exp = boc.get_ophys_experiments(cell_specimen_ids=[dsi_cell['cell_specimen_id']],\n",
" stimuli=[stim_info.DRIFTING_GRATINGS])[0]\n",
"\n",
"data_set = boc.get_ophys_experiment_data(cell_exp['id'])\n",
"\n",
"print(\"Metadata from NWB file:\")\n",
"pprint.pprint(data_set.get_metadata())\n",
"\n",
"print(\"stimuli available in this file:\")\n",
"print(data_set.list_stimuli())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fluorescence Traces\n",
"Now that we have a data set, we can plot the traces for the cell we care about."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dsi_cell_id = dsi_cell['cell_specimen_id']\n",
"time, raw_traces = data_set.get_fluorescence_traces(cell_specimen_ids=[dsi_cell_id])\n",
"_, demixed_traces = data_set.get_demixed_traces(cell_specimen_ids=[dsi_cell_id])\n",
"_, neuropil_traces = data_set.get_neuropil_traces(cell_specimen_ids=[dsi_cell_id])\n",
"_, corrected_traces = data_set.get_corrected_fluorescence_traces(cell_specimen_ids=[dsi_cell_id])\n",
"_, dff_traces = data_set.get_dff_traces(cell_specimen_ids=[dsi_cell_id])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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S1UFxzgDVrrAH4BtuiCcfRFT9Nt4YuOCCpHNBaVIowIkzAGhuBtLU8KUSzd7q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\nT55sLmh4p2W7iRPNuM3NmceJbPY4Zp14ojvsnXe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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Pb52cYTl69+79Y39DQwMaGhrCZIdqVNhCbSXf4lNNiq2DlpZo55f2wkU+tb6v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\n9a+LTztihLlAqKoed1zhce+6y3w+84z5bGpSXWc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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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FUa7MtXjXLMYnqV+o/JCSZLETvckmtQurXOVrzz3jlyUKqtVrkJLIM9ztLZgs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\nBa8Yd96p+vXXrl/z57sdjVVXza8EunV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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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wYDJP4PET9obV7t2BY48tbx5xr+Ow6Z1ySnlBqz1r553PTz9Fm79buctg7Njy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\n/X+DGugaGSJK/wAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# plot raw and corrected ROI trace\n",
"plt.figure(figsize=(14,4))\n",
"plt.title(\"Raw Fluorescence Trace\")\n",
"plt.plot(time, raw_traces[0])\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=(14,4))\n",
"plt.title(\"Demixed Fluorescence Trace\")\n",
"plt.plot(time, demixed_traces[0])\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=(14,4))\n",
"plt.title(\"Neuropil-corrected Fluorescence Trace\")\n",
"plt.plot(time, corrected_traces[0])\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=(14,4))\n",
"plt.title(\"dF/F Trace\")\n",
"# warning: dF/F can occasionally be one element longer or shorter \n",
"# than the time stamps for the original traces.\n",
"plt.plot(time[:len(dff_traces[0])], dff_traces[0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ROI Masks\n",
"If you want to take a look at the cell visually, you can open the NWB file and extract a pixel mask. You can also pull out the maximum intensity projection of the movie for context."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rW9mwYQOHH374jI59MDMXNhuNRo2QsCspAjU94STvS2tNU1MTY2NjJl9tcHCw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\n+utYvtUAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"\n",
"data_set = boc.get_ophys_experiment_data(510221121)\n",
"\n",
"# get the specimen IDs for a few cells\n",
"cids = data_set.get_cell_specimen_ids()[:15:5]\n",
"\n",
"# get masks for specific cells\n",
"roi_mask_list = data_set.get_roi_mask(cell_specimen_ids=cids)\n",
"\n",
"# plot each mask\n",
"f, axes = plt.subplots(1, len(cids)+2, figsize=(15, 3))\n",
"for ax, roi_mask, cid in zip(axes[:-2], roi_mask_list, cids):\n",
" ax.imshow(roi_mask.get_mask_plane(), cmap='gray')\n",
" ax.set_title('cell %d' % cid)\n",
"\n",
"# make a mask of all ROIs in the experiment \n",
"all_roi_masks = data_set.get_roi_mask_array()\n",
"combined_mask = all_roi_masks.max(axis=0)\n",
"\n",
"axes[-2].imshow(combined_mask, cmap='gray')\n",
"axes[-2].set_title('all ROIs')\n",
"\n",
"# show the movie max projection\n",
"max_projection = data_set.get_max_projection()\n",
"axes[-1].imshow(max_projection, cmap='gray')\n",
"axes[-1].set_title('max projection')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ROI Analysis\n",
"The code to compute all of the cell metrics available for download (along with several others we haven't put in our database yet) is in the AllenSDK. All you need is an NWB file. For example, this is how you compute metrics for the drifting gratings stimulus and plot some results."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done analyzing drifting gratings\n"
]
}
],
"source": [
"from allensdk.brain_observatory.drifting_gratings import DriftingGratings\n",
"\n",
"# example loading drifing grating data\n",
"data_set = boc.get_ophys_experiment_data(512326618)\n",
"dg = DriftingGratings(data_set)\n",
"dg_peak = dg.peak\n",
"print(\"done analyzing drifting gratings\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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3gIlblvkKrE4WA89UVFYpsX3p2vmJr8gmsyrjuqWateBKT3BpDLFpwMURcUVu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\n7l1cAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# filter for visually responding, selective cells\n",
"vis_cells = (dg_peak.ptest_dg < 0.05) & (dg_peak.peak_dff_dg > 3)\n",
"osi_cells = vis_cells & (dg_peak.osi_dg > 0.5) & (dg_peak.osi_dg <= 1.5)\n",
"dsi_cells = vis_cells & (dg_peak.dsi_dg > 0.5) & (dg_peak.dsi_dg <= 1.5)\n",
"\n",
"# 2-d tf vs. ori histogram\n",
"# tfval = 0 is used for the blank sweep, so we are ignoring it here\n",
"os = np.zeros((len(dg.orivals), len(dg.tfvals)-1))\n",
"ds = np.zeros((len(dg.orivals), len(dg.tfvals)-1))\n",
"\n",
"for i,trial in dg_peak[osi_cells].iterrows():\n",
" os[trial.ori_dg, trial.tf_dg-1] += 1\n",
" \n",
"for i,trial in dg_peak[dsi_cells].iterrows():\n",
" ds[trial.ori_dg, trial.tf_dg-1] += 1\n",
"\n",
"max_count = max(os.max(), ds.max())\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"\n",
"# plot direction selectivity\n",
"im = ax1.imshow(ds, clim=[0,max_count], cmap='hot', interpolation='nearest')\n",
"ax1.set_xlabel('temporal frequency')\n",
"ax1.set_ylabel('direction')\n",
"ax1.set_xticks(np.arange(len(dg.tfvals)-1))\n",
"ax1.set_xticklabels(dg.tfvals[1:])\n",
"ax1.set_yticks(np.arange(len(dg.orivals)))\n",
"ax1.set_yticklabels(dg.orivals)\n",
"ax1.set_title('direction selective cells')\n",
"\n",
"# plot orientation selectivity\n",
"im = ax2.imshow(os, clim=[0,max_count], cmap='hot', interpolation='nearest')\n",
"ax2.set_xlabel('temporal frequency')\n",
"ax2.set_ylabel('orientation')\n",
"ax2.set_xticks(np.arange(len(dg.tfvals)-1))\n",
"ax2.set_xticklabels(dg.tfvals[1:])\n",
"ax2.set_yticks(np.arange(len(dg.orivals)))\n",
"ax2.set_yticklabels(dg.orivals)\n",
"ax2.set_title('orientation selective cells')\n",
"\n",
"# plot a colorbar\n",
"fig.subplots_adjust(right=0.9)\n",
"cbar_ax = fig.add_axes([0.95, 0.05, 0.05, 0.85])\n",
"cbar = fig.colorbar(im, cax=cbar_ax)\n",
"cbar.set_ticks(np.arange(0, max_count, 2)+0.5)\n",
"cbar.set_ticklabels(np.arange(0, max_count, 2, dtype=int))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Neuropil Correction\n",
"All of the raw fluorescence traces are available in NWB files, but some of these signals are contaminated by nearby neuropil signal. Neuropil correction is performed after the signal from overlapping traces has been demixed. The code to perform neuropil correction is available in the AllenSDK and can be used as follows:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"r = 0.142000\n",
"max error = 0.005002\n"
]
}
],
"source": [
"from allensdk.brain_observatory.r_neuropil import estimate_contamination_ratios\n",
"\n",
"data_set = boc.get_ophys_experiment_data(569407590)\n",
"csid = data_set.get_cell_specimen_ids()[0]\n",
"\n",
"time, demixed_traces = data_set.get_demixed_traces(cell_specimen_ids=[csid])\n",
"_, neuropil_traces = data_set.get_neuropil_traces(cell_specimen_ids=[csid])\n",
"\n",
"results = estimate_contamination_ratios(demixed_traces[0], neuropil_traces[0])\n",
"correction = demixed_traces[0] - results['r'] * neuropil_traces[0]\n",
"print(\"r = %f\" % results['r'])\n",
"print(\"max error = %f\" % results['err'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The NWB files contain the neuropil traces and `r` values needed to perform this correction, so you don't need to recompute them. The corrected trace can be computed on the fly when you use the `get_corrected_fluorescence_traces` method:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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S+NByiy2S57xUhs1R8L6o1BtkAabImQ0QVE0aatQwTax7pTpPBwwwLaq1bOkO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\ndp/b8Tao8gY/999vrgOvb75xl7d5swn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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, corrected_traces = data_set.get_corrected_fluorescence_traces(cell_specimen_ids=[csid])\n",
"\n",
"plt.figure(figsize=(14,4))\n",
"plt.title(\"Neuropil-corrected Fluorescence Trace\")\n",
"plt.plot(time, corrected_traces[0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute dF/F\n",
"You can compute dF/F for yourself using the `allensdk.brain_observatory.dff` module."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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PFL8APPWUf24s7aJwxRXxWrXo/088Md4/GvxHb5Ur3C5Jt7EU3paZdLF8883w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\nrG1azjmnttOXpMUXD3+vv77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DIDR6G2QgWvjR3TzxRHGGMyhUKHTUUeE5LGratPgzImb+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\nsp/BayhZIwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from allensdk.brain_observatory.dff import compute_dff\n",
"\n",
"plt.figure(figsize=(14,4))\n",
"plt.title(\"dF/F Trace\")\n",
"dff = compute_dff(np.array(corrected_traces))\n",
"plt.plot(time, dff[0,:])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running Speed\n",
"We recorded the animal's running speed during the course of the experiment and made it available in the NWB file."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rxGPZMj1cKV0y17KlfgfUfvu5p6dHD13KMGiQe7XN776z+ydM0Bn8yy5Lfzbt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\nBkFERERERFRR/h+bikRfGJ6/MQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"data_set = boc.get_ophys_experiment_data(501940850)\n",
"\n",
"dxcm, dxtime = data_set.get_running_speed()\n",
"plt.figure(figsize=(14,4))\n",
"plt.plot(dxtime, dxcm)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Motion Correction\n",
"X and Y translation values in pixels required to correct for motion artifacts during the experiment are available as well."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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uZF3hqYXRd11fxdnPgzEHkc2zELJ3N70LALiTfActF7dEWlYaAKkgwtL/luLr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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"data_set = boc.get_ophys_experiment_data(501940850)\n",
"\n",
"mc = data_set.get_motion_correction()\n",
"\n",
"plt.figure(figsize=(14,4))\n",
"plt.plot(mc.timestamp, mc.x_motion)\n",
"plt.plot(mc.timestamp, mc.y_motion)\n",
"plt.legend(['x motion','y motion'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Eye Tracking\n",
"Many experiments contain pupil position and pupil size from eye tracking. Extreme outliers from the tracking have been removed and replaced with NaN, which will appear as gaps in the plotted data. If an experiment does not have eye tracking data, a NoEyeTrackingException will be raised."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No eye tracking for experiment 501940850.\n"
]
},
{
"data": {
"image/png": 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Zj9zeuV1vrJtISUtBVFIUyj5T1vI+kYmRbk/TbYRSKXYh5kKWnSt/7vyy70a1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ufO7beLGp9cGVBE8bs8BY0bByX7kwonHH+8/rOqAaU2WlXrOXOB3NcnzveL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\nnAHl2NRw3m+HA/j++/xtq7zca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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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teptNSXTvXnQK8nHmmfq5owNYeeVi05K1++8vOgXlMG9e47dpGlsnkddN8ZQp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\nbkCU0jf106dX56qFef11nbM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30Vn49FOv0acR1lg+iKtRcD25P3HaDpk61f5thfUA5TpJ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\nOtqvvaaHEjj6aD09auwg01Xs5ZdHt1MJ60LXDJlgs8dmodZ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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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jgCeAk3s2NDMz29M6cp/CZcAxwLKI+FvgKGBTj0ZlZmaZ6EhS2BEROwAk9YmI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\ny3FSMDOzHCcFMzPL+f+tBk9itxbuGwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from allensdk.brain_observatory.brain_observatory_exceptions import NoEyeTrackingException\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# example with no eye tracking data\n",
"data_set = boc.get_ophys_experiment_data(501940850)\n",
"try:\n",
" timestamps, locations = data_set.get_pupil_location()\n",
"except NoEyeTrackingException:\n",
" print(\"No eye tracking for experiment %s.\" % data_set.get_metadata()[\"ophys_experiment_id\"])\n",
"\n",
"data_set = boc.get_ophys_experiment_data(569407590)\n",
" \n",
"# looking at azimuth and altitude over time\n",
"# by default locations returned are (azimuth, altitude)\n",
"# passing as_spherical=False to get_pupil_location will return (x,y) in cm\n",
"timestamps, locations = data_set.get_pupil_location()\n",
"plt.figure(figsize=(14,4))\n",
"plt.plot(timestamps, locations.T[0])\n",
"plt.plot(timestamps, locations.T[1])\n",
"plt.title(\"Eye position over time\")\n",
"plt.xlabel(\"time (s)\")\n",
"plt.ylabel(\"angle (deg)\")\n",
"plt.legend(['azimuth', 'altitude'])\n",
"plt.show()\n",
"\n",
"#pupil size over time\n",
"timestamps, area = data_set.get_pupil_size()\n",
"plt.figure(figsize=(14,4))\n",
"plt.plot(timestamps, area)\n",
"plt.title(\"Pupil size over time\")\n",
"plt.xlabel(\"time (s)\")\n",
"plt.ylabel(\"area (px)\")\n",
"plt.ylim(0, 20000)\n",
"plt.show()\n",
"\n",
"# scatter of gaze positions over approximate screen area\n",
"plt.figure()\n",
"plt.scatter(locations.T[0], locations.T[1], s=2, c=\"m\", edgecolor=\"\")\n",
"plt.title(\"Eye position scatter plot\")\n",
"plt.xlim(-70, 70)\n",
"plt.ylim(-60, 60)\n",
"plt.xlabel(\"azimuth (deg)\")\n",
"plt.ylabel(\"altitude (deg)\")\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}