From a9e78a066fe341379768fb2ecef40debeef072b0 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Wed, 27 Apr 2022 15:23:28 -0500 Subject: [PATCH 1/8] WIP trialization --- notebooks/00-data-download-optional.ipynb | 10 +- notebooks/03-process.ipynb | 210 ++++++++++++++++-- .../00-data-download-optional.py | 0 .../{scripts => py_scripts}/01-configure.py | 0 .../02-workflow-structure-optional.py | 0 .../{scripts => py_scripts}/03-process.py | 0 .../06-drop-optional.py | 0 user_data/alignments.csv | 4 + user_data/behavior_recordings.csv | 3 + user_data/blocks.csv | 3 + user_data/events.csv | 78 +++++++ user_data/trials.csv | 51 +++++ workflow_miniscope/analysis.py | 136 ++++++++++++ 13 files changed, 471 insertions(+), 24 deletions(-) rename notebooks/{scripts => py_scripts}/00-data-download-optional.py (100%) rename notebooks/{scripts => py_scripts}/01-configure.py (100%) rename notebooks/{scripts => py_scripts}/02-workflow-structure-optional.py (100%) rename notebooks/{scripts => py_scripts}/03-process.py (100%) rename notebooks/{scripts => py_scripts}/06-drop-optional.py (100%) create mode 100644 user_data/alignments.csv create mode 100644 user_data/behavior_recordings.csv create mode 100644 user_data/blocks.csv create mode 100644 user_data/events.csv create mode 100644 user_data/trials.csv create mode 100644 workflow_miniscope/analysis.py diff --git a/notebooks/00-data-download-optional.ipynb b/notebooks/00-data-download-optional.ipynb index 3c0f38c..94b1e04 100644 --- a/notebooks/00-data-download-optional.ipynb +++ b/notebooks/00-data-download-optional.ipynb @@ -103,7 +103,8 @@ "metadata": {}, "outputs": [], "source": [ - "client.download('workflow-miniscope-test-set', target_directory='/tmp/example_data', revision='v1')" + "client.download('workflow-miniscope-test-set', \n", + " target_directory='/tmp/example_data', revision='v1')" ] }, { @@ -146,8 +147,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3.7.9 64-bit ('workflow-calcium-imaging': conda)", - "name": "python379jvsc74a57bd01a512f474e195e32ad84236879d3bb44800a92b431919ef0b10d543f5012a23c" + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" }, "language_info": { "codemirror_mode": { @@ -159,7 +161,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.11" } }, "nbformat": 4, diff --git a/notebooks/03-process.ipynb b/notebooks/03-process.ipynb index 8b797d9..71d36e9 100644 --- a/notebooks/03-process.ipynb +++ b/notebooks/03-process.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,11 +46,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from workflow_miniscope.pipeline import *\n", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@dss-db.datajoint.io:3306\n" + ] + } + ], + "source": [ + "import datajoint as dj\n", + "from workflow_miniscope.pipeline import subject, session, miniscope, Equipment, \\\n", + " AnatomicalLocation\n", "from element_interface.utils import find_full_path" ] }, @@ -93,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -264,9 +274,146 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RecordingInfo: 0it [00:00, ?it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Store metadata about recording\n", + "
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nchannels

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nframes

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px_height

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spatial_downsample

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subject12021-01-01 00:00:0101111770600600nannan20.02.015.0=BLOB=
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id nchannels nframes px_height px_width um_height um_width fps gain spatial_downsa led_power time_stamp\n", + "+----------+ +------------+ +------------+ +-----------+ +---------+ +-----------+ +----------+ +-----------+ +----------+ +------+ +------+ +------------+ +-----------+ +--------+\n", + "subject1 2021-01-01 00: 0 1 111770 600 600 nan nan 20.0 2.0 1 5.0 =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "populate_settings = {'display_progress': True}\n", "miniscope.RecordingInfo.populate(**populate_settings)\n", @@ -288,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -341,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -390,9 +537,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing: 0%| | 0/1 [00:47\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mminiscope\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProcessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mpopulate_settings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m in \u001b[0;36mpopulate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/element_interface/run_caiman.py\u001b[0m in \u001b[0;36mrun_caiman\u001b[0;34m(file_paths, parameters, sampling_rate, output_dir, is3D)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mcnm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCNMF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdview\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m cnmf_output, mc_output = cnm.fit_file(\n\u001b[0m\u001b[1;32m 44\u001b[0m motion_correct=True, include_eval=True, output_dir=output_dir, return_mc=True)\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: fit_file() got an unexpected keyword argument 'output_dir'" + ] + } + ], "source": [ "miniscope.Processing.populate(**populate_settings)" ] @@ -532,9 +702,9 @@ "formats": "ipynb,scripts//py" }, "kernelspec": { - "display_name": "Python 3.7.9 64-bit ('workflow-calcium-imaging': conda)", + "display_name": "venv-nwb", "language": "python", - "name": "python3" + "name": "venv-nwb" }, "language_info": { "codemirror_mode": { @@ -546,9 +716,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.8.11" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/scripts/00-data-download-optional.py b/notebooks/py_scripts/00-data-download-optional.py similarity index 100% rename from notebooks/scripts/00-data-download-optional.py rename to notebooks/py_scripts/00-data-download-optional.py diff --git a/notebooks/scripts/01-configure.py b/notebooks/py_scripts/01-configure.py similarity index 100% rename from notebooks/scripts/01-configure.py rename to notebooks/py_scripts/01-configure.py diff --git a/notebooks/scripts/02-workflow-structure-optional.py b/notebooks/py_scripts/02-workflow-structure-optional.py similarity index 100% rename from notebooks/scripts/02-workflow-structure-optional.py rename to notebooks/py_scripts/02-workflow-structure-optional.py diff --git a/notebooks/scripts/03-process.py b/notebooks/py_scripts/03-process.py similarity index 100% rename from notebooks/scripts/03-process.py rename to notebooks/py_scripts/03-process.py diff --git a/notebooks/scripts/06-drop-optional.py b/notebooks/py_scripts/06-drop-optional.py similarity index 100% rename from notebooks/scripts/06-drop-optional.py rename to notebooks/py_scripts/06-drop-optional.py diff --git a/user_data/alignments.csv b/user_data/alignments.csv new file mode 100644 index 0000000..f27ba1f --- /dev/null +++ b/user_data/alignments.csv @@ -0,0 +1,4 @@ +alignment_name,alignment_event_type,alignment_time_shift,start_event_type,start_time_shift,end_event_type,end_time_shift +left_button,left,0,left,-1,left,1 +center_button,center,0,center,-1,center,1 +right_button,right,0,right,-1,right,1 diff --git a/user_data/behavior_recordings.csv b/user_data/behavior_recordings.csv new file mode 100644 index 0000000..11c6dd6 --- /dev/null +++ b/user_data/behavior_recordings.csv @@ -0,0 +1,3 @@ +subject,session_datetime,filepath +subject1,2019-01-01 00:00:00,./user_data/trials.csv +subject1,2019-01-01 00:00:00,./user_data/events.csv diff --git a/user_data/blocks.csv b/user_data/blocks.csv new file mode 100644 index 0000000..d6f943a --- /dev/null +++ b/user_data/blocks.csv @@ -0,0 +1,3 @@ +subject,session_datetime,block_id,block_start_time,block_stop_time,attribute_name,attribute_value +subject1,2019-01-01 00:00:00,1,0,24,type,light +subject1,2019-01-01 00:00:00,2,24,48,type,dark diff --git a/user_data/events.csv b/user_data/events.csv new file mode 100644 index 0000000..1da572a --- /dev/null +++ b/user_data/events.csv @@ -0,0 +1,78 @@ +subject,session_datetime,trial_id,event_start_time,event_type +subject1,2019-01-01 00:00:00,1,0.269,right +subject1,2019-01-01 00:00:00,1,0.407,left +subject1,2019-01-01 00:00:00,2,1.611,center +subject1,2019-01-01 00:00:00,2,1.649,center +subject1,2019-01-01 00:00:00,3,2.935,left +subject1,2019-01-01 00:00:00,3,2.777,left +subject1,2019-01-01 00:00:00,4,4.006,right +subject1,2019-01-01 00:00:00,5,5.123,center +subject1,2019-01-01 00:00:00,6,5.995,right +subject1,2019-01-01 00:00:00,6,6.033,center +subject1,2019-01-01 00:00:00,7,7.29,left +subject1,2019-01-01 00:00:00,7,7.397,center +subject1,2019-01-01 00:00:00,8,8.54,left +subject1,2019-01-01 00:00:00,8,8.531,center +subject1,2019-01-01 00:00:00,9,10.118,left +subject1,2019-01-01 00:00:00,9,10.035,center +subject1,2019-01-01 00:00:00,10,11.042,right +subject1,2019-01-01 00:00:00,11,12.496,center +subject1,2019-01-01 00:00:00,11,12.293,right +subject1,2019-01-01 00:00:00,12,13.666,left +subject1,2019-01-01 00:00:00,13,14.845,right +subject1,2019-01-01 00:00:00,14,15.822,left +subject1,2019-01-01 00:00:00,14,15.642,left +subject1,2019-01-01 00:00:00,15,16.82,left +subject1,2019-01-01 00:00:00,16,18.004,left +subject1,2019-01-01 00:00:00,16,17.763,left +subject1,2019-01-01 00:00:00,17,18.943,center +subject1,2019-01-01 00:00:00,18,19.952,right +subject1,2019-01-01 00:00:00,18,20.211,center +subject1,2019-01-01 00:00:00,19,21.054,right +subject1,2019-01-01 00:00:00,19,21.262,right +subject1,2019-01-01 00:00:00,20,22.541,center +subject1,2019-01-01 00:00:00,21,23.339,left +subject1,2019-01-01 00:00:00,21,23.444,right +subject1,2019-01-01 00:00:00,22,24.616,right +subject1,2019-01-01 00:00:00,23,25.554,left +subject1,2019-01-01 00:00:00,23,25.492,left +subject1,2019-01-01 00:00:00,24,26.724,center +subject1,2019-01-01 00:00:00,25,24.476,left +subject1,2019-01-01 00:00:00,25,24.323,right +subject1,2019-01-01 00:00:00,26,25.423,center +subject1,2019-01-01 00:00:00,26,25.31,center +subject1,2019-01-01 00:00:00,27,26.546,left +subject1,2019-01-01 00:00:00,28,27.5,left +subject1,2019-01-01 00:00:00,29,28.625,right +subject1,2019-01-01 00:00:00,29,28.523,right +subject1,2019-01-01 00:00:00,30,29.498,right +subject1,2019-01-01 00:00:00,31,30.654,center +subject1,2019-01-01 00:00:00,32,31.914,center +subject1,2019-01-01 00:00:00,32,32.165,right +subject1,2019-01-01 00:00:00,33,33.228,right +subject1,2019-01-01 00:00:00,34,34.287,center +subject1,2019-01-01 00:00:00,34,34.315,center +subject1,2019-01-01 00:00:00,35,35.383,right +subject1,2019-01-01 00:00:00,35,35.588,center +subject1,2019-01-01 00:00:00,36,36.832,left +subject1,2019-01-01 00:00:00,36,36.597,center +subject1,2019-01-01 00:00:00,37,38.001,center +subject1,2019-01-01 00:00:00,37,37.973,left +subject1,2019-01-01 00:00:00,38,39.011,right +subject1,2019-01-01 00:00:00,39,40.092,left +subject1,2019-01-01 00:00:00,39,40.099,left +subject1,2019-01-01 00:00:00,40,41.133,center +subject1,2019-01-01 00:00:00,40,41.179,center +subject1,2019-01-01 00:00:00,41,42.206,right +subject1,2019-01-01 00:00:00,42,43.695,right +subject1,2019-01-01 00:00:00,43,44.761,center +subject1,2019-01-01 00:00:00,44,45.896,center +subject1,2019-01-01 00:00:00,44,45.916,center +subject1,2019-01-01 00:00:00,45,47.316,right +subject1,2019-01-01 00:00:00,46,48.23,center +subject1,2019-01-01 00:00:00,47,49.44,left +subject1,2019-01-01 00:00:00,47,49.559,center +subject1,2019-01-01 00:00:00,48,50.527,left +subject1,2019-01-01 00:00:00,49,51.947,left +subject1,2019-01-01 00:00:00,50,48.535,right +subject1,2019-01-01 00:00:00,50,48.404,right diff --git a/user_data/trials.csv b/user_data/trials.csv new file mode 100644 index 0000000..626665b --- /dev/null +++ b/user_data/trials.csv @@ -0,0 +1,51 @@ +subject,session_datetime,block_id,trial_id,trial_start_time,trial_stop_time,trial_type,attribute_name,attribute_value +subject1,2019-01-01 00:00:00,1,1,0.193,1.057,stim,lumen,646 +subject1,2019-01-01 00:00:00,1,2,1.468,2.332,ctrl,lumen,555 +subject1,2019-01-01 00:00:00,1,3,2.662,3.526,ctrl,lumen,907 +subject1,2019-01-01 00:00:00,1,4,3.738,4.602,ctrl,lumen,639 +subject1,2019-01-01 00:00:00,1,5,4.826,5.69,stim,lumen,835 +subject1,2019-01-01 00:00:00,1,6,5.973,6.837,stim,lumen,815 +subject1,2019-01-01 00:00:00,1,7,7.252,8.116,ctrl,lumen,995 +subject1,2019-01-01 00:00:00,1,8,8.462,9.326,ctrl,lumen,869 +subject1,2019-01-01 00:00:00,1,9,9.731,10.595,ctrl,lumen,501 +subject1,2019-01-01 00:00:00,1,10,11.019,11.883,stim,lumen,901 +subject1,2019-01-01 00:00:00,1,11,12.206,13.07,stim,lumen,738 +subject1,2019-01-01 00:00:00,1,12,13.279,14.143,stim,lumen,638 +subject1,2019-01-01 00:00:00,1,13,14.427,15.291,ctrl,lumen,585 +subject1,2019-01-01 00:00:00,1,14,15.563,16.427,ctrl,lumen,974 +subject1,2019-01-01 00:00:00,1,15,16.715,17.579,ctrl,lumen,515 +subject1,2019-01-01 00:00:00,1,16,17.706,18.57,ctrl,lumen,575 +subject1,2019-01-01 00:00:00,1,17,18.841,19.705,stim,lumen,788 +subject1,2019-01-01 00:00:00,1,18,19.828,20.692,stim,lumen,509 +subject1,2019-01-01 00:00:00,1,19,20.965,21.829,stim,lumen,789 +subject1,2019-01-01 00:00:00,1,20,22.113,22.977,ctrl,lumen,928 +subject1,2019-01-01 00:00:00,1,21,23.094,23.958,stim,lumen,937 +subject1,2019-01-01 00:00:00,1,22,24.314,25.178,stim,lumen,733 +subject1,2019-01-01 00:00:00,1,23,25.321,26.185,stim,lumen,527 +subject1,2019-01-01 00:00:00,1,24,26.311,27.175,stim,lumen,857 +subject1,2019-01-01 00:00:00,1,25,24.139,25.003,stim,lumen,536 +subject1,2019-01-01 00:00:00,2,26,25.159,26.023,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,27,26.203,27.067,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,28,27.192,28.056,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,29,28.465,29.329,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,30,29.43,30.294,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,31,30.557,31.421,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,32,31.774,32.638,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,33,33.034,33.898,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,34,34.175,35.039,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,35,35.36,36.224,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,36,36.481,37.345,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,37,37.63,38.494,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,38,38.748,39.612,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,39,39.995,40.859,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,40,41.027,41.891,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,41,42.161,43.025,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,42,43.296,44.16,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,43,44.523,45.387,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,44,45.733,46.597,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,45,46.913,47.777,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,46,48.067,48.931,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,47,49.198,50.062,stim,lumen,0 +subject1,2019-01-01 00:00:00,2,48,50.268,51.132,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,49,51.52,52.384,ctrl,lumen,0 +subject1,2019-01-01 00:00:00,2,50,48.211,49.075,ctrl,lumen,0 diff --git a/workflow_miniscope/analysis.py b/workflow_miniscope/analysis.py new file mode 100644 index 0000000..720c449 --- /dev/null +++ b/workflow_miniscope/analysis.py @@ -0,0 +1,136 @@ +import datajoint as dj +import numpy as np + +from workflow_miniscope.pipeline import db_prefix, session, miniscope, trial, event + + +schema = dj.schema(db_prefix + 'analysis') + + +@schema +class ActivityAlignmentCondition(dj.Manual): + definition = """ + -> miniscope.Activity + -> event.AlignmentEvent + trial_condition: varchar(128) # user-friendly name of condition + --- + condition_description='': varchar(1000) + bin_size=0.04: float # bin-size (in second) used to compute the PSTH + """ + + class Trial(dj.Part): + definition = """ # Trials (or subset) to compute event-aligned activity + -> master + -> trial.Trial + """ + + +@schema +class ActivityAlignment(dj.Computed): + definition = """ + -> ActivityAlignmentCondition + --- + aligned_timestamps: longblob + """ + + class AlignedTrialActivity(dj.Part): + definition = """ + -> master + -> miniscope.Activity.Trace + -> ActivityAlignmentCondition.Trial + --- + aligned_trace: longblob # (s) Calcium activity aligned to the event time + """ + + def make(self, key): + sess_time, scan_time, nframes, frame_rate = (scan.ScanInfo * session.Session + & key + ).fetch1('session_datetime', + 'scan_datetime', + 'nframes', 'fps') + + # Estimation of frame timestamps with respect to the session-start + # (to be replaced by timestamps retrieved from some synchronization routine) + scan_start = (scan_time - sess_time).total_seconds() if scan_time else 0 + frame_timestamps = np.arange(nframes) / frame_rate + scan_start + + trialized_event_times = trial.get_trialized_alignment_event_times( + key, trial.Trial & (ActivityAlignmentCondition.Trial & key)) + + min_limit = (trialized_event_times.event - trialized_event_times.start).max() + max_limit = (trialized_event_times.end - trialized_event_times.event).max() + + aligned_timestamps = np.arange(-min_limit, max_limit, 1/frame_rate) + nsamples = len(aligned_timestamps) + + trace_keys, activity_traces = (miniscope.Activity.Trace & key + ).fetch('KEY', 'activity_trace', order_by='mask') + activity_traces = np.vstack(activity_traces) + + aligned_trial_activities = [] + for _, r in trialized_event_times.iterrows(): + if r.event is None or np.isnan(r.event): + continue + alignment_start_idx = int((r.event - min_limit) * frame_rate) + roi_aligned_activities = activity_traces[:, + alignment_start_idx: + (alignment_start_idx + nsamples)] + if roi_aligned_activities.shape[-1] != nsamples: + shape_diff = nsamples - roi_aligned_activities.shape[-1] + roi_aligned_activities = np.pad(roi_aligned_activities, + ((0, 0), (0, shape_diff)), + mode='constant', constant_values=np.nan) + + aligned_trial_activities.extend([{**key, **r.trial_key, **trace_key, + 'aligned_trace': aligned_trace} + for trace_key, aligned_trace + in zip(trace_keys, + roi_aligned_activities)]) + + self.insert1({**key, 'aligned_timestamps': aligned_timestamps}) + self.AlignedTrialActivity.insert(aligned_trial_activities) + + def plot_aligned_activities(self, key, roi, axs=None, title=None): + """ + Plot event-aligned Calcium activities for all selected trials, and + trial-averaged Calcium activity + e.g. dF/F, neuropil-corrected dF/F, Calcium events, etc. + :param key: key of ActivityAlignment master table + :param roi: miniscope segmentation mask + :param axs: optional definition of axes for plot. + Default is plt.subplots(2, 1, figsize=(12, 8)) + :param title: Optional title label + """ + import matplotlib.pyplot as plt + + fig = None + if axs is None: + fig, (ax0, ax1) = plt.subplots(2, 1, figsize=(12, 8)) + else: + ax0, ax1 = axs + + aligned_timestamps = (self & key).fetch1('aligned_timestamps') + trial_ids, aligned_spikes = (self.AlignedTrialActivity + & key & {'mask': roi}).fetch( + 'trial_id', 'aligned_trace', order_by='trial_id') + + aligned_spikes = np.vstack(aligned_spikes) + + ax0.imshow(aligned_spikes, cmap='inferno', + interpolation='nearest', aspect='auto', + extent=(aligned_timestamps[0], + aligned_timestamps[-1], + 0, + aligned_spikes.shape[0])) + ax0.axvline(x=0, linestyle='--', color='white') + ax0.set_axis_off() + + ax1.plot(aligned_timestamps, np.nanmean(aligned_spikes, axis=0)) + ax1.axvline(x=0, linestyle='--', color='black') + ax1.set_xlabel('Time (s)') + ax1.set_xlim(aligned_timestamps[0], aligned_timestamps[-1]) + + if title: + plt.suptitle(title) + + return fig From c1e819678a9c8ebc2b7b04aaf0abef6002735728 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Wed, 27 Apr 2022 15:31:05 -0500 Subject: [PATCH 2/8] WIP add ingest funcs --- workflow_miniscope/ingest.py | 60 ++++++++++++++++++++++++++++++------ 1 file changed, 51 insertions(+), 9 deletions(-) diff --git a/workflow_miniscope/ingest.py b/workflow_miniscope/ingest.py index 500fa5f..9fccc18 100644 --- a/workflow_miniscope/ingest.py +++ b/workflow_miniscope/ingest.py @@ -5,17 +5,18 @@ from .pipeline import subject, session, Equipment, miniscope from .paths import get_miniscope_root_data_dir -from element_interface.utils import find_full_path, recursive_search +from element_interface.utils import find_full_path, recursive_search, csv -def ingest_subjects(subject_csv_path='./user_data/subjects.csv'): - print('\n-------------- Insert new "Subject" --------------') - with open(subject_csv_path, newline= '') as f: - input_subjects = list(csv.DictReader(f, delimiter=',')) - print(f'\n---- Insert {len(input_subjects)} entry(s) into subject.Subject ----') - subject.Subject.insert(input_subjects, skip_duplicates=True) +def ingest_subjects(subject_csv_path='./user_data/subjects.csv', + skip_duplicates=True, verbose=True): + """ + Ingest subjects listed in the subject column of ./user_data/subjects.csv + """ + csvs = [subject_csv_path] + tables = [subject.Subject()] - print('\n---- Successfully completed ingest_subjects ----') + ingest_csv_to_table(csvs, tables, skip_duplicates=skip_duplicates, verbose=verbose) def ingest_sessions(session_csv_path='./user_data/sessions.csv'): @@ -73,7 +74,8 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv'): acquisition_software=acquisition_software, recording_directory=session_dir.as_posix())) - print(f'\n---- Insert {len(set(val for dic in hardware_list for val in dic.values()))} entry(s) into lab.Equipment ----') + new_equipment_n = len(set(val for dic in hardware_list for val in dic.values())) + print(f'\n---- Insert {new_equipment_n} entry(s) into lab.Equipment ----') Equipment.insert(hardware_list, skip_duplicates=True) print(f'\n---- Insert {len(session_list)} entry(s) into session.Session ----') @@ -86,6 +88,46 @@ def ingest_sessions(session_csv_path='./user_data/sessions.csv'): print('\n---- Successfully completed ingest_sessions ----') +def ingest_events(recording_csv_path='./user_data/behavior_recordings.csv', + block_csv_path='./user_data/blocks.csv', + trial_csv_path='./user_data/trials.csv', + event_csv_path='./user_data/events.csv', + skip_duplicates=True, verbose=True): + """ + Ingest each level of experiment heirarchy for element-trial: + recording, block (i.e., phases of trials), trials (repeated units), + events (optionally 0-duration occurances within trial). + This ingestion function is duplicated across wf-array-ephys and wf-calcium-imaging + """ + csvs = [recording_csv_path, recording_csv_path, + block_csv_path, block_csv_path, + trial_csv_path, trial_csv_path, trial_csv_path, + trial_csv_path, + event_csv_path, event_csv_path, event_csv_path] + tables = [event.BehaviorRecording(), event.BehaviorRecording.File(), + trial.Block(), trial.Block.Attribute(), + trial.TrialType(), trial.Trial(), trial.Trial.Attribute(), + trial.BlockTrial(), + event.EventType(), event.Event(), trial.TrialEvent()] + + ingest_csv_to_table(csvs, tables, skip_duplicates=skip_duplicates, verbose=verbose) + # allow_direct_insert=True) + # Allow direct insert required bc element-trial has Imported that should be Manual + # ISSUE: element-interface version doesn't have allow_direct_insert arg + + +def ingest_alignment(alignment_csv_path='./user_data/alignments.csv', + skip_duplicates=True, verbose=True): + """This is duplicated across wf-array-ephys and wf-calcium-imaging""" + + csvs = [alignment_csv_path] + tables = [event.AlignmentEvent()] + + ingest_csv_to_table(csvs, tables, skip_duplicates=skip_duplicates, verbose=verbose) + + if __name__ == '__main__': ingest_subjects() ingest_sessions() + ingest_events() + ingest_alignment() From b244abca25686955b0b28c346b60e5038e1296f4 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 28 Apr 2022 11:21:49 -0500 Subject: [PATCH 3/8] revised notebooks to reflect updated element --- notebooks/00-data-download-optional.ipynb | 3 +- notebooks/01-configure.ipynb | 3 +- .../02-workflow-structure-optional.ipynb | 102 ++++++++-------- notebooks/03-process.ipynb | 112 ++++++++++-------- notebooks/06-drop-optional.ipynb | 11 +- .../py_scripts/00-data-download-optional.py | 8 +- .../02-workflow-structure-optional.py | 17 ++- notebooks/py_scripts/03-process.py | 16 ++- workflow_miniscope/pipeline.py | 6 +- 9 files changed, 160 insertions(+), 118 deletions(-) diff --git a/notebooks/00-data-download-optional.ipynb b/notebooks/00-data-download-optional.ipynb index 94b1e04..21379fc 100644 --- a/notebooks/00-data-download-optional.ipynb +++ b/notebooks/00-data-download-optional.ipynb @@ -143,8 +143,7 @@ ], "metadata": { "jupytext": { - "formats": "ipynb,scripts//py", - "main_language": "python" + "formats": "ipynb,scripts//py" }, "kernelspec": { "display_name": "venv-nwb", diff --git a/notebooks/01-configure.ipynb b/notebooks/01-configure.ipynb index 65c7f52..f8be0b3 100644 --- a/notebooks/01-configure.ipynb +++ b/notebooks/01-configure.ipynb @@ -197,7 +197,8 @@ ], "metadata": { "jupytext": { - "formats": "ipynb,scripts//py" + "formats": "ipynb,scripts//py", + "main_language": "python" }, "kernelspec": { "display_name": "Python 3.7.9 64-bit ('workflow-calcium-imaging': conda)", diff --git a/notebooks/02-workflow-structure-optional.ipynb b/notebooks/02-workflow-structure-optional.ipynb index b93d50c..caece11 100644 --- a/notebooks/02-workflow-structure-optional.ipynb +++ b/notebooks/02-workflow-structure-optional.ipynb @@ -47,11 +47,14 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ Each module contains a schema object that enables interaction with the schema in the database." - ] + "cell_type": "code", + "execution_count": null, + "id": "693929a9", + "metadata": { + "title": "Each module contains a schema object that enables interaction with the schema in the database." + }, + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -65,11 +68,14 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ The table classes in the module corresponds to a table in the schema in the database." - ] + "cell_type": "code", + "execution_count": null, + "id": "d0ee126a", + "metadata": { + "title": "The table classes in the module corresponds to a table in the schema in the database." + }, + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -90,9 +96,9 @@ "title": "The first time importing the modules, empty schemas and tables will be created in the database." }, "source": [ - "+ By importing the modules for the first time, the schemas and tables will be created inside the database.\n", + "# + By importing the modules for the first time, the schemas and tables will be created inside the database.\n", "\n", - "+ Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed and manipulated by the modules." + "# + Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed and manipulated by the modules." ] }, { @@ -104,7 +110,7 @@ "source": [ "## DataJoint tools to explore schemas and tables\n", "\n", - "+ `dj.list_schemas()`: list all schemas a user has access to in the current database" + "# + `dj.list_schemas()`: list all schemas a user has access to in the current database" ] }, { @@ -119,11 +125,14 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ `dj.Diagram()`: plot tables and dependencies in a schema. " - ] + "cell_type": "code", + "execution_count": null, + "id": "687dbcb3", + "metadata": { + "title": "`dj.Diagram()`: plot tables and dependencies in a schema." + }, + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -205,7 +214,7 @@ "title": "`heading`:" }, "source": [ - "+ `describe()`: show table definition with foreign key references." + "# + `describe()`: show table definition with foreign key references." ] }, { @@ -218,11 +227,14 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ `heading`: show attribute definitions regardless of foreign key references" - ] + "cell_type": "code", + "execution_count": null, + "id": "08837864", + "metadata": { + "title": "`heading`: show attribute definitions regardless of foreign key references" + }, + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -243,7 +255,7 @@ "source": [ "# DataJoint Elements installed in `workflow-miniscope`\n", "\n", - "+ [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source." + "# + [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source." ] }, { @@ -255,19 +267,15 @@ "dj.Diagram(lab)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ [`subject`](https://github.com/datajoint/element-animal): general animal information, such as User, Genetic background." - ] - }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "title": "[`subject`](https://github.com/datajoint/element-animal): general animal information, such as User, Genetic background." + }, "outputs": [], "source": [ + "\n", "dj.Diagram(subject)" ] }, @@ -282,19 +290,15 @@ "subject.Subject.describe();" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ [`session`](https://github.com/datajoint/element-session): General information of experimental sessions." - ] - }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "title": "[`session`](https://github.com/datajoint/element-session): General information of experimental sessions." + }, "outputs": [], "source": [ + "\n", "dj.Diagram(session)" ] }, @@ -310,11 +314,14 @@ ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "+ [`miniscope`](https://github.com/datajoint/element-miniscope): miniscope raw recording and processed data" - ] + "cell_type": "code", + "execution_count": null, + "id": "dc175fed", + "metadata": { + "title": "[`miniscope`](https://github.com/datajoint/element-miniscope): miniscope raw recording and processed data" + }, + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -342,7 +349,8 @@ "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,scripts//py" + "formats": "ipynb,scripts//py", + "main_language": "python" }, "kernelspec": { "display_name": "Python 3.7.9 64-bit ('workflow-calcium-imaging': conda)", diff --git a/notebooks/03-process.ipynb b/notebooks/03-process.ipynb index 71d36e9..3f250bc 100644 --- a/notebooks/03-process.ipynb +++ b/notebooks/03-process.ipynb @@ -46,17 +46,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting cbroz@dss-db.datajoint.io:3306\n" - ] - } - ], + "outputs": [], "source": [ "import datajoint as dj\n", "from workflow_miniscope.pipeline import subject, session, miniscope, Equipment, \\\n", @@ -103,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +118,9 @@ "metadata": {}, "outputs": [], "source": [ - "Equipment.insert1(dict(acquisition_hardware='UCLA Miniscope'))" + "Equipment.insert1(dict(equipment='UCLA Miniscope',\n", + " modality='Miniscope',\n", + " description='V4, >1mm field of view, 1mm working distance'))" ] }, { @@ -235,7 +229,7 @@ " recording_id=0)\n", "\n", "miniscope.Recording.insert1(dict(**recording_key, \n", - " acquisition_hardware='UCLA Miniscope', \n", + " equipment='UCLA Miniscope', \n", " acquisition_software='Miniscope-DAQ-V4',\n", " recording_directory='subject1/session1',\n", " recording_notes='No notes for this session.'))\n", @@ -274,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -409,7 +403,7 @@ " (Total: 1)" ] }, - "execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -488,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -518,14 +512,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "miniscope.ProcessingTask.insert1(dict(**recording_key,\n", " paramset_id=0,\n", " processing_output_dir='subject1/session1/caiman',\n", - " task_mode='trigger'))" + " task_mode='load'))" ] }, { @@ -537,29 +531,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Processing: 0%| | 0/1 [00:47\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mminiscope\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProcessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mpopulate_settings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m in \u001b[0;36mpopulate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdisplay_progress\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m ):\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_populate1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpopulate_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0merror_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/element_interface/run_caiman.py\u001b[0m in \u001b[0;36mrun_caiman\u001b[0;34m(file_paths, parameters, sampling_rate, output_dir, is3D)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mcnm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCNMF\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdview\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m cnmf_output, mc_output = cnm.fit_file(\n\u001b[0m\u001b[1;32m 44\u001b[0m motion_correct=True, include_eval=True, output_dir=output_dir, return_mc=True)\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: fit_file() got an unexpected keyword argument 'output_dir'" + "Processing: 100%|███████████████████████████████| 1/1 [00:15<00:00, 15.85s/it]\n" ] } ], @@ -586,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -610,9 +589,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MotionCorrection: 100%|█████████████████████████| 1/1 [00:04<00:00, 4.01s/it]\n" + ] + } + ], "source": [ "miniscope.MotionCorrection.populate(**populate_settings)" ] @@ -629,9 +616,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Segmentation: 100%|█████████████████████████████| 1/1 [00:02<00:00, 2.25s/it]\n" + ] + } + ], "source": [ "miniscope.Segmentation.populate(**populate_settings)" ] @@ -660,9 +655,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fluorescence: 100%|█████████████████████████████| 1/1 [00:01<00:00, 1.83s/it]\n" + ] + } + ], "source": [ "miniscope.Fluorescence.populate(**populate_settings)" ] @@ -677,9 +680,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Activity: 100%|█████████████████████████████████| 2/2 [00:02<00:00, 1.45s/it]\n" + ] + } + ], "source": [ "miniscope.Activity.populate(**populate_settings)" ] @@ -688,10 +699,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Next steps\n", + "" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/06-drop-optional.ipynb b/notebooks/06-drop-optional.ipynb index e955455..b705fc3 100644 --- a/notebooks/06-drop-optional.ipynb +++ b/notebooks/06-drop-optional.ipynb @@ -40,7 +40,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "lines_to_next_cell": 0 + }, "outputs": [], "source": [ "# miniscope.schema.drop()\n", @@ -52,14 +54,17 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [] } ], "metadata": { "jupytext": { - "formats": "ipynb,scripts//py" + "formats": "ipynb,scripts//py", + "main_language": "python" }, "kernelspec": { "display_name": "Python 3.7.9 64-bit ('workflow-calcium-imaging': conda)", diff --git a/notebooks/py_scripts/00-data-download-optional.py b/notebooks/py_scripts/00-data-download-optional.py index 23781da..232ec78 100644 --- a/notebooks/py_scripts/00-data-download-optional.py +++ b/notebooks/py_scripts/00-data-download-optional.py @@ -8,8 +8,9 @@ # format_version: '1.5' # jupytext_version: 1.13.7 # kernelspec: -# display_name: 'Python 3.7.9 64-bit (''workflow-calcium-imaging'': conda)' -# name: python379jvsc74a57bd01a512f474e195e32ad84236879d3bb44800a92b431919ef0b10d543f5012a23c +# display_name: venv-nwb +# language: python +# name: venv-nwb # --- # # Download example dataset @@ -44,7 +45,8 @@ # Run download for a given dataset and revision: -client.download('workflow-miniscope-test-set', target_directory='/tmp/example_data', revision='v1') +client.download('workflow-miniscope-test-set', + target_directory='/tmp/example_data', revision='v1') # ## Directory structure # diff --git a/notebooks/py_scripts/02-workflow-structure-optional.py b/notebooks/py_scripts/02-workflow-structure-optional.py index b6d0b18..b1deefc 100644 --- a/notebooks/py_scripts/02-workflow-structure-optional.py +++ b/notebooks/py_scripts/02-workflow-structure-optional.py @@ -37,27 +37,30 @@ # + Each module contains a schema object that enables interaction with the schema in the database. + # + Each module imported above corresponds to one schema inside the database. For example, `ephys` corresponds to `neuro_ephys` schema in the database. miniscope.schema # + The table classes in the module corresponds to a table in the schema in the database. + # + Each datajoint table class inside the module corresponds to a table inside the schema. For example, the class `ephys.EphysRecording` correponds to the table `_ephys_recording` in the schema `neuro_ephys` in the database. # preview columns and contents in a table miniscope.Processing() # + The first time importing the modules, empty schemas and tables will be created in the database. [markdown] -# # + By importing the modules for the first time, the schemas and tables will be created inside the database. +# # # + By importing the modules for the first time, the schemas and tables will be created inside the database. # -# # + Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed and manipulated by the modules. +# # # + Once created, importing modules will not create schemas and tables again, but the existing schemas/tables can be accessed and manipulated by the modules. # + The schemas and tables will not be re-created when importing modules if they have existed. [markdown] # ## DataJoint tools to explore schemas and tables # -# # + `dj.list_schemas()`: list all schemas a user has access to in the current database +# # # + `dj.list_schemas()`: list all schemas a user has access to in the current database # + `dj.list_schemas()`: list all schemas a user could access. dj.list_schemas() -# + `dj.Diagram()`: plot tables and dependencies in a schema. +# + `dj.Diagram()`: plot tables and dependencies in a schema. + # + `dj.Diagram()`: plot tables and dependencies # plot diagram for all tables in a schema @@ -107,19 +110,20 @@ dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(miniscope) # + `heading`: [markdown] -# # + `describe()`: show table definition with foreign key references. +# # # + `describe()`: show table definition with foreign key references. # - miniscope.Processing.describe(); # + `heading`: show attribute definitions regardless of foreign key references + # + `heading`: show table attributes regardless of foreign key references. miniscope.Processing.heading # + ephys [markdown] # # DataJoint Elements installed in `workflow-miniscope` # -# # + [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source. +# # # + [`lab`](https://github.com/datajoint/element-lab): lab management related information, such as Lab, User, Project, Protocol, Source. # - dj.Diagram(lab) @@ -140,6 +144,7 @@ # + [`miniscope`](https://github.com/datajoint/element-miniscope): miniscope raw recording and processed data + # + [probe and ephys](https://github.com/datajoint/element-array-ephys): Neuropixel based probe and ephys tables dj.Diagram(miniscope) # - diff --git a/notebooks/py_scripts/03-process.py b/notebooks/py_scripts/03-process.py index dced950..5727db1 100644 --- a/notebooks/py_scripts/03-process.py +++ b/notebooks/py_scripts/03-process.py @@ -8,9 +8,9 @@ # format_version: '1.5' # jupytext_version: 1.13.7 # kernelspec: -# display_name: 'Python 3.7.9 64-bit (''workflow-calcium-imaging'': conda)' +# display_name: venv-nwb # language: python -# name: python3 +# name: venv-nwb # --- # # Interactively run miniscope workflow @@ -35,7 +35,9 @@ # # + This script `activates` the DataJoint `Elements` and declares other required tables. -from workflow_miniscope.pipeline import * +import datajoint as dj +from workflow_miniscope.pipeline import subject, session, miniscope, Equipment, \ + AnatomicalLocation from element_interface.utils import find_full_path # ## Schema diagrams @@ -58,7 +60,9 @@ # ## Insert an entry into `lab.Equipment` -Equipment.insert1(dict(acquisition_hardware='UCLA Miniscope')) +Equipment.insert1(dict(equipment='UCLA Miniscope', + modality='Miniscope', + description='V4, >1mm field of view, 1mm working distance')) # ## Insert an entry into `session.Session` @@ -101,7 +105,7 @@ recording_id=0) miniscope.Recording.insert1(dict(**recording_key, - acquisition_hardware='UCLA Miniscope', + equipment='UCLA Miniscope', acquisition_software='Miniscope-DAQ-V4', recording_directory='subject1/session1', recording_notes='No notes for this session.')) @@ -193,7 +197,7 @@ miniscope.ProcessingTask.insert1(dict(**recording_key, paramset_id=0, processing_output_dir='subject1/session1/caiman', - task_mode='trigger')) + task_mode='load')) # ## Populate `miniscope.Processing` diff --git a/workflow_miniscope/pipeline.py b/workflow_miniscope/pipeline.py index 1b974db..10d825c 100644 --- a/workflow_miniscope/pipeline.py +++ b/workflow_miniscope/pipeline.py @@ -32,10 +32,10 @@ @lab.schema class Equipment(dj.Manual): definition = """ - equipment: varchar(32) + equipment : varchar(32) --- - modality: varchar(256) - description: varchar(256) + modality : varchar(64) + description=null : varchar(256) """ @lab.schema From 44d807201e77e3604e16147242cf88f52ce7ec04 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 28 Apr 2022 13:57:26 -0500 Subject: [PATCH 4/8] WIP trialization nb 03, 05, 07. Errs on each --- notebooks/03-process.ipynb | 4 +- notebooks/05-explore.ipynb | 2217 +++++++++++++ notebooks/06-drop-optional.ipynb | 9 +- .../07-downstream-analysis-optional.ipynb | 2937 +++++++++++++++++ notebooks/py_scripts/03-process.py | 6 +- notebooks/py_scripts/05-explore.py | 200 ++ notebooks/py_scripts/06-drop-optional.py | 5 +- .../07-downstream-analysis-optional.py | 208 ++ user_data/behavior_recordings.csv | 4 +- user_data/blocks.csv | 4 +- user_data/events.csv | 154 +- user_data/subjects.csv | 2 +- user_data/trials.csv | 100 +- workflow_miniscope/analysis.py | 8 +- workflow_miniscope/ingest.py | 9 +- workflow_miniscope/pipeline.py | 4 + 16 files changed, 5723 insertions(+), 148 deletions(-) create mode 100644 notebooks/05-explore.ipynb create mode 100644 notebooks/07-downstream-analysis-optional.ipynb create mode 100644 notebooks/py_scripts/05-explore.py create mode 100644 notebooks/py_scripts/07-downstream-analysis-optional.py diff --git a/notebooks/03-process.ipynb b/notebooks/03-process.ipynb index 3f250bc..3e61308 100644 --- a/notebooks/03-process.ipynb +++ b/notebooks/03-process.ipynb @@ -589,14 +589,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "MotionCorrection: 100%|█████████████████████████| 1/1 [00:04<00:00, 4.01s/it]\n" + "MotionCorrection: 0it [00:00, ?it/s]\n" ] } ], diff --git a/notebooks/05-explore.ipynb b/notebooks/05-explore.ipynb new file mode 100644 index 0000000..3d81127 --- /dev/null +++ b/notebooks/05-explore.ipynb @@ -0,0 +1,2217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore Element Miniscope\n", + "\n", + "+ This notebook will describe the steps for interacting with the data ingested into `workflow-miniscope`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@dss-db.datajoint.io:3306\n" + ] + } + ], + "source": [ + "import os\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "import datajoint as dj\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from workflow_miniscope.pipeline import lab, subject, session, miniscope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workflow architecture\n", + "\n", + "This workflow is assembled from 4 DataJoint elements:\n", + "+ [element-lab](https://github.com/datajoint/element-lab)\n", + "+ [element-animal](https://github.com/datajoint/element-animal)\n", + "+ [element-session](https://github.com/datajoint/element-session)\n", + "+ [element-miniscope](https://github.com/datajoint/element-miniscope)\n", + "\n", + "For the architecture and detailed descriptions for each of those elements, please visit the respective links. \n", + "\n", + "Below is the diagram describing the core components of the fully assembled pipeline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dj.Diagram(miniscope) + (dj.Diagram(session.Session) + 1) - 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Browsing the data with DataJoint `query` and `fetch` \n", + "\n", + "+ DataJoint provides functions to query data and fetch. For a detailed tutorials, visit our [general tutorial site](https://playground.datajoint.io/).\n", + "\n", + "+ Running through the pipeline, we have ingested data of subject3 into the database.\n", + "\n", + "+ Here are some highlights of the important tables.\n", + "\n", + "### `subject.Subject` and `session.Session` tables" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Animal Subject\n", + "
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outlier_frames

\n", + " mask with true for frames with outlier shifts\n", + "
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y_shifts

\n", + " (pixels) y motion correction shifts\n", + "
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x_shifts

\n", + " (pixels) x motion correction shifts\n", + "
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y_std

\n", + " (pixels) standard deviation of\n", + "
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x_std

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subject12021-01-01 00:00:01000=BLOB==BLOB==BLOB=0.05619640.0570838
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id outlier_fr y_shifts x_shifts y_std x_std \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +-----------+ +-----------+\n", + "subject1 2021-01-01 00: 0 0 0 =BLOB= =BLOB= =BLOB= 0.0561964 0.0570838 \n", + " (Total: 1)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.MotionCorrection.RigidMotionCorrection & curation_key" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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curation_id

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outlier_frames

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block_height

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block_width

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block_count_y

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Total: 0

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id outlier_fr block_height block_width block_count_y block_count_x \n", + "+---------+ +------------+ +------------+ +------------+ +------------+ +--------+ +------------+ +------------+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.MotionCorrection.NonRigidMotionCorrection & curation_key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ For non-rigid motion correction, the details for the individual blocks are stored in `miniscope.MotionCorrection.Block`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " FOV-tiled blocks used for non-rigid motion correction\n", + "
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subject

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paramset_id

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curation_id

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block_id

\n", + " \n", + "
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block_y

\n", + " (y_start, y_end) in pixel of this block\n", + "
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block_x

\n", + " (x_start, x_end) in pixel of this block\n", + "
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y_shifts

\n", + " (pixels) y motion correction shifts for\n", + "
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x_shifts

\n", + " (pixels) x motion correction shifts for\n", + "
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y_std

\n", + " (pixels) standard deviation of y shifts\n", + "
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x_std

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Total: 0

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *block_id block_y block_x y_shifts x_shifts y_std x_std \n", + "+---------+ +------------+ +------------+ +------------+ +------------+ +----------+ +--------+ +--------+ +--------+ +--------+ +-------+ +-------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.MotionCorrection.Block & curation_key & 'block_id=0'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ Summary images are stored in `miniscope.MotionCorrection.Summary`\n", + "\n", + " + Reference image - image used as an alignment template\n", + "\n", + " + Average image - mean of registered frames\n", + "\n", + " + Correlation image - correlation map (computed during region of interest \\[ROI\\] detection)\n", + "\n", + " + Maximum projection image - max of registered frames" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " summary images for each field and channel after corrections\n", + "
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paramset_id

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curation_id

\n", + " \n", + "
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ref_image

\n", + " image used as alignment template\n", + "
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average_image

\n", + " mean of registered frames\n", + "
\n", + "

correlation_image

\n", + " correlation map\n", + "
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max_proj_image

\n", + " max of registered frames\n", + "
subject12021-01-01 00:00:01000=BLOB==BLOB==BLOB==BLOB=
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id ref_image average_im correlatio max_proj_i\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", + "subject1 2021-01-01 00: 0 0 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.MotionCorrection.Summary & curation_key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "+ Lets fetch the `average_image` and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "average_image = (miniscope.MotionCorrection.Summary & curation_key\n", + " ).fetch1('average_image')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Invalid shape (1, 600, 600) for image data", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_27306/2976288256.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maverage_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\u001b[0m name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 456\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m 2638\u001b[0m \u001b[0minterpolation_stage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2639\u001b[0m resample=None, url=None, data=None, **kwargs):\n\u001b[0;32m-> 2640\u001b[0;31m __ret = gca().imshow(\n\u001b[0m\u001b[1;32m 2641\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2642\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\u001b[0m name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 456\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1410\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1411\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1414\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m 5440\u001b[0m **kwargs)\n\u001b[1;32m 5441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5442\u001b[0;31m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5443\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5444\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m 713\u001b[0m if not (self._A.ndim == 2\n\u001b[1;32m 714\u001b[0m or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[0;32m--> 715\u001b[0;31m raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0m\u001b[1;32m 716\u001b[0m .format(self._A.shape))\n\u001b[1;32m 717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Invalid shape (1, 600, 600) for image data" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(average_image);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `miniscope.Segmentation` table\n", + "\n", + "+ Lets fetch and plot a mask stored in the `miniscope.Segmentation.Mask` table for one `curation_id`.\n", + "\n", + "+ Each mask can be associated with a field by the attribute `mask_center_z`. For example, masks with `mask_center_z=0` are in the field identified with `field_idx=0` in `miniscope.ScanInfo.Field`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_xpix, mask_ypix = (miniscope.Segmentation.Mask \n", + " * miniscope.MaskClassification.MaskType \n", + " & curation_key & 'mask_center_z=0' & 'mask_npix > 130'\n", + " ).fetch('mask_xpix','mask_ypix')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_image = np.zeros(np.shape(average_image), dtype=bool)\n", + "for xpix, ypix in zip(mask_xpix, mask_ypix):\n", + " mask_image[ypix, xpix] = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(average_image);\n", + "plt.contour(mask_image, colors='white', linewidths=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `miniscope.MaskClassification` table\n", + "\n", + "+ This table provides the `mask_type` and `confidence` for the mask classification." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-01-01 00:00:01000caiman_default_classifier13somanan
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *mask_classifi *mask_id mask_type confidence \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +---------+ +-----------+ +------------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_default 13 soma nan \n", + " (Total: 1)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.MaskClassification.MaskType & curation_key & 'mask_id=13'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `miniscope.Fluorescence` and `miniscope.Activity` tables\n", + "\n", + "+ Lets fetch and plot the flourescence and activity traces for one mask." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "query_cells = (miniscope.Segmentation.Mask \n", + " * miniscope.MaskClassification.MaskType \n", + " & curation_key \n", + " # & 'mask_center_x=0' \n", + " & 'mask_npix > 130'\n", + " ).proj()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-01-01 00:00:0100013caiman_default_classifier
subject12021-01-01 00:00:0100015caiman_default_classifier
subject12021-01-01 00:00:0100017caiman_default_classifier
subject12021-01-01 00:00:0100020caiman_default_classifier
subject12021-01-01 00:00:0100021caiman_default_classifier
subject12021-01-01 00:00:0100022caiman_default_classifier
subject12021-01-01 00:00:0100025caiman_default_classifier
subject12021-01-01 00:00:0100026caiman_default_classifier
subject12021-01-01 00:00:0100032caiman_default_classifier
subject12021-01-01 00:00:0100036caiman_default_classifier
subject12021-01-01 00:00:0100037caiman_default_classifier
subject12021-01-01 00:00:0100039caiman_default_classifier
subject12021-01-01 00:00:0100040caiman_default_classifier
subject12021-01-01 00:00:0100059caiman_default_classifier
subject12021-01-01 00:00:0100083caiman_default_classifier
subject12021-01-01 00:00:0100085caiman_default_classifier
subject12021-01-01 00:00:0100086caiman_default_classifier
subject12021-01-01 00:00:0100090caiman_default_classifier
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Total: 18

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *mask_id *mask_classifi\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+\n", + "subject1 2021-01-01 00: 0 0 0 13 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 15 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 17 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 20 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 21 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 22 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 25 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 26 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 32 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 36 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 37 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 39 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 40 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 59 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 83 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 85 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 86 caiman_default\n", + "subject1 2021-01-01 00: 0 0 0 90 caiman_default\n", + " (Total: 18)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query_cells" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "fluorescence_traces = (miniscope.Fluorescence.Trace & query_cells\n", + " ).fetch('fluorescence', order_by='mask_id')\n", + "\n", + "activity_traces = (miniscope.Activity.Trace & query_cells\n", + " ).fetch('activity_trace', order_by='mask_id')\n", + "\n", + "sampling_rate = (miniscope.RecordingInfo & curation_key).fetch1('fps') # [Hz]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(16, 4))\n", + "ax2 = ax.twinx()\n", + "\n", + "for f, a in zip(fluorescence_traces, activity_traces):\n", + " ax.plot(np.r_[:f.size] * 1/sampling_rate, f, 'k', label='fluorescence trace') \n", + " ax2.plot(np.r_[:a.size] * 1/sampling_rate, a, 'r', alpha=0.5, label='deconvolved trace')\n", + " \n", + " break\n", + "\n", + "ax.tick_params(labelsize=14)\n", + "ax2.tick_params(labelsize=14)\n", + "\n", + "ax.legend(loc='upper left', prop={'size': 14})\n", + "ax2.legend(loc='upper right', prop={'size': 14})\n", + "\n", + "ax.set_xlabel('Time (s)')\n", + "ax.set_ylabel('Activity (a.u.)')\n", + "ax2.set_ylabel('Activity (a.u.)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary and Next Step\n", + "\n", + "+ This notebook highlights the major tables in the workflow and visualize some of the ingested results. \n", + "\n", + "+ The next notebook [06-drop](06-drop-optional.ipynb) shows how to drop schemas and tables if needed." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + }, + "metadata": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/06-drop-optional.ipynb b/notebooks/06-drop-optional.ipynb index b705fc3..0f5ab45 100644 --- a/notebooks/06-drop-optional.ipynb +++ b/notebooks/06-drop-optional.ipynb @@ -67,8 +67,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3.7.9 64-bit ('workflow-calcium-imaging': conda)", - "name": "python379jvsc74a57bd01a512f474e195e32ad84236879d3bb44800a92b431919ef0b10d543f5012a23c" + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" }, "language_info": { "codemirror_mode": { @@ -80,9 +81,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.11" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/07-downstream-analysis-optional.ipynb b/notebooks/07-downstream-analysis-optional.ipynb new file mode 100644 index 0000000..feab5e9 --- /dev/null +++ b/notebooks/07-downstream-analysis-optional.ipynb @@ -0,0 +1,2937 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d7e0268d-2d43-4f2e-8852-bb47391d0852", + "metadata": { + "tags": [] + }, + "source": [ + "# DataJoint U24 - Workflow Miniscope" + ] + }, + { + "cell_type": "markdown", + "id": "f59b4b0b-a0a0-4946-8b0a-9160965e5516", + "metadata": { + "tags": [] + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "id": "3bd39998-9678-4c09-ab85-494074e5680c", + "metadata": {}, + "source": [ + "First, let's change directories to find the `dj_local_conf` file." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "436715bc-c039-4d16-80b1-a2d18ee03c52", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd())=='notebooks': os.chdir('..')\n", + "assert os.path.basename(os.getcwd())=='workflow-miniscope', (\n", + " \"Please move to the workflow directory\")\n", + "# We'll be working with long tables, so we'll make visualization easier with a limit\n", + "import datajoint as dj; dj.config['display.limit']=10" + ] + }, + { + "cell_type": "markdown", + "id": "30b79178-1bdf-4c45-81c0-98cc85b65b69", + "metadata": {}, + "source": [ + "Next, we populate the python namespace with the required schemas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "79cef246", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connecting cbroz@dss-db.datajoint.io:3306\n" + ] + } + ], + "source": [ + "from workflow_miniscope.pipeline import session, miniscope, trial, event" + ] + }, + { + "cell_type": "markdown", + "id": "1aef115b-e711-4614-a555-0319fff8ca33", + "metadata": { + "incorrectly_encoded_metadata": "jp-MarkdownHeadingCollapsed=true", + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Trial and Event schemas" + ] + }, + { + "cell_type": "markdown", + "id": "9603fc4f-b70f-42da-8ef3-cf4477fec5d0", + "metadata": {}, + "source": [ + "Tables in the `trial` and `event` schemas specify the structure of your experiment, including block, trial and event timing. \n", + "- Session has a 1-to-1 mapping with a behavior recording\n", + "- A block is a continuous phase of an experiment that contains repeated instances of a condition, or trials. \n", + "- Events may occur within or outside of conditions, either instantaneous or continuous.\n", + "\n", + "The diagram below shows (a) the levels of hierarchy and (b) how the bounds may not completely overlap. A block may not fully capure trials and events may occur outside both blocks/trials." + ] + }, + { + "cell_type": "markdown", + "id": "e9dddcdf-df71-4b67-8b0c-7ef7c95c32bb", + "metadata": {}, + "source": [ + "```\n", + "|----------------------------------------------------------------------------|\n", + "|-------------------------------- Session ---------------------------------|__\n", + "|-------------------------- BehaviorRecording ---------------------------|____\n", + "|----- Block 1 -----|______|----- Block 2 -----|______|----- Block 3 -----|___\n", + "| trial 1 || trial 2 |____| trial 3 || trial 4 |____| trial 5 |____| trial 6 |\n", + "|_|e1|_|e2||e3|_|e4|__|e5|__|e6||e7||e8||e9||e10||e11|____|e12||e13|_________|\n", + "|----------------------------------------------------------------------------|\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a63a23cc-ff15-4e24-8cf9-bd5c5e0c5d5f", + "metadata": {}, + "source": [ + "Let's load some example data. The `ingest.py` script has a series of loaders to help. If you've already run the other notebooks, you might skip `ingest_subjects` and `ingest_sessions`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "00584a67-524a-4002-ad0f-683f2b53c5b0", + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_miniscope.ingest import ingest_subjects, ingest_sessions,\\\n", + " ingest_events, ingest_alignment" + ] + }, + { + "cell_type": "markdown", + "id": "e4b1d090-45b1-4fbf-a558-846d36bbb98a", + "metadata": {}, + "source": [ + "If you've already run previous notebooks, no need to ingest subjects or sessions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5b6eaecd-a823-4649-9b81-63f8f2b4dc21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---- Inserting 0 entry(s) into behavior_recording ----\n", + "\n", + "---- Inserting 0 entry(s) into behavior_recording__file ----\n", + "\n", + "---- Inserting 2 entry(s) into _block ----\n", + "\n", + "---- Inserting 2 entry(s) into _block__attribute ----\n", + "\n", + "---- Inserting 0 entry(s) into #trial_type ----\n", + "\n", + "---- Inserting 50 entry(s) into _trial ----\n", + "\n", + "---- Inserting 50 entry(s) into _trial__attribute ----\n", + "\n", + "---- Inserting 50 entry(s) into _block_trial ----\n", + "\n", + "---- Inserting 0 entry(s) into #event_type ----\n", + "\n", + "---- Inserting 77 entry(s) into _event ----\n", + "\n", + "---- Inserting 77 entry(s) into _trial_event ----\n" + ] + } + ], + "source": [ + "# ingest_subjects(); ingest_sessions()\n", + "ingest_events()" + ] + }, + { + "cell_type": "markdown", + "id": "9b512789-a9d8-4b1a-af50-45eec09ab47a", + "metadata": {}, + "source": [ + "We have 50 total trials, either 'stim' or 'ctrl', with start and stop time" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4cdf4879-cd05-43c3-89fa-cacc1b1474a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Experimental trials\n", + "
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subject12021-01-01 00:00:015stim4.8265.69
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subject12021-01-01 00:00:017ctrl7.2528.116
subject12021-01-01 00:00:018ctrl8.4629.326
subject12021-01-01 00:00:019ctrl9.73110.595
subject12021-01-01 00:00:0110stim11.01911.883
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...

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Total: 50

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *trial_id trial_type trial_start_ti trial_stop_tim\n", + "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+\n", + "subject1 2021-01-01 00: 1 stim 0.193 1.057 \n", + "subject1 2021-01-01 00: 2 ctrl 1.468 2.332 \n", + "subject1 2021-01-01 00: 3 ctrl 2.662 3.526 \n", + "subject1 2021-01-01 00: 4 ctrl 3.738 4.602 \n", + "subject1 2021-01-01 00: 5 stim 4.826 5.69 \n", + "subject1 2021-01-01 00: 6 stim 5.973 6.837 \n", + "subject1 2021-01-01 00: 7 ctrl 7.252 8.116 \n", + "subject1 2021-01-01 00: 8 ctrl 8.462 9.326 \n", + "subject1 2021-01-01 00: 9 ctrl 9.731 10.595 \n", + "subject1 2021-01-01 00: 10 stim 11.019 11.883 \n", + " ...\n", + " (Total: 50)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trial.Trial() & \"subject='subject1'\"" + ] + }, + { + "cell_type": "markdown", + "id": "e1b7fd18-ef4b-4323-bd59-29c48f38ad3c", + "metadata": {}, + "source": [ + "Each trial is paired with one or more events that take place during the trial window." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7fe42898-3ff9-4394-a811-a4cb85320f04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-01-01 00:00:011left0.407
subject12021-01-01 00:00:011right0.269
subject12021-01-01 00:00:012center1.611
subject12021-01-01 00:00:012center1.649
subject12021-01-01 00:00:013left2.777
subject12021-01-01 00:00:013left2.935
subject12021-01-01 00:00:014right4.006
\n", + " \n", + "

Total: 7

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *trial_id *event_type *event_start_t\n", + "+----------+ +------------+ +----------+ +------------+ +------------+\n", + "subject1 2021-01-01 00: 1 left 0.407 \n", + "subject1 2021-01-01 00: 1 right 0.269 \n", + "subject1 2021-01-01 00: 2 center 1.611 \n", + "subject1 2021-01-01 00: 2 center 1.649 \n", + "subject1 2021-01-01 00: 3 left 2.777 \n", + "subject1 2021-01-01 00: 3 left 2.935 \n", + "subject1 2021-01-01 00: 4 right 4.006 \n", + " (Total: 7)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trial.TrialEvent() & 'trial_id<5' & \"subject='subject1'\"" + ] + }, + { + "cell_type": "markdown", + "id": "4ee25da8-b5ce-4a9b-b1f9-eba06ac09136", + "metadata": {}, + "source": [ + "Finally, the `AlignmentEvent` describes the event of interest and the window we'd like to see around it." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5122d831-48b2-4214-bd43-a42d67a5dc2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "---- Inserting 3 entry(s) into alignment_event ----\n" + ] + } + ], + "source": [ + "ingest_alignment()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "50a2c99f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " time_shift is seconds to shift with respect to (WRT) a variable\n", + "
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center_buttoncenter0.0center-5.0center5.0
left_buttonleft0.0left-5.0left5.0
right_buttonright0.0right-5.0right5.0
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Total: 3

\n", + " " + ], + "text/plain": [ + "*alignment_nam alignment_desc alignment_even alignment_time start_event_ty start_time_shi end_event_type end_time_shift\n", + "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "center_button center 0.0 center -5.0 center 5.0 \n", + "left_button left 0.0 left -5.0 left 5.0 \n", + "right_button right 0.0 right -5.0 right 5.0 \n", + " (Total: 3)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "event.AlignmentEvent()" + ] + }, + { + "cell_type": "markdown", + "id": "4936a1e8", + "metadata": { + "tags": [] + }, + "source": [ + "# Event-aligned trialized calcium activity" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0678d202", + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_miniscope import analysis" + ] + }, + { + "cell_type": "markdown", + "id": "f4a64347-803f-4fab-a937-e75e5cf03eac", + "metadata": { + "incorrectly_encoded_metadata": "jp-MarkdownHeadingCollapsed=true", + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "a2e97d75", + "metadata": {}, + "source": [ + "The `analysis` schema provides example tables to perform event-aligned Calcium activity analysis.\n", + "+ ***ActivityAlignmentCondition*** - a manual table to specify the inputs and condition for the analysis\n", + "+ ***ActivityAlignment*** - a computed table to extract event-aligned Calcium activity (e.g. dF/F, spikes)" + ] + }, + { + "cell_type": "markdown", + "id": "ba4607a1", + "metadata": {}, + "source": [ + "Let's start by creating several analyses configuration - i.e. inserting into ***ActivityAlignmentCondition***" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9a36c342", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " inferred neural activity from fluorescence trace - e.g. dff, spikes\n", + "
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subject12021-01-01 00:00:01000caiman_deconvolution
subject12021-01-01 00:00:01000caiman_dff
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Total: 2

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_deconvo\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff \n", + " (Total: 2)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.Activity()" + ] + }, + { + "cell_type": "markdown", + "id": "177809bb-2c9a-44dc-8deb-63106a22b7b4", + "metadata": {}, + "source": [ + "We'll isolate the scan of interest with the following key:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8642f010", + "metadata": {}, + "outputs": [], + "source": [ + "activity_key = (miniscope.Activity & {'subject': 'subject1',\n", + " 'extraction_method': 'caiman_dff'}\n", + " ).fetch1('KEY')" + ] + }, + { + "cell_type": "markdown", + "id": "f69aa462-564b-4144-9ab8-3f391bff2c80", + "metadata": {}, + "source": [ + "Here, we can see all trials for this scan:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c9c95806", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Experimental trials\n", + "
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subject12021-01-01 00:00:011stim0.1931.057
subject12021-01-01 00:00:012ctrl1.4682.332
subject12021-01-01 00:00:013ctrl2.6623.526
subject12021-01-01 00:00:014ctrl3.7384.602
subject12021-01-01 00:00:015stim4.8265.69
subject12021-01-01 00:00:016stim5.9736.837
subject12021-01-01 00:00:017ctrl7.2528.116
subject12021-01-01 00:00:018ctrl8.4629.326
subject12021-01-01 00:00:019ctrl9.73110.595
subject12021-01-01 00:00:0110stim11.01911.883
\n", + "

...

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Total: 50

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *trial_id trial_type trial_start_ti trial_stop_tim\n", + "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+\n", + "subject1 2021-01-01 00: 1 stim 0.193 1.057 \n", + "subject1 2021-01-01 00: 2 ctrl 1.468 2.332 \n", + "subject1 2021-01-01 00: 3 ctrl 2.662 3.526 \n", + "subject1 2021-01-01 00: 4 ctrl 3.738 4.602 \n", + "subject1 2021-01-01 00: 5 stim 4.826 5.69 \n", + "subject1 2021-01-01 00: 6 stim 5.973 6.837 \n", + "subject1 2021-01-01 00: 7 ctrl 7.252 8.116 \n", + "subject1 2021-01-01 00: 8 ctrl 8.462 9.326 \n", + "subject1 2021-01-01 00: 9 ctrl 9.731 10.595 \n", + "subject1 2021-01-01 00: 10 stim 11.019 11.883 \n", + " ...\n", + " (Total: 50)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trial.Trial & ca_activity_key" + ] + }, + { + "cell_type": "markdown", + "id": "699c695b-6c71-4743-a352-f01adc2276f5", + "metadata": {}, + "source": [ + "And highlight a subset based on `trial_type`" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1851ad2b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Experimental trials\n", + "
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subject12021-01-01 00:00:012ctrl1.4682.332
subject12021-01-01 00:00:013ctrl2.6623.526
subject12021-01-01 00:00:014ctrl3.7384.602
subject12021-01-01 00:00:017ctrl7.2528.116
subject12021-01-01 00:00:018ctrl8.4629.326
subject12021-01-01 00:00:019ctrl9.73110.595
subject12021-01-01 00:00:0113ctrl14.42715.291
subject12021-01-01 00:00:0114ctrl15.56316.427
subject12021-01-01 00:00:0115ctrl16.71517.579
subject12021-01-01 00:00:0116ctrl17.70618.57
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Total: 23

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *trial_id trial_type trial_start_ti trial_stop_tim\n", + "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+\n", + "subject1 2021-01-01 00: 2 ctrl 1.468 2.332 \n", + "subject1 2021-01-01 00: 3 ctrl 2.662 3.526 \n", + "subject1 2021-01-01 00: 4 ctrl 3.738 4.602 \n", + "subject1 2021-01-01 00: 7 ctrl 7.252 8.116 \n", + "subject1 2021-01-01 00: 8 ctrl 8.462 9.326 \n", + "subject1 2021-01-01 00: 9 ctrl 9.731 10.595 \n", + "subject1 2021-01-01 00: 13 ctrl 14.427 15.291 \n", + "subject1 2021-01-01 00: 14 ctrl 15.563 16.427 \n", + "subject1 2021-01-01 00: 15 ctrl 16.715 17.579 \n", + "subject1 2021-01-01 00: 16 ctrl 17.706 18.57 \n", + " ...\n", + " (Total: 23)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ctrl_trials = trial.Trial & ca_activity_key & 'trial_type = \"ctrl\"'\n", + "ctrl_trials" + ] + }, + { + "cell_type": "markdown", + "id": "f65a298b-7bba-411a-9827-26bf3e2a19c6", + "metadata": {}, + "source": [ + "Here, we target the event of interest with another key:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a8714cf0-86ff-4053-b17a-cf8784195b18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'alignment_name': 'center_button'}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alignment_key = (event.AlignmentEvent & 'alignment_name = \"center_button\"'\n", + " ).fetch1('KEY')\n", + "alignment_key" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dc1d3449-ea2a-430d-84b1-c45c9774e9d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject1',\n", + " 'session_datetime': datetime.datetime(2021, 1, 1, 0, 0, 1),\n", + " 'recording_id': 0,\n", + " 'paramset_id': 0,\n", + " 'curation_id': 0,\n", + " 'extraction_method': 'caiman_dff',\n", + " 'alignment_name': 'center_button',\n", + " 'trial_condition': 'ctrl_center_button'}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alignment_condition = {**ca_activity_key, **alignment_key, \n", + " 'trial_condition': 'ctrl_center_button'}\n", + "alignment_condition" + ] + }, + { + "cell_type": "markdown", + "id": "f81c8ef8-89a0-4147-9cbb-9bdb4527e769", + "metadata": {}, + "source": [ + "Next, we add this to the `ActivityAlignment` table in the `analysis` schema" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8bc824cb", + "metadata": {}, + "outputs": [], + "source": [ + "analysis.ActivityAlignmentCondition.insert1(alignment_condition, skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "946902da-60ff-42ed-99aa-b55bb7c16e53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button0.04
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me *alignment_nam *trial_conditi condition_desc bin_size \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +----------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 0.04 \n", + " (Total: 1)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analysis.ActivityAlignmentCondition()" + ] + }, + { + "cell_type": "markdown", + "id": "0754c8ea-a1f8-4eba-aad2-7a3c2b837940", + "metadata": {}, + "source": [ + "Using the [projection](https://docs.datajoint.org/python/v0.13/queries/08-Proj.html) method, we can generate a table of relevant trials by `trial_type` and `alignment_condition`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4b263a8f-3bdb-4cb2-9dd1-95cb754c7e63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button2
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button3
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button4
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button7
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button8
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button9
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button13
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button14
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button15
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button16
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Total: 23

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me *alignment_nam *trial_conditi *trial_id \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +----------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 2 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 3 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 4 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 7 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 8 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 9 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 13 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 14 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 15 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 16 \n", + " ...\n", + " (Total: 23)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample = (analysis.ActivityAlignmentCondition * ctrl_trials \n", + " & alignment_condition).proj()\n", + "sample" + ] + }, + { + "cell_type": "markdown", + "id": "4915c554-a79d-4e3a-8cdc-23d47acf981e", + "metadata": {}, + "source": [ + "And insert these trials into the `ActivityAlignmentCondition.Trial` part table" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8994e8f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Trials (or subset) to compute event-aligned activity\n", + "
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subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button2
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button3
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button4
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button7
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button8
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button9
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button13
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button14
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button15
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button16
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Total: 23

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me *alignment_nam *trial_conditi *trial_id \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +----------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 2 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 3 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 4 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 7 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 8 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 9 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 13 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 14 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 15 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 16 \n", + " ...\n", + " (Total: 23)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analysis.ActivityAlignmentCondition.Trial.insert(sample, skip_duplicates=True)\n", + "analysis.ActivityAlignmentCondition.Trial()" + ] + }, + { + "cell_type": "markdown", + "id": "4ddd0345", + "metadata": {}, + "source": [ + "With the steps above, we have create a new alignment condition for analysis, named `ctrl_center_button`, which specifies:\n", + "+ an Activity of interest for analysis\n", + "+ an event of interest to align the Ca+ activity to - `center_button`\n", + "+ a set of trials of interest to perform the analysis on - `ctrl` trials\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "2a23b206", + "metadata": {}, + "source": [ + "Now, let's create another set with:\n", + "+ the same Activity of interest for analysis\n", + "+ an event of interest to align the Ca+ activity to - `center_button`\n", + "+ a set of trials of interest to perform the analysis on - `stim` trials" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "39d4d423", + "metadata": {}, + "outputs": [], + "source": [ + "stim_trials = trial.Trial & ca_activity_key & 'trial_type = \"stim\"'\n", + "alignment_condition = {**ca_activity_key, **alignment_key, \n", + " 'trial_condition': 'stim_center_button'}\n", + "analysis.ActivityAlignmentCondition.insert1(alignment_condition, skip_duplicates=True)\n", + "analysis.ActivityAlignmentCondition.Trial.insert(\n", + " (analysis.ActivityAlignmentCondition * stim_trials & alignment_condition).proj(),\n", + " skip_duplicates=True)" + ] + }, + { + "cell_type": "markdown", + "id": "00cbff81-346d-43a2-b638-5858d19b5506", + "metadata": {}, + "source": [ + "Note the two entries in `ActivityAlignmentCondition.trial_condition`" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "6452c508", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button0.04
subject12021-01-01 00:00:01000caiman_dffcenter_buttonstim_center_button0.04
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\n", + " user-friendly name of condition\n", + "
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trial_id

\n", + " trial number (1-based indexing)\n", + "
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button2
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button3
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button4
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button7
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button8
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button9
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button13
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button14
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button15
subject12021-01-01 00:00:01000caiman_dffcenter_buttonctrl_center_button16
\n", + "

...

\n", + "

Total: 23

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me *alignment_nam *trial_conditi *trial_id \n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +----------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 2 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 3 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 4 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 7 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 8 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 9 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 13 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 14 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 15 \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 16 \n", + " ...\n", + " (Total: 23)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analysis.ActivityAlignmentCondition.Trial & 'trial_condition = \"ctrl_center_button\"'" + ] + }, + { + "cell_type": "markdown", + "id": "1486add8", + "metadata": { + "incorrectly_encoded_metadata": "jp-MarkdownHeadingCollapsed=true", + "tags": [] + }, + "source": [ + "### Computation\n", + "Just like the element itself, we can run computations with `populate()`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f467d3f7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ActivityAlignment: 0%| | 0/2 [00:04\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mActivityAlignment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisplay_progress\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m in \u001b[0;36mpopulate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdisplay_progress\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m ):\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_populate1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpopulate_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0merror_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m in \u001b[0;36m_populate1\u001b[0;34m(self, key, jobs, suppress_errors, return_exception_objects, make_kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_allow_insert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_kwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSystemExit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Volumes/GoogleDrive/My Drive/NWB/workflow-miniscope/workflow_miniscope/analysis.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mnsamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maligned_timestamps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m trace_keys, activity_traces = (miniscope.Activity.Trace & key\n\u001b[0m\u001b[1;32m 71\u001b[0m ).fetch('KEY', 'activity_trace', order_by='mask')\n\u001b[1;32m 72\u001b[0m \u001b[0mactivity_traces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivity_traces\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/fetch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, offset, limit, order_by, format, as_dict, squeeze, download_path, *attrs)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mattributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mattrs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mattributes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m ret = ret.fetch(\n\u001b[0m\u001b[1;32m 229\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/fetch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, offset, limit, order_by, format, as_dict, squeeze, download_path, *attrs)\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mreturn_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# fetch all attributes as a numpy.record_array or pandas.DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m cur = self._expression.cursor(\n\u001b[0m\u001b[1;32m 256\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder_by\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder_by\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 257\u001b[0m )\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/expression.py\u001b[0m in \u001b[0;36mcursor\u001b[0;34m(self, offset, limit, order_by, as_dict)\u001b[0m\n\u001b[1;32m 621\u001b[0m \u001b[0msql\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\" LIMIT %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" OFFSET %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcursor_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress_warnings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 338\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLostConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreconnect\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36m_execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtranslate_query_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 296\u001b[0m def query(\n", + "\u001b[0;31mUnknownAttributeError\u001b[0m: Unknown column 'mask' in 'order clause'" + ] + } + ], + "source": [ + "analysis.ActivityAlignment.populate(display_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c3a9d9ce-6e7c-4467-ae7e-96063b2fddf4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button=BLOB=
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Total: 2

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trial_id

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aligned_trace

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subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button004=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button005=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0015=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0019=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0022=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0023=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0033=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0036=BLOB=
subject32021-10-25 13:06:40001suite2p_deconvolutioncenter_buttonctrl_center_button0040=BLOB=
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *paramset_idx *curation_id *extraction_me *alignment_nam *trial_conditi *mask *fluo_channel *trial_id aligned_tr\n", + "+----------+ +------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------+ +------------+ +----------+ +--------+\n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 4 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 5 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 15 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 19 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 22 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 23 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 33 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 36 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 40 =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button stim_center_bu 0 0 1 =BLOB= \n", + " ...\n", + " (Total: 126084)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analysis.ActivityAlignment.AlignedTrialSpikes()" + ] + }, + { + "cell_type": "markdown", + "id": "c4320cdd", + "metadata": {}, + "source": [ + "### Visualization" + ] + }, + { + "cell_type": "markdown", + "id": "40d11d31-782c-432d-acf8-3273b533ed51", + "metadata": {}, + "source": [ + "With the `plot_aligned_activities` function, we can see the density of activity relative to our alignment event. For more information, see the corresponding docstring." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f24049f9-7eda-4d14-b420-5a33ce60f36b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method plot_aligned_activities in module workflow_calcium_imaging.analysis:\n", + "\n", + "plot_aligned_activities(key, roi, axs=None, title=None) method of workflow_calcium_imaging.analysis.ActivityAlignment instance\n", + " peri-stimulus time histogram (PSTH) for calcium imaging spikes\n", + " :param key: key of ActivityAlignment master table\n", + " :param roi: imaging segmentation mask\n", + " :param axs: optional definition of axes for plot.\n", + " Default is plt.subplots(2, 1, figsize=(12, 8))\n", + " :param title: Optional title label\n", + "\n" + ] + } + ], + "source": [ + "help(analysis.ActivityAlignment().plot_aligned_activities)" + ] + }, + { + "cell_type": "markdown", + "id": "7029b46f-5682-4548-b774-bd315a20b8ff", + "metadata": {}, + "source": [ + "For a refresher on the differences between masks, we can browse the `imaging.Segmentation.Mask` table." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "32934d3b-01a7-428e-bf59-dbc89131979a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " A mask produced by segmentation.\n", + "
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mask_npix

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mask_center_x

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mask_center_y

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mask_center_z

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mask_xpix

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mask_ypix

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mask_zpix

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subject32021-10-25 13:06:40001001333364800=BLOB==BLOB==BLOB==BLOB=
subject32021-10-25 13:06:40001101262833480=BLOB==BLOB==BLOB==BLOB=
subject32021-10-25 13:06:40001201575911900=BLOB==BLOB==BLOB==BLOB=
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *paramset_idx *curation_id *mask segmentation_c mask_npix mask_center_x mask_center_y mask_center_z mask_xpix mask_ypix mask_zpix mask_weigh\n", + "+----------+ +------------+ +---------+ +------------+ +------------+ +------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", + "subject3 2021-10-25 13: 0 0 1 0 0 133 336 480 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 1 0 126 283 348 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject3 2021-10-25 13: 0 0 1 2 0 157 591 190 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + " (Total: 3)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imaging.Segmentation.Mask & 'mask<3'" + ] + }, + { + "cell_type": "markdown", + "id": "532ed185-4696-4d1a-ba26-2499f8426c2d", + "metadata": {}, + "source": [ + "Then, we can directly compare the stimulus and control conditions relative to center button presses." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f5e985f1-8bdd-450a-b283-8e93ab635202", + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_calcium_imaging import analysis\n", + "from workflow_calcium_imaging.pipeline import session, imaging, trial, event\n", + "ca_activity_key = (imaging.Activity & {'subject': 'subject3', 'scan_id': 0}\n", + " ).fetch1('KEY')\n", + "alignment_key = (event.AlignmentEvent & 'alignment_name = \"center_button\"'\n", + " ).fetch1('KEY')\n", + "alignment_condition_ctrl = {**ca_activity_key, **alignment_key, \n", + " 'trial_condition': 'ctrl_center_button'}\n", + "alignment_condition_stim = {**ca_activity_key, **alignment_key, \n", + " 'trial_condition': 'stim_center_button'}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c006d977-5d66-458f-992a-99728a96bb06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "analysis.ActivityAlignment().plot_aligned_activities(alignment_condition_stim, roi=2,\n", + " title='Stimulus Center Button');\n", + "analysis.ActivityAlignment().plot_aligned_activities(alignment_condition_ctrl, roi=2,\n", + " title='Control Center Button');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c0f1691", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-nwb", + "language": "python", + "name": "venv-nwb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/py_scripts/03-process.py b/notebooks/py_scripts/03-process.py index 5727db1..7c8edf7 100644 --- a/notebooks/py_scripts/03-process.py +++ b/notebooks/py_scripts/03-process.py @@ -256,6 +256,8 @@ miniscope.Activity.populate(**populate_settings) -# ## Next steps +# + + diff --git a/notebooks/py_scripts/05-explore.py b/notebooks/py_scripts/05-explore.py new file mode 100644 index 0000000..25ae4c6 --- /dev/null +++ b/notebooks/py_scripts/05-explore.py @@ -0,0 +1,200 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-nwb +# language: python +# name: venv-nwb +# --- + +# # Explore Element Miniscope +# +# + This notebook will describe the steps for interacting with the data ingested into `workflow-miniscope`. + +# + +import os +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +import datajoint as dj +import matplotlib.pyplot as plt +import numpy as np + +from workflow_miniscope.pipeline import lab, subject, session, miniscope +# - + +# ## Workflow architecture +# +# This workflow is assembled from 4 DataJoint elements: +# + [element-lab](https://github.com/datajoint/element-lab) +# + [element-animal](https://github.com/datajoint/element-animal) +# + [element-session](https://github.com/datajoint/element-session) +# + [element-miniscope](https://github.com/datajoint/element-miniscope) +# +# For the architecture and detailed descriptions for each of those elements, please visit the respective links. +# +# Below is the diagram describing the core components of the fully assembled pipeline. +# + +dj.Diagram(miniscope) + (dj.Diagram(session.Session) + 1) - 1 + +# ## Browsing the data with DataJoint `query` and `fetch` +# +# + DataJoint provides functions to query data and fetch. For a detailed tutorials, visit our [general tutorial site](https://playground.datajoint.io/). +# +# + Running through the pipeline, we have ingested data of subject3 into the database. +# +# + Here are some highlights of the important tables. +# +# ### `subject.Subject` and `session.Session` tables + +subject.Subject() & "subject='subject1'" + +session.Session() & "subject='subject1'" + +# + Fetch the primary key for the session of interest which will be used later on in this notebook. + +session_key = (session.Session & 'subject = "subject1"').fetch1('KEY') + +# ### `miniscope.Scan` and `miniscope.ScanInfo` tables +# +# + These tables stores the scan metadata within a particular session. + +miniscope.RecordingInfo & session_key + +miniscope.RecordingInfo.File & session_key + +# ### Processing tables +# +# - `ProcessingMethod`: Analysis software +# - `ProcessingParamSet`: Parameters for analysis +# - `ProcessingTask`: Staging area for pairs of recordings and processing parameters, as either triggered or loaded +# - `Processing`: Computed table with a `make` function for loading or triggering analysis +# - `Curation`: supports multiple curations of an entry in `ProcessingTask` + +miniscope.ProcessingMethod() + +miniscope.ProcessingParamSet() + +miniscope.ProcessingTask * miniscope.Processing & session_key + +# In this example workflow, `curation_output_dir` is the same as the `processing_output_dir`, as these results were not manually curated. + +miniscope.Curation & session_key + +# ### `miniscope.MotionCorrection` table +# +# + After processing and curation, results are passed to the `miniscope.MotionCorrection` and `miniscope.Segmentation` tables. +# +# + For the example data, the raw data is corrected with rigid and non-rigid motion correction which is stored in `miniscope.MotionCorrection.RigidMotionCorrection` and `miniscope.MotionCorrection.NonRigidMotionCorrection`, respectively. +# +# + Lets first query the information for one curation. + +miniscope.Curation() + +curation_key = (miniscope.Curation & session_key & 'curation_id=0').fetch1('KEY') + +curation_key + +miniscope.MotionCorrection.RigidMotionCorrection & curation_key + +miniscope.MotionCorrection.NonRigidMotionCorrection & curation_key + +# + For non-rigid motion correction, the details for the individual blocks are stored in `miniscope.MotionCorrection.Block`. + +miniscope.MotionCorrection.Block & curation_key & 'block_id=0' + +# + Summary images are stored in `miniscope.MotionCorrection.Summary` +# +# + Reference image - image used as an alignment template +# +# + Average image - mean of registered frames +# +# + Correlation image - correlation map (computed during region of interest \[ROI\] detection) +# +# + Maximum projection image - max of registered frames + +miniscope.MotionCorrection.Summary & curation_key + +# + Lets fetch the `average_image` and plot it. + +average_image = (miniscope.MotionCorrection.Summary & curation_key + ).fetch1('average_image') + +plt.imshow(average_image); + +# ### `miniscope.Segmentation` table +# +# + Lets fetch and plot a mask stored in the `miniscope.Segmentation.Mask` table for one `curation_id`. +# +# + Each mask can be associated with a field by the attribute `mask_center_z`. For example, masks with `mask_center_z=0` are in the field identified with `field_idx=0` in `miniscope.ScanInfo.Field`. + +mask_xpix, mask_ypix = (miniscope.Segmentation.Mask + * miniscope.MaskClassification.MaskType + & curation_key & 'mask_center_z=0' & 'mask_npix > 130' + ).fetch('mask_xpix','mask_ypix') + +mask_image = np.zeros(np.shape(average_image), dtype=bool) +for xpix, ypix in zip(mask_xpix, mask_ypix): + mask_image[ypix, xpix] = True + +plt.imshow(average_image); +plt.contour(mask_image, colors='white', linewidths=0.5); + +# ### `miniscope.MaskClassification` table +# +# + This table provides the `mask_type` and `confidence` for the mask classification. + +miniscope.MaskClassification.MaskType & curation_key & 'mask_id=13' + +# ### `miniscope.Fluorescence` and `miniscope.Activity` tables +# +# + Lets fetch and plot the flourescence and activity traces for one mask. + +query_cells = (miniscope.Segmentation.Mask + * miniscope.MaskClassification.MaskType + & curation_key + # & 'mask_center_x=0' + & 'mask_npix > 130' + ).proj() + +query_cells + +# + +fluorescence_traces = (miniscope.Fluorescence.Trace & query_cells + ).fetch('fluorescence', order_by='mask_id') + +activity_traces = (miniscope.Activity.Trace & query_cells + ).fetch('activity_trace', order_by='mask_id') + +sampling_rate = (miniscope.RecordingInfo & curation_key).fetch1('fps') # [Hz] + +# + +fig, ax = plt.subplots(1, 1, figsize=(16, 4)) +ax2 = ax.twinx() + +for f, a in zip(fluorescence_traces, activity_traces): + ax.plot(np.r_[:f.size] * 1/sampling_rate, f, 'k', label='fluorescence trace') + ax2.plot(np.r_[:a.size] * 1/sampling_rate, a, 'r', alpha=0.5, label='deconvolved trace') + + break + +ax.tick_params(labelsize=14) +ax2.tick_params(labelsize=14) + +ax.legend(loc='upper left', prop={'size': 14}) +ax2.legend(loc='upper right', prop={'size': 14}) + +ax.set_xlabel('Time (s)') +ax.set_ylabel('Activity (a.u.)') +ax2.set_ylabel('Activity (a.u.)'); +# - + +# ## Summary and Next Step +# +# + This notebook highlights the major tables in the workflow and visualize some of the ingested results. +# +# + The next notebook [06-drop](06-drop-optional.ipynb) shows how to drop schemas and tables if needed. diff --git a/notebooks/py_scripts/06-drop-optional.py b/notebooks/py_scripts/06-drop-optional.py index 6e9e4ba..a10e103 100644 --- a/notebooks/py_scripts/06-drop-optional.py +++ b/notebooks/py_scripts/06-drop-optional.py @@ -8,8 +8,9 @@ # format_version: '1.5' # jupytext_version: 1.13.7 # kernelspec: -# display_name: 'Python 3.7.9 64-bit (''workflow-calcium-imaging'': conda)' -# name: python379jvsc74a57bd01a512f474e195e32ad84236879d3bb44800a92b431919ef0b10d543f5012a23c +# display_name: venv-nwb +# language: python +# name: venv-nwb # --- # # Drop schemas diff --git a/notebooks/py_scripts/07-downstream-analysis-optional.py b/notebooks/py_scripts/07-downstream-analysis-optional.py new file mode 100644 index 0000000..2c2b7cb --- /dev/null +++ b/notebooks/py_scripts/07-downstream-analysis-optional.py @@ -0,0 +1,208 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.13.7 +# kernelspec: +# display_name: venv-nwb +# language: python +# name: venv-nwb +# --- + +# + [markdown] tags=[] +# # DataJoint U24 - Workflow Miniscope + +# + [markdown] tags=[] +# ## Setup +# - + +# First, let's change directories to find the `dj_local_conf` file. + +import os +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd())=='notebooks': os.chdir('..') +assert os.path.basename(os.getcwd())=='workflow-miniscope', ( + "Please move to the workflow directory") +# We'll be working with long tables, so we'll make visualization easier with a limit +import datajoint as dj; dj.config['display.limit']=10 + +# Next, we populate the python namespace with the required schemas + +from workflow_miniscope.pipeline import session, miniscope, trial, event + +# + [markdown] jp-MarkdownHeadingCollapsed=true jp-MarkdownHeadingCollapsed=true tags=[] +# ## Trial and Event schemas +# - + +# Tables in the `trial` and `event` schemas specify the structure of your experiment, including block, trial and event timing. +# - Session has a 1-to-1 mapping with a behavior recording +# - A block is a continuous phase of an experiment that contains repeated instances of a condition, or trials. +# - Events may occur within or outside of conditions, either instantaneous or continuous. +# +# The diagram below shows (a) the levels of hierarchy and (b) how the bounds may not completely overlap. A block may not fully capure trials and events may occur outside both blocks/trials. + +# ``` +# |----------------------------------------------------------------------------| +# |-------------------------------- Session ---------------------------------|__ +# |-------------------------- BehaviorRecording ---------------------------|____ +# |----- Block 1 -----|______|----- Block 2 -----|______|----- Block 3 -----|___ +# | trial 1 || trial 2 |____| trial 3 || trial 4 |____| trial 5 |____| trial 6 | +# |_|e1|_|e2||e3|_|e4|__|e5|__|e6||e7||e8||e9||e10||e11|____|e12||e13|_________| +# |----------------------------------------------------------------------------| +# ``` + +# Let's load some example data. The `ingest.py` script has a series of loaders to help. If you've already run the other notebooks, you might skip `ingest_subjects` and `ingest_sessions`. + +from workflow_miniscope.ingest import ingest_subjects, ingest_sessions,\ + ingest_events, ingest_alignment + +# If you've already run previous notebooks, no need to ingest subjects or sessions. + +# ingest_subjects(); ingest_sessions() +ingest_events() + +# We have 50 total trials, either 'stim' or 'ctrl', with start and stop time + +trial.Trial() & "subject='subject1'" + +# Each trial is paired with one or more events that take place during the trial window. + +trial.TrialEvent() & 'trial_id<5' & "subject='subject1'" + +# Finally, the `AlignmentEvent` describes the event of interest and the window we'd like to see around it. + +ingest_alignment() + +event.AlignmentEvent() + +# + [markdown] tags=[] +# # Event-aligned trialized calcium activity +# - + +from workflow_miniscope import analysis + +# + [markdown] jp-MarkdownHeadingCollapsed=true jp-MarkdownHeadingCollapsed=true tags=[] +# ### Analysis +# - + +# The `analysis` schema provides example tables to perform event-aligned Calcium activity analysis. +# + ***ActivityAlignmentCondition*** - a manual table to specify the inputs and condition for the analysis +# + ***ActivityAlignment*** - a computed table to extract event-aligned Calcium activity (e.g. dF/F, spikes) + +# Let's start by creating several analyses configuration - i.e. inserting into ***ActivityAlignmentCondition*** + +miniscope.Activity() + +# We'll isolate the scan of interest with the following key: + +activity_key = (miniscope.Activity & {'subject': 'subject1', + 'extraction_method': 'caiman_dff'} + ).fetch1('KEY') + +# Here, we can see all trials for this scan: + +trial.Trial & ca_activity_key + +# And highlight a subset based on `trial_type` + +ctrl_trials = trial.Trial & ca_activity_key & 'trial_type = "ctrl"' +ctrl_trials + +# Here, we target the event of interest with another key: + +alignment_key = (event.AlignmentEvent & 'alignment_name = "center_button"' + ).fetch1('KEY') +alignment_key + +alignment_condition = {**ca_activity_key, **alignment_key, + 'trial_condition': 'ctrl_center_button'} +alignment_condition + +# Next, we add this to the `ActivityAlignment` table in the `analysis` schema + +analysis.ActivityAlignmentCondition.insert1(alignment_condition, skip_duplicates=True) + +analysis.ActivityAlignmentCondition() + +# Using the [projection](https://docs.datajoint.org/python/v0.13/queries/08-Proj.html) method, we can generate a table of relevant trials by `trial_type` and `alignment_condition` + +sample = (analysis.ActivityAlignmentCondition * ctrl_trials + & alignment_condition).proj() +sample + +# And insert these trials into the `ActivityAlignmentCondition.Trial` part table + +analysis.ActivityAlignmentCondition.Trial.insert(sample, skip_duplicates=True) +analysis.ActivityAlignmentCondition.Trial() + +# With the steps above, we have create a new alignment condition for analysis, named `ctrl_center_button`, which specifies: +# + an Activity of interest for analysis +# + an event of interest to align the Ca+ activity to - `center_button` +# + a set of trials of interest to perform the analysis on - `ctrl` trials +# +# --- + +# Now, let's create another set with: +# + the same Activity of interest for analysis +# + an event of interest to align the Ca+ activity to - `center_button` +# + a set of trials of interest to perform the analysis on - `stim` trials + +stim_trials = trial.Trial & ca_activity_key & 'trial_type = "stim"' +alignment_condition = {**ca_activity_key, **alignment_key, + 'trial_condition': 'stim_center_button'} +analysis.ActivityAlignmentCondition.insert1(alignment_condition, skip_duplicates=True) +analysis.ActivityAlignmentCondition.Trial.insert( + (analysis.ActivityAlignmentCondition * stim_trials & alignment_condition).proj(), + skip_duplicates=True) + +# Note the two entries in `ActivityAlignmentCondition.trial_condition` + +analysis.ActivityAlignmentCondition() + +analysis.ActivityAlignmentCondition.Trial & 'trial_condition = "ctrl_center_button"' + +# + [markdown] jp-MarkdownHeadingCollapsed=true tags=[] +# ### Computation +# Just like the element itself, we can run computations with `populate()` +# - + +analysis.ActivityAlignment.populate(display_progress=True) + +analysis.ActivityAlignment() + +# The `AlignedTrialSpikes` part table captures aligned traces fore each alignment and trial condition specified in the master table. + +analysis.ActivityAlignment.AlignedTrialSpikes() + +# ### Visualization + +# With the `plot_aligned_activities` function, we can see the density of activity relative to our alignment event. For more information, see the corresponding docstring. + +help(analysis.ActivityAlignment().plot_aligned_activities) + +# For a refresher on the differences between masks, we can browse the `imaging.Segmentation.Mask` table. + +imaging.Segmentation.Mask & 'mask<3' + +# Then, we can directly compare the stimulus and control conditions relative to center button presses. + +from workflow_calcium_imaging import analysis +from workflow_calcium_imaging.pipeline import session, imaging, trial, event +ca_activity_key = (imaging.Activity & {'subject': 'subject3', 'scan_id': 0} + ).fetch1('KEY') +alignment_key = (event.AlignmentEvent & 'alignment_name = "center_button"' + ).fetch1('KEY') +alignment_condition_ctrl = {**ca_activity_key, **alignment_key, + 'trial_condition': 'ctrl_center_button'} +alignment_condition_stim = {**ca_activity_key, **alignment_key, + 'trial_condition': 'stim_center_button'} + +analysis.ActivityAlignment().plot_aligned_activities(alignment_condition_stim, roi=2, + title='Stimulus Center Button'); +analysis.ActivityAlignment().plot_aligned_activities(alignment_condition_ctrl, roi=2, + title='Control Center Button'); + + diff --git a/user_data/behavior_recordings.csv b/user_data/behavior_recordings.csv index 11c6dd6..3c6a9dc 100644 --- a/user_data/behavior_recordings.csv +++ b/user_data/behavior_recordings.csv @@ -1,3 +1,3 @@ subject,session_datetime,filepath -subject1,2019-01-01 00:00:00,./user_data/trials.csv -subject1,2019-01-01 00:00:00,./user_data/events.csv +subject1,2021-01-01 00:00:01,./user_data/trials.csv +subject1,2021-01-01 00:00:01,./user_data/events.csv diff --git a/user_data/blocks.csv b/user_data/blocks.csv index d6f943a..95429a9 100644 --- a/user_data/blocks.csv +++ b/user_data/blocks.csv @@ -1,3 +1,3 @@ subject,session_datetime,block_id,block_start_time,block_stop_time,attribute_name,attribute_value -subject1,2019-01-01 00:00:00,1,0,24,type,light -subject1,2019-01-01 00:00:00,2,24,48,type,dark +subject1,2021-01-01 00:00:01,1,0,24,type,light +subject1,2021-01-01 00:00:01,2,24,48,type,dark diff --git a/user_data/events.csv b/user_data/events.csv index 1da572a..f106b06 100644 --- a/user_data/events.csv +++ b/user_data/events.csv @@ -1,78 +1,78 @@ subject,session_datetime,trial_id,event_start_time,event_type -subject1,2019-01-01 00:00:00,1,0.269,right -subject1,2019-01-01 00:00:00,1,0.407,left -subject1,2019-01-01 00:00:00,2,1.611,center -subject1,2019-01-01 00:00:00,2,1.649,center -subject1,2019-01-01 00:00:00,3,2.935,left -subject1,2019-01-01 00:00:00,3,2.777,left -subject1,2019-01-01 00:00:00,4,4.006,right -subject1,2019-01-01 00:00:00,5,5.123,center -subject1,2019-01-01 00:00:00,6,5.995,right -subject1,2019-01-01 00:00:00,6,6.033,center -subject1,2019-01-01 00:00:00,7,7.29,left -subject1,2019-01-01 00:00:00,7,7.397,center -subject1,2019-01-01 00:00:00,8,8.54,left -subject1,2019-01-01 00:00:00,8,8.531,center -subject1,2019-01-01 00:00:00,9,10.118,left -subject1,2019-01-01 00:00:00,9,10.035,center 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00:00:01,44,45.916,center +subject1,2021-01-01 00:00:01,45,47.316,right +subject1,2021-01-01 00:00:01,46,48.23,center +subject1,2021-01-01 00:00:01,47,49.44,left +subject1,2021-01-01 00:00:01,47,49.559,center +subject1,2021-01-01 00:00:01,48,50.527,left +subject1,2021-01-01 00:00:01,49,51.947,left +subject1,2021-01-01 00:00:01,50,48.535,right +subject1,2021-01-01 00:00:01,50,48.404,right diff --git a/user_data/subjects.csv b/user_data/subjects.csv index a2ffc2c..91bbe23 100644 --- a/user_data/subjects.csv +++ b/user_data/subjects.csv @@ -1,2 +1,2 @@ subject,sex,subject_birth_date,subject_description -subject1,M,2019-01-01 00:00:00, \ No newline at end of file +subject1,M,2021-01-01 00:00:01, diff --git a/user_data/trials.csv b/user_data/trials.csv index 626665b..25f7e02 100644 --- a/user_data/trials.csv +++ b/user_data/trials.csv @@ -1,51 +1,51 @@ subject,session_datetime,block_id,trial_id,trial_start_time,trial_stop_time,trial_type,attribute_name,attribute_value -subject1,2019-01-01 00:00:00,1,1,0.193,1.057,stim,lumen,646 -subject1,2019-01-01 00:00:00,1,2,1.468,2.332,ctrl,lumen,555 -subject1,2019-01-01 00:00:00,1,3,2.662,3.526,ctrl,lumen,907 -subject1,2019-01-01 00:00:00,1,4,3.738,4.602,ctrl,lumen,639 -subject1,2019-01-01 00:00:00,1,5,4.826,5.69,stim,lumen,835 -subject1,2019-01-01 00:00:00,1,6,5.973,6.837,stim,lumen,815 -subject1,2019-01-01 00:00:00,1,7,7.252,8.116,ctrl,lumen,995 -subject1,2019-01-01 00:00:00,1,8,8.462,9.326,ctrl,lumen,869 -subject1,2019-01-01 00:00:00,1,9,9.731,10.595,ctrl,lumen,501 -subject1,2019-01-01 00:00:00,1,10,11.019,11.883,stim,lumen,901 -subject1,2019-01-01 00:00:00,1,11,12.206,13.07,stim,lumen,738 -subject1,2019-01-01 00:00:00,1,12,13.279,14.143,stim,lumen,638 -subject1,2019-01-01 00:00:00,1,13,14.427,15.291,ctrl,lumen,585 -subject1,2019-01-01 00:00:00,1,14,15.563,16.427,ctrl,lumen,974 -subject1,2019-01-01 00:00:00,1,15,16.715,17.579,ctrl,lumen,515 -subject1,2019-01-01 00:00:00,1,16,17.706,18.57,ctrl,lumen,575 -subject1,2019-01-01 00:00:00,1,17,18.841,19.705,stim,lumen,788 -subject1,2019-01-01 00:00:00,1,18,19.828,20.692,stim,lumen,509 -subject1,2019-01-01 00:00:00,1,19,20.965,21.829,stim,lumen,789 -subject1,2019-01-01 00:00:00,1,20,22.113,22.977,ctrl,lumen,928 -subject1,2019-01-01 00:00:00,1,21,23.094,23.958,stim,lumen,937 -subject1,2019-01-01 00:00:00,1,22,24.314,25.178,stim,lumen,733 -subject1,2019-01-01 00:00:00,1,23,25.321,26.185,stim,lumen,527 -subject1,2019-01-01 00:00:00,1,24,26.311,27.175,stim,lumen,857 -subject1,2019-01-01 00:00:00,1,25,24.139,25.003,stim,lumen,536 -subject1,2019-01-01 00:00:00,2,26,25.159,26.023,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,27,26.203,27.067,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,28,27.192,28.056,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,29,28.465,29.329,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,30,29.43,30.294,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,31,30.557,31.421,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,32,31.774,32.638,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,33,33.034,33.898,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,34,34.175,35.039,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,35,35.36,36.224,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,36,36.481,37.345,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,37,37.63,38.494,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,38,38.748,39.612,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,39,39.995,40.859,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,40,41.027,41.891,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,41,42.161,43.025,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,42,43.296,44.16,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,43,44.523,45.387,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,44,45.733,46.597,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,45,46.913,47.777,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,46,48.067,48.931,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,47,49.198,50.062,stim,lumen,0 -subject1,2019-01-01 00:00:00,2,48,50.268,51.132,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,49,51.52,52.384,ctrl,lumen,0 -subject1,2019-01-01 00:00:00,2,50,48.211,49.075,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,1,1,0.193,1.057,stim,lumen,646 +subject1,2021-01-01 00:00:01,1,2,1.468,2.332,ctrl,lumen,555 +subject1,2021-01-01 00:00:01,1,3,2.662,3.526,ctrl,lumen,907 +subject1,2021-01-01 00:00:01,1,4,3.738,4.602,ctrl,lumen,639 +subject1,2021-01-01 00:00:01,1,5,4.826,5.69,stim,lumen,835 +subject1,2021-01-01 00:00:01,1,6,5.973,6.837,stim,lumen,815 +subject1,2021-01-01 00:00:01,1,7,7.252,8.116,ctrl,lumen,995 +subject1,2021-01-01 00:00:01,1,8,8.462,9.326,ctrl,lumen,869 +subject1,2021-01-01 00:00:01,1,9,9.731,10.595,ctrl,lumen,501 +subject1,2021-01-01 00:00:01,1,10,11.019,11.883,stim,lumen,901 +subject1,2021-01-01 00:00:01,1,11,12.206,13.07,stim,lumen,738 +subject1,2021-01-01 00:00:01,1,12,13.279,14.143,stim,lumen,638 +subject1,2021-01-01 00:00:01,1,13,14.427,15.291,ctrl,lumen,585 +subject1,2021-01-01 00:00:01,1,14,15.563,16.427,ctrl,lumen,974 +subject1,2021-01-01 00:00:01,1,15,16.715,17.579,ctrl,lumen,515 +subject1,2021-01-01 00:00:01,1,16,17.706,18.57,ctrl,lumen,575 +subject1,2021-01-01 00:00:01,1,17,18.841,19.705,stim,lumen,788 +subject1,2021-01-01 00:00:01,1,18,19.828,20.692,stim,lumen,509 +subject1,2021-01-01 00:00:01,1,19,20.965,21.829,stim,lumen,789 +subject1,2021-01-01 00:00:01,1,20,22.113,22.977,ctrl,lumen,928 +subject1,2021-01-01 00:00:01,1,21,23.094,23.958,stim,lumen,937 +subject1,2021-01-01 00:00:01,1,22,24.314,25.178,stim,lumen,733 +subject1,2021-01-01 00:00:01,1,23,25.321,26.185,stim,lumen,527 +subject1,2021-01-01 00:00:01,1,24,26.311,27.175,stim,lumen,857 +subject1,2021-01-01 00:00:01,1,25,24.139,25.003,stim,lumen,536 +subject1,2021-01-01 00:00:01,2,26,25.159,26.023,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,27,26.203,27.067,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,28,27.192,28.056,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,29,28.465,29.329,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,30,29.43,30.294,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,31,30.557,31.421,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,32,31.774,32.638,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,33,33.034,33.898,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,34,34.175,35.039,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,35,35.36,36.224,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,36,36.481,37.345,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,37,37.63,38.494,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,38,38.748,39.612,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,39,39.995,40.859,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,40,41.027,41.891,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,41,42.161,43.025,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,42,43.296,44.16,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,43,44.523,45.387,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,44,45.733,46.597,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,45,46.913,47.777,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,46,48.067,48.931,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,47,49.198,50.062,stim,lumen,0 +subject1,2021-01-01 00:00:01,2,48,50.268,51.132,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,49,51.52,52.384,ctrl,lumen,0 +subject1,2021-01-01 00:00:01,2,50,48.211,49.075,ctrl,lumen,0 diff --git a/workflow_miniscope/analysis.py b/workflow_miniscope/analysis.py index 720c449..f0f9118 100644 --- a/workflow_miniscope/analysis.py +++ b/workflow_miniscope/analysis.py @@ -43,11 +43,15 @@ class AlignedTrialActivity(dj.Part): """ def make(self, key): - sess_time, scan_time, nframes, frame_rate = (scan.ScanInfo * session.Session + # DEVNOTE : caimg->miniscope, no scan_datetime. so removed scan_time from fetch + # safe to assume no sess-scan diff? + sess_time, nframes, frame_rate = (miniscope.RecordingInfo + * session.Session & key ).fetch1('session_datetime', - 'scan_datetime', + # 'scan_datetime', 'nframes', 'fps') + scan_time = None # Estimation of frame timestamps with respect to the session-start # (to be replaced by timestamps retrieved from some synchronization routine) diff --git a/workflow_miniscope/ingest.py b/workflow_miniscope/ingest.py index 9fccc18..73830ef 100644 --- a/workflow_miniscope/ingest.py +++ b/workflow_miniscope/ingest.py @@ -3,9 +3,10 @@ from datetime import datetime import json -from .pipeline import subject, session, Equipment, miniscope +from .pipeline import subject, session, Equipment, miniscope, trial, event from .paths import get_miniscope_root_data_dir -from element_interface.utils import find_full_path, recursive_search, csv +from element_interface.utils import find_full_path, recursive_search, \ + ingest_csv_to_table def ingest_subjects(subject_csv_path='./user_data/subjects.csv', @@ -110,8 +111,8 @@ def ingest_events(recording_csv_path='./user_data/behavior_recordings.csv', trial.BlockTrial(), event.EventType(), event.Event(), trial.TrialEvent()] - ingest_csv_to_table(csvs, tables, skip_duplicates=skip_duplicates, verbose=verbose) - # allow_direct_insert=True) + ingest_csv_to_table(csvs, tables, skip_duplicates=skip_duplicates, verbose=verbose, + allow_direct_insert=True) # Allow direct insert required bc element-trial has Imported that should be Manual # ISSUE: element-interface version doesn't have allow_direct_insert arg diff --git a/workflow_miniscope/pipeline.py b/workflow_miniscope/pipeline.py index 10d825c..9f260d9 100644 --- a/workflow_miniscope/pipeline.py +++ b/workflow_miniscope/pipeline.py @@ -3,6 +3,7 @@ from element_lab import lab from element_animal import subject from element_session import session_with_datetime as session +from element_event import trial, event from element_miniscope import miniscope from element_lab.lab import Source, Lab, Protocol, User, Location, Project @@ -26,6 +27,9 @@ Experimenter = lab.User session.activate(db_prefix + 'session', linking_module=__name__) +# Activate "event" and "trial" schema --------------------------------- + +trial.activate(db_prefix + 'trial', db_prefix + 'event', linking_module=__name__) # Declare table `Equipment` and `AnatomicalLocation` for use in element_miniscope ------ From 008f96c9170bb1c23bc62dd0a0d0125d4a58cdc3 Mon Sep 17 00:00:00 2001 From: Chris Broz Date: Thu, 28 Apr 2022 17:51:58 -0500 Subject: [PATCH 5/8] Notebooks 05, 07 successful run --- notebooks/05-explore.ipynb | 321 ++----------- .../07-downstream-analysis-optional.ipynb | 422 ++++++++++-------- workflow_miniscope/analysis.py | 5 +- 3 files changed, 289 insertions(+), 459 deletions(-) diff --git a/notebooks/05-explore.ipynb b/notebooks/05-explore.ipynb index 3d81127..87b87b4 100644 --- a/notebooks/05-explore.ipynb +++ b/notebooks/05-explore.ipynb @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -418,7 +418,7 @@ " (Total: 1)" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1112,7 +1112,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -1121,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -1134,7 +1134,7 @@ " 'curation_id': 0}" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1145,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1257,7 +1257,7 @@ " (Total: 1)" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1266,247 +1266,6 @@ "miniscope.MotionCorrection.RigidMotionCorrection & curation_key" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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block_x

\n", - " (x_start, x_end) in pixel of this block\n", - "
\n", - "

y_shifts

\n", - " (pixels) y motion correction shifts for\n", - "
\n", - "

x_shifts

\n", - " (pixels) x motion correction shifts for\n", - "
\n", - "

y_std

\n", - " (pixels) standard deviation of y shifts\n", - "
\n", - "

x_std

\n", - " (pixels) standard deviation of x shifts\n", - "
\n", - " \n", - "

Total: 0

\n", - " " - ], - "text/plain": [ - "*subject *session_datet *recording_id *paramset_id *curation_id *block_id block_y block_x y_shifts x_shifts y_std x_std \n", - "+---------+ +------------+ +------------+ +------------+ +------------+ +----------+ +--------+ +--------+ +--------+ +--------+ +-------+ +-------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "miniscope.MotionCorrection.Block & curation_key & 'block_id=0'" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1524,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1632,7 +1391,7 @@ " (Total: 1)" ] }, - "execution_count": 19, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1650,39 +1409,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "average_image = (miniscope.MotionCorrection.Summary & curation_key\n", - " ).fetch1('average_image')" + " ).fetch1('average_image')[0]" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ - { - "ename": "TypeError", - "evalue": "Invalid shape (1, 600, 600) for image data", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/_9/tzvq__ws5z9gv5s7jvkj570r0000gn/T/ipykernel_27306/2976288256.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maverage_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\u001b[0m name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 456\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m 2638\u001b[0m \u001b[0minterpolation_stage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2639\u001b[0m resample=None, url=None, data=None, **kwargs):\n\u001b[0;32m-> 2640\u001b[0;31m __ret = gca().imshow(\n\u001b[0m\u001b[1;32m 2641\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2642\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\u001b[0m name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[0;32m--> 456\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;31m# Don't modify *func*'s signature, as boilerplate.py needs it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1410\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1411\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1414\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m 5440\u001b[0m **kwargs)\n\u001b[1;32m 5441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5442\u001b[0;31m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5443\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5444\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m 713\u001b[0m if not (self._A.ndim == 2\n\u001b[1;32m 714\u001b[0m or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[0;32m--> 715\u001b[0;31m raise TypeError(\"Invalid shape {} for image data\"\n\u001b[0m\u001b[1;32m 716\u001b[0m .format(self._A.shape))\n\u001b[1;32m 717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: Invalid shape (1, 600, 600) for image data" - ] - }, { "data": { - "image/png": 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\n", 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" ] @@ -1710,19 +1452,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "mask_xpix, mask_ypix = (miniscope.Segmentation.Mask \n", " * miniscope.MaskClassification.MaskType \n", - " & curation_key & 'mask_center_z=0' & 'mask_npix > 130'\n", + " & curation_key # & 'mask_npix > 130'\n", " ).fetch('mask_xpix','mask_ypix')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1733,9 +1475,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.imshow(average_image);\n", "plt.contour(mask_image, colors='white', linewidths=0.5);" @@ -1752,7 +1507,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1860,7 +1615,7 @@ " (Total: 1)" ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1880,7 +1635,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1894,7 +1649,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2113,7 +1868,7 @@ " (Total: 18)" ] }, - "execution_count": 38, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2124,7 +1879,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -2139,7 +1894,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 31, "metadata": {}, "outputs": [ { diff --git a/notebooks/07-downstream-analysis-optional.ipynb b/notebooks/07-downstream-analysis-optional.ipynb index feab5e9..cb2145d 100644 --- a/notebooks/07-downstream-analysis-optional.ipynb +++ b/notebooks/07-downstream-analysis-optional.ipynb @@ -702,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "9a36c342", "metadata": {}, "outputs": [ @@ -805,7 +805,7 @@ " (Total: 2)" ] }, - "execution_count": 10, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -2192,6 +2192,7 @@ "id": "1486add8", "metadata": { "incorrectly_encoded_metadata": "jp-MarkdownHeadingCollapsed=true", + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -2209,26 +2210,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "ActivityAlignment: 0%| | 0/2 [00:04\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mActivityAlignment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisplay_progress\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m in \u001b[0;36mpopulate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdisplay_progress\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m ):\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_populate1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpopulate_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0merror_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/autopopulate.py\u001b[0m in \u001b[0;36m_populate1\u001b[0;34m(self, key, jobs, suppress_errors, return_exception_objects, make_kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_allow_insert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_kwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSystemExit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Volumes/GoogleDrive/My Drive/NWB/workflow-miniscope/workflow_miniscope/analysis.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mnsamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maligned_timestamps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m trace_keys, activity_traces = (miniscope.Activity.Trace & key\n\u001b[0m\u001b[1;32m 71\u001b[0m ).fetch('KEY', 'activity_trace', order_by='mask')\n\u001b[1;32m 72\u001b[0m \u001b[0mactivity_traces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivity_traces\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/fetch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, offset, limit, order_by, format, as_dict, squeeze, download_path, *attrs)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mattributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mattrs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mattributes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m ret = ret.fetch(\n\u001b[0m\u001b[1;32m 229\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/fetch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, offset, limit, order_by, format, as_dict, squeeze, download_path, *attrs)\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mreturn_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# fetch all attributes as a numpy.record_array or pandas.DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m cur = self._expression.cursor(\n\u001b[0m\u001b[1;32m 256\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder_by\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder_by\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 257\u001b[0m )\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/expression.py\u001b[0m in \u001b[0;36mcursor\u001b[0;34m(self, offset, limit, order_by, as_dict)\u001b[0m\n\u001b[1;32m 621\u001b[0m \u001b[0msql\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\" LIMIT %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" OFFSET %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0mcursor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcursor_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress_warnings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 338\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLostConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreconnect\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/venv-nwb/lib/python3.8/site-packages/datajoint/connection.py\u001b[0m in \u001b[0;36m_execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtranslate_query_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 296\u001b[0m def query(\n", - "\u001b[0;31mUnknownAttributeError\u001b[0m: Unknown column 'mask' in 'order clause'" + "ActivityAlignment: 100%|█████████████████████████████████████████████████████████| 2/2 [00:10<00:00, 5.34s/it]\n" ] } ], @@ -2305,10 +2287,10 @@ "

session_datetime

\n", " \n", "
\n", - "

scan_id

\n", + "

recording_id

\n", " \n", "
\n", - "

paramset_idx

\n", + "

paramset_id

\n", " \n", "
\n", "

curation_id

\n", @@ -2326,20 +2308,20 @@ "

aligned_timestamps

\n", " \n", "
\n", - " subject3\n", - "2021-10-25 13:06:40\n", + " subject1\n", + "2021-01-01 00:00:01\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "0\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "0\n", + "caiman_dff\n", "center_button\n", "stim_center_button\n", "=BLOB= \n", @@ -2349,10 +2331,10 @@ " " ], "text/plain": [ - "*subject *session_datet *scan_id *paramset_idx *curation_id *extraction_me *alignment_nam *trial_conditi aligned_ti\n", - "+----------+ +------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+\n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button stim_center_bu =BLOB= \n", + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me *alignment_nam *trial_conditi aligned_ti\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button stim_center_bu =BLOB= \n", " (Total: 2)" ] }, @@ -2375,7 +2357,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "974d65f3-da0f-4dbf-aa79-108e42fb74c0", "metadata": {}, "outputs": [ @@ -2442,10 +2424,10 @@ "

session_datetime

\n", " \n", "
\n", - "

scan_id

\n", + "

recording_id

\n", " \n", "
\n", - "

paramset_idx

\n", + "

paramset_id

\n", " \n", "
\n", "

curation_id

\n", @@ -2460,10 +2442,10 @@ "

trial_condition

\n", " user-friendly name of condition\n", "
\n", - "

mask

\n", + "

mask_id

\n", " \n", "
\n", - "

fluo_channel

\n", + "

fluorescence_channel

\n", " 0-based indexing\n", "
\n", "

trial_id

\n", @@ -2472,146 +2454,146 @@ "

aligned_trace

\n", " (s) Calcium activity aligned to the event time\n", "
\n", - " subject3\n", - "2021-10-25 13:06:40\n", + " subject1\n", + "2021-01-01 00:00:01\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "0\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "2\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "4\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "7\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "5\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "8\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "15\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "9\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "19\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "20\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "22\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "27\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "23\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "37\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "33\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "43\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "36\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", "ctrl_center_button\n", + "1\n", "0\n", + "46\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "40\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", - "1\n", - "suite2p_deconvolution\n", + "caiman_dff\n", "center_button\n", - "stim_center_button\n", - "0\n", - "0\n", + "ctrl_center_button\n", "1\n", + "0\n", + "50\n", "=BLOB= \n", " \n", "

...

\n", - "

Total: 126084

\n", + "

Total: 2938

\n", " " ], "text/plain": [ - "*subject *session_datet *scan_id *paramset_idx *curation_id *extraction_me *alignment_nam *trial_conditi *mask *fluo_channel *trial_id aligned_tr\n", - "+----------+ +------------+ +---------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------+ +------------+ +----------+ +--------+\n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 4 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 5 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 15 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 19 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 22 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 23 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 33 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 36 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button ctrl_center_bu 0 0 40 =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 suite2p_deconv center_button stim_center_bu 0 0 1 =BLOB= \n", + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me *alignment_nam *trial_conditi *mask_id *fluorescence_ *trial_id aligned_tr\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +----------+ +--------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 2 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 7 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 8 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 9 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 20 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 27 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 37 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 43 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 46 =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff center_button ctrl_center_bu 1 0 50 =BLOB= \n", " ...\n", - " (Total: 126084)" + " (Total: 2938)" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "analysis.ActivityAlignment.AlignedTrialSpikes()" + "analysis.ActivityAlignment.AlignedTrialActivity()" ] }, { @@ -2632,7 +2614,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "f24049f9-7eda-4d14-b420-5a33ce60f36b", "metadata": {}, "outputs": [ @@ -2640,12 +2622,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Help on method plot_aligned_activities in module workflow_calcium_imaging.analysis:\n", + "Help on method plot_aligned_activities in module workflow_miniscope.analysis:\n", "\n", - "plot_aligned_activities(key, roi, axs=None, title=None) method of workflow_calcium_imaging.analysis.ActivityAlignment instance\n", - " peri-stimulus time histogram (PSTH) for calcium imaging spikes\n", + "plot_aligned_activities(key, roi, axs=None, title=None) method of workflow_miniscope.analysis.ActivityAlignment instance\n", + " Plot event-aligned Calcium activities for all selected trials, and\n", + " trial-averaged Calcium activity\n", + " e.g. dF/F, neuropil-corrected dF/F, Calcium events, etc.\n", " :param key: key of ActivityAlignment master table\n", - " :param roi: imaging segmentation mask\n", + " :param roi: miniscope segmentation mask\n", " :param axs: optional definition of axes for plot.\n", " Default is plt.subplots(2, 1, figsize=(12, 8))\n", " :param title: Optional title label\n", @@ -2667,7 +2651,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "32934d3b-01a7-428e-bf59-dbc89131979a", "metadata": {}, "outputs": [ @@ -2734,109 +2718,84 @@ "

session_datetime

\n", " \n", "
\n", - "

scan_id

\n", + "

recording_id

\n", " \n", "
\n", - "

paramset_idx

\n", + "

paramset_id

\n", " \n", "
\n", "

curation_id

\n", " \n", "
\n", - "

mask

\n", + "

mask_id

\n", " \n", "
\n", "

segmentation_channel

\n", " 0-based indexing\n", "
\n", "

mask_npix

\n", - " number of pixels in ROIs\n", + " number of pixels in this mask\n", "
\n", "

mask_center_x

\n", - " center x coordinate in pixel\n", + " (pixels) center x coordinate\n", "
\n", "

mask_center_y

\n", - " center y coordinate in pixel\n", - "
\n", - "

mask_center_z

\n", - " center z coordinate in pixel\n", + " (pixels) center y coordinate\n", "
\n", "

mask_xpix

\n", - " x coordinates in pixels\n", + " (pixels) x coordinates\n", "
\n", "

mask_ypix

\n", - " y coordinates in pixels\n", - "
\n", - "

mask_zpix

\n", - " z coordinates in pixels\n", + " (pixels) y coordinates\n", "
\n", "

mask_weights

\n", " weights of the mask at the indices above\n", "
\n", - " subject3\n", - "2021-10-25 13:06:40\n", - "0\n", - "0\n", - "1\n", - "0\n", - "0\n", - "133\n", - "336\n", - "480\n", + " subject1\n", + "2021-01-01 00:00:01\n", "0\n", - "=BLOB=\n", - "=BLOB=\n", - "=BLOB=\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", "0\n", "0\n", "1\n", - "1\n", - "0\n", - "126\n", - "283\n", - "348\n", "0\n", + "209\n", + "87\n", + "8\n", "=BLOB=\n", "=BLOB=\n", - "=BLOB=\n", - "=BLOB=subject3\n", - "2021-10-25 13:06:40\n", + "=BLOB=subject1\n", + "2021-01-01 00:00:01\n", "0\n", "0\n", - "1\n", - "2\n", "0\n", - "157\n", - "591\n", - "190\n", + "2\n", "0\n", - "=BLOB=\n", + "896\n", + "83\n", + "240\n", "=BLOB=\n", "=BLOB=\n", "=BLOB= \n", " \n", " \n", - "

Total: 3

\n", + "

Total: 2

\n", " " ], "text/plain": [ - "*subject *session_datet *scan_id *paramset_idx *curation_id *mask segmentation_c mask_npix mask_center_x mask_center_y mask_center_z mask_xpix mask_ypix mask_zpix mask_weigh\n", - "+----------+ +------------+ +---------+ +------------+ +------------+ +------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", - "subject3 2021-10-25 13: 0 0 1 0 0 133 336 480 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 1 0 126 283 348 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", - "subject3 2021-10-25 13: 0 0 1 2 0 157 591 190 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", - " (Total: 3)" + "*subject *session_datet *recording_id *paramset_id *curation_id *mask_id segmentation_c mask_npix mask_center_x mask_center_y mask_xpix mask_ypix mask_weigh\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +---------+ +------------+ +-----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n", + "subject1 2021-01-01 00: 0 0 0 1 0 209 87 8 =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-01-01 00: 0 0 0 2 0 896 83 240 =BLOB= =BLOB= =BLOB= \n", + " (Total: 2)" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "imaging.Segmentation.Mask & 'mask<3'" + "miniscope.Segmentation.Mask & 'mask_id<3'" ] }, { @@ -2849,14 +2808,129 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, + "id": "2ef9c48e-d788-46a3-992a-f61c44693c65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " inferred neural activity from fluorescence trace - e.g. dff, spikes\n", + "
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subject

\n", + " \n", + "
\n", + "

session_datetime

\n", + " \n", + "
\n", + "

recording_id

\n", + " \n", + "
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paramset_id

\n", + " \n", + "
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curation_id

\n", + " \n", + "
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extraction_method

\n", + " \n", + "
subject12021-01-01 00:00:01000caiman_deconvolution
subject12021-01-01 00:00:01000caiman_dff
\n", + " \n", + "

Total: 2

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *recording_id *paramset_id *curation_id *extraction_me\n", + "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", + "subject1 2021-01-01 00: 0 0 0 caiman_deconvo\n", + "subject1 2021-01-01 00: 0 0 0 caiman_dff \n", + " (Total: 2)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "miniscope.Activity()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "id": "f5e985f1-8bdd-450a-b283-8e93ab635202", "metadata": {}, "outputs": [], "source": [ - "from workflow_calcium_imaging import analysis\n", - "from workflow_calcium_imaging.pipeline import session, imaging, trial, event\n", - "ca_activity_key = (imaging.Activity & {'subject': 'subject3', 'scan_id': 0}\n", + "from workflow_miniscope import analysis\n", + "from workflow_miniscope.pipeline import session, miniscope, trial, event\n", + "ca_activity_key = (miniscope.Activity & {'subject': 'subject1', 'recording_id': 0}\n", + " & \"extraction_method='caiman_dff'\"\n", " ).fetch1('KEY')\n", "alignment_key = (event.AlignmentEvent & 'alignment_name = \"center_button\"'\n", " ).fetch1('KEY')\n", @@ -2868,13 +2942,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "c006d977-5d66-458f-992a-99728a96bb06", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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BkPrdwfJAxf8B4J1dRwEAPrf/K6D2oLfRcnl6zr8bDqntyy5PLuqYlqt77rkbwNwn3EUjgwCAkjVmbkvE1ji3qfdlMV/jkEgDAMoy69wy/2NKJNwLALDtaeeRLADev2l4Do8Wdr7aFbe6483Me5wLef5GhFD7ZCnzc37G6r+tM7EJAPC7N/eZ+2x+8RgAwNYp9VndkVHHE73Hvze7CwDwr8cnAQCXPvscAOADXa8BAFw/cZd5rNOEuu3+0k8BAH/U/e6K8dxb2KaeY+YXAICzkh8EAJw/pB77b3Z81dy3L3UyACBnqePamWF1fLkn93UA7rH/9H71t/3bzgIAYDeeNY/RK9YCACZwAACwsXy8+vtz3wXVM79tdam4x5EZ55aFx6n6szCdf149orSE3/2YKSYiIiKiwGNLNiKiNpTL5Vo9BCKiQGFQTETUhs4//4JWD4GIKFBYPkFEREREgcegmIioDX3603+NT3/6r1s9DCKiwGBQTETUhs45500455w3tXoYRESBwaCYiIiIiAKPQTERERERBR6DYiIiIiIKPLZkIyJqQ6Ojo60eAhFRoDAoJiJqQ+95z4WtHgIRUaCwfIKIiIiIAo9BMRFRG/rsZ/8Bn/3sP7R6GEREgcHyCSKiNnTWWWe2eghERIHCTDERERERBR6DYiIiIiIKPAbFRERERBR4rCkmImpDe/bsafUQiIgChUExEVEbuvjiS1o9BCKiQGH5BBEREREFHoNiIqI29PnPX4PPf/6aVg+DiCgwhJSy1WPASR3vlQBwy+tGAAAv+dmvAADv7voYAEDCHeN91oMAgNHsE84t9uEaJtGcCBEDAEhZXPTHjoT7AQCWPaqey6mEkrAAAGvSrzb33Z/5Td3HikVWAwCK1oGmnz8VPxIAkC3s8P15IrYeANARHQIAjGQeNT/TY6se18tS7wUAPJn9gXNLGADQlTgWAJAM9wIAJoq7zO8USvt8nz8c6gYA2OXJhn9Lu7rnnrsBAG984zmL8GjC+ar2pbVen1rbrL7d72eafs/zxT0V/y8U1XYViaj3z7K9z6nyMq9NvB8A8MvcN3wfuyf5UgDARO4p35/76U6eBACYzD3T9O+0u+rPebP0/gIALHvMeazwvB7rcAmJNACgLDMtHYcQCQCAlPnD/hit3o+FQp0AgHJ5uvF9F/X9CjtfK+O7+W7/fqS0hN/tzBQTERERUeAxKCYiIiKiwGNQTERERESBx5ZsRERtaOvWra0eAhFRoDAoJiJqQx/5yBWtHgIRUaCwfIKIiIiIAo9BMRFRG7ruumtx3XXXtnoYRESBwfIJIqI2dNxxx7V6CEREgcJMMREREREFHoNiIiIiIgo8BsVEREREFHisKSYiakObN29u9RCIiAKFQTERURv6sz+7qtVDICIKFJZPEBEREVHgMSgmImpDN954A2688YZWD4OIKDBYPkFE1IbWr1/f6iEQEQUKM8VEREREFHgMiomIiIgo8BgUExEREVHgsaaYiKgNPfDAg60eAhFRoDAoJiJqQ5/61F+3eghERIEipJStHgOEiLR+EG1MiAQAICTiAAC7POn8JOx8tWf/jnO+I2EtwXhi6rFlcdEfe2kI52u7bWZqXKFQBwCgXJ5ewGPV3hbm6tj02wAA2zI/bXjfnuRLAQATuacAAJFwPwDAskcXPI7lIhLuBQBY9njD++rPspT5RXv+2Z/HsDOuHnOfWu/HXMbejvTfDjS/PwqHugG4+9FETHX5yBf3+D2DfvSK51ucfV/l51+Wc84zLd4+231/Jzy3Lt1+MBnbCADIFXdV3B4KdQJY6D6uVZrbt0YjgwCAkjVc8z59qZMBAE++qwwA+PHDrwQAbJ9Rx/bpktombstuBgD86aDav9550N0mHrLvBAD85ZrzAQA/HFaf7QGptut7cl8HAJyf/ggA4I7MdQCA7770AwCAz78gzGM9nLsRAHDTy98PALjoie8DAF6W+gM1zuwPAABXrf0YAOB7k08CAPZlfgUAOC91uXmsdFi9Tj/L3woA+IvV7wAAfGb3tQCAr594KQDgw1uu939xDjMpLeF3O2uKiYja0C233Ixbbrm51cMgIgoMlk8QEbWh/v7+Vg+BiChQmCkmIiIiosBjUExEREREgcegmIiIiIgCjzXFRERt6O67/7vVQyAiChQGxUREbejqq/+h1UMgIgoUlk8QERERUeAxKCYiakN33HE77rjj9lYPg4goMFg+QUTUhpLJZKuHQEQUKMwUExEREVHgMSgmIiIiosBjUExEREREgceaYiKiNnT77T9t9RCIiAKFQTERURu65prPtXoIRESBwvIJIiIiIgo8BsVERG3onnvuxj333N3qYRARBQaDYiIiIiIKPAbFRERERBR4DIqJiIiIKPAYFBMRERFR4AkpZavHACEiVYMIO1/tBT/2sem3AQC2ZRr3/OxJvhQAMJF7CgAQCfcDACx7dMHjWC4i4V4AgGWPN7yvEAkAgJT5RXt+IWLOYxadW8LOuHqccdV+L+Yy9nak/3bA+/fXFw51AwDs8iQAIBFbDwDIF/f4PYN+9Irna/a56lOPHQp1qMcs55xnshbhsRX3/Z3w3Lp0+69kbCMAIFfcVXF7KNQJACiXp5fsuQHgiis+CgC49tqvLuKjNrdvjUYGAQAla7jmffpSJwMAnnxXGQDw44dfCQDYPhMHAEyX1DZxW3YzAOBPB9X+9c6D7jbxkH0nAOAv15wPAPjhsPp8D0i1Xd+T+zoA4Pz0RwAAd2SuAwB896UfAAB8/gVhHuvh3I0AgJte/n4AwEVPfB8A8LLUH6hxZn8AALhq7ccAAN+bfBIAsC/zKwDAeanLzWOlw+p1+ln+VgDAX6x+BwDgM7uvBQB8/cRLAQAf3nK9/4tDbSEk0gCAssws2XMs7n50eelKHG++n8o/V/GzzsQmAMB0/nkA84sXdEw2lX8RQOP3cSh9pvl+OPe0+p2q/bQ+RuYKOwR8sE8xEVEbWtxgmIiIGmH5BBFRG0omk0gmk60eBhFRYDBTTETUhu6443YAwBvfeE6LR0JEFAzMFBMRERFR4DEoJiIiIqLAY1BMRERERIHHoJiIiIiIAo8T7YiI2tD119/Q6iEQEQUKg2IiojZ0ww3favUQiIgCheUTRERtqL+/H/39/a0eBhFRYDBTTETUhm655WYA7FNMRHS4MFNMRERERIHHoJiIiIiIAo9BMREREREFHoNiIiIiIgo8TrQjImpD11771VYPgYgoUBgUExG1oZtv/kGrh0BEFCgsnyAiakPr16/H+vXrWz0MIqLAYKaYiKgN3XijWuaZfYqJiA4PZoqJiIiIKPAYFBMRERFR4AkpZavHACEirR8EACAMABAQAAAJq5WDaVpIpAEAZZmZ9bNIuBcAYNnjdR8jHT8GAHBJz1sAAO88Yr/52bEbdgEAXvfzDgDA/uxj6r59HwAAjBXKAIAfT38NAPBX6z8CALhrdAoA8EjuO+axBtKnAQBeHXolAKA/rs7L/mPkywCAj676OABguqTu/51xdfv7ej4GANhWmDKPtSO0BQBwmnyFGofMOs/3bQBAT/KlAIChkPrbDpZfAACcHXq1eYyf574PAHhv1/sBAN+f+Irfy0PLnnC+zn9Xoz8jmcILizCexu65524A8y+fSMTceuR8cc+CxvLOzivM95vldgDAjpmfL+gxAeDIjjdXPJb+zE7knvK9fyyyGgAgYQMAStaw+VkqfiQAIFvYUXHfkj0BAAiH1H7SskcBuK9PItwDAIiE4uaxRrNPq+eR+Xn8VbSSCKfKtHE8EHa+2rMfQyTUY1RtT9HIIIDK7Rho7rgtRMx5zGKDcS1cs3FEPfHoWgBAobSv6d9p9m/0e32rb6uOk6S0BHwwU0xEREREgceJdkREbeiaaz7f6iEQEQUKg2IiojZ0++23t3oIRESBwvIJIqI2dNxxx+G4445r9TCIiAKDmWIiojZ03XXXAmCfYiKiw4WZYiIiIiIKPAbFRERERBR4DIqJiIiIKPAYFBMRERFR4HGiHRFRG7r66s+2eghERIHCoJiIqA3dfffdrR4CEVGgsHyCiKgNnXzyyTj55JNbPQwiosBgppiIqA194QufA8A+xUREhwszxUREREQUeAyKiYiIiCjwGBQTERERUeAxKCYiIiKiwONEOyKiNvSpT3261UMgIgoUBsVERG3ogQceaPUQiIgCheUTRERt6KyzzsJZZ53V6mEQEQUGM8VERG3os5+9GgD7FBMRHS5CStnqMUCISOsHsUChUCcAoFyernGPsPPVPizjEc75joQ1p99r/HfQfAmRAAAkY6sBANnCjkV89KXbvjoTm8z30/nnF/3xW0GIGABAymKLR1LbPfeoZZ7nGhQvxmd4vvuPWo8G59Ha09KMLxLuBQBY9viiPq6fWETtU4rWAQDAZ478KADgyj/8IQAgvnbM3Hfk4eMBAG/44TEAgK2Z2wAAf3+E+p2X900AAN712PcBAM+c/xoAwD8+qFZX/Pb4N8xj/cnAZQCA+7J7AABXH5MGAEyX1Ofrj7dc7zveJ978evVcP7/P3HZG8mIAQMS5gP3bwo8BAB/o+QAA4MGCeo7nMrcCAP6g6woAQCIszGM8VRhVj5+9yfd5qTm19o8hod7fsswc9jE1Q38OAPezUIuUlvC7neUTRERERBR4DIqJiIiIKPAYFBMRERFR4HGiHRFRG7ryyk+0eghERIHCoJiIqA09/vjjrR4CEVGgsHyCiKgNnXPOOTjnHLZjIyI6XJgpJiJqQ5/+9KcAAHfffXeLR0JEFAzMFBMRERFR4DEoJiIiIqLAY1BMRERERIHHoJiIiIiIAo8T7YiI2tBHPnJFq4dARBQoDIqJiNrQ1q1bWz0EIqJAYfkEEVEbuuCCC3DBBRe0ehhERIHBTDERURu66qo/AwDcfvvtLR4JEVEwMFNMRERERIHHoJiIiIiIAo9BMREREREFHoNiIiIiIgo8TrQjImpDF198SauHQEQUKAyKiYja0J49e1o9BCKiQBFSylaPAUJE5j2IE9PvAgBsyfxo0cbTrHT8GPN9pvBCxW36/8nYRvU10gsAGMs+DgA4Ov1WAMCE3GceYzz7NABAwgIADKXPBAAczDy4NH9ACwkRAwBIWVzAYyScx8gvyphqPIvztXoTDTtf7SV4LiAWGQIAFK0Di/j48yU83y/9/iIc6gYA2OXJJX+udnbhhe8FANx88w8AAJFwPwDAskcP+1hCIm2+L8vMYX/+2Wp9Ll1/MvhxAMC//NGtAIBjv64+UzlrAgDw6vA5AIBfZL8GwH19AeCKwfcBAJ6dKgEAnhJq3zxe2gkAOCHyWgDAatEFAPivzHUAgDckLzOPkYPzu+XfqP8X1Wc5FFL7vk2JN6hxrhkAAHxi278DANZ3qNv/Zv1R5rE++tx3AABnJv4QAHBcsgMA8K1x9Tu9yRMBAJnSMADAstV7FPTPULtZimNWo/2l/uw287kNhTrVfcvTAAApLVHv/isRM8VERG3oiis+CsANiomIaGlxoh0RERERBR6DYiIiIiIKPAbFRERERBR4DIqJiIiIKPA40Y6IqA295z0XtnoIRESBwqCYiKgNjY4e/tZrRERBxvIJIqI2dMklH8Qll3yw1cMgIgoMZoqJiNrQpZeqZZ5vuOFbLR4JEVEwMFNMRERERIHHoJiIiIiIAo9BMREREREFHoNiIiIiIgo8TrQjImpD559/QauHQEQUKAyKiYjaUC6Xa/UQiIgCheUTRERt6IorPoorrvhoq4dBRBQYDIqJiNrQhRe+Fxde+N5WD4OIKDAYFBMRERFR4DEoJiIiIqLAY1BMRERERIHHoJiIiIiIAk9IKVs9BiIiIiKilmKmmIiIiIgCj0ExEREREQUeg2IiIiIiCjwGxUREREQUeAyKiYiIiCjwGBQTERERUeAxKCYiIiKiwGNQTERERESBx6CYiIiIiAKPQTERERERBR6DYiIiIiIKPAbFRERERBR4DIqJiIiIKPAYFBMRERFR4DEoJiIiIqLAY1BMRERERIHHoJiIiIiIAo9BMREREREFHoNiIiIiIgo8BsVEREREFHgMiomIiIgo8BgUExEREVHgMSgmIiIiosBjUExEREREgcegmIiIiIgCj0ExEREREQUeg2IiIiIiCjwGxUREREQUeAyKiYiIiCjwGBQTERERUeAxKCYiIiKiwGNQTERERESBx6CYiIiIiAKPQTERERERBR6DYiIiIiIKPAbFRERERBR4DIqJiIiIKPAYFBMRERFR4EVaPQAAGBgYkEceeWSrh0FEREREK9yjjz46IqUcrL69LYLiI488Eo888kirh0FE1DbuuusuAMC5557b4pEQEa0sQoidfre3RVBMRESVrr76agAMiomIDhfWFBMRERFR4DEoJiIiIqLAY1BMRERERIHHoJiIiIiIAo8T7YiI2tB1113X6iEQEQUKM8VERG3o+OOPx/HHH9/qYdAK8fVfbceLI5lWD4OorTEoJiJqQ7fddhtuu+22Vg+DVoB8ycbVP92C//zdnlYPhaitsXyCiKgNXXPNNQCAt7/97S0eCS13BasMABjPFls8EqL2xkwxERHRClaydVBcavFIiNobg2IiIqIVTAfFE8wUE9XFoJiIiGgFK1kSADCeYaaYqB4GxURERCtY0bYBMFNM1Agn2hERtaEbb7yx1UOgFaKoM8WsKSaqi0ExEVEb2rBhQ6uHQCuErinOlWzkSzYS0XCLR0TUnlg+QUTUhm666SbcdNNNrR4GrQBFJygG2JaNqB5miomI2tC1114LALjoootaPBJa7kqWJyjOlLCmO9nC0RC1L2aKiYiIVjBvppiT7YhqY1BMRES0ghW9mWJOtiOqiUExERHRClaypfl+jJliopqaCoqFED1CiFuEEM8KIbYIIc4SQvQJIe4UQjzvfO117iuEEF8SQmwTQjwhhDhtaf8EIiIiqqXkLZ/IMCgmqqXZiXZfBPBfUsr3CCFiAFIAPgXgbinl/xVC/BWAvwLwlwDeCmCT8+9VAK51vhIRUZNuueWWVg+BVgiWTxA1p2GmWAjRDeB1AL4BAFLKopRyAsA7ANzg3O0GAO90vn8HgG9J5UEAPUKINYs8biKiFW1gYAADAwOtHgatAHqiXSwS4kQ7ojqaKZ84CsAwgG8KIR4TQnxdCJEGMCSl3O/c5wCAIef7dQB2e35/j3NbBSHE5UKIR4QQjwwPD8//LyAiWoGuv/56XH/99a0eBq0AunxiVWecfYqJ6mgmKI4AOA3AtVLKUwFkoEolDCmlBCB9frcmKeXXpJSnSylPHxwcnMuvEhGteAyKabFUBsUsnyCqpZmgeA+APVLKh5z/3wIVJB/UZRHO10POz/cC8K5Put65jYiIiA4zXVO8qjPBTDFRHQ2DYinlAQC7hRDHOzedA+AZAD8BcIlz2yUAbnW+/wmADzpdKM4EMOkpsyAiIqLDqOi0ZBvsjGOc3SeIamq2+8T/B+A7TueJ7QA+BBVQ3yyEuAzATgAXOve9A8D5ALYByDr3JSIiohYo2WXEwiH0pWOYyluw7DIiYS5TQFStqaBYSrkZwOk+PzrH574SwMcXNiwiIiJaDEWrjGhYoDcVBQBM5kro74i3eFRE7afZTDERER1Gd9xxR6uHQCtEyS4jGgmhNx0DoHoVMygmmo1BMRFRG0qlUq0eAq0QunyiJ6WCYvYqJvLHoiIiojb0la98BV/5yldaPQxaAQpWGdFwyJRPjHGyHZEvBsVERG3o5ptvxs0339zqYdAKULIlYpEQek2mmL2KifwwKCYiIlrBSpYqn3BripkpJvLDoJiIiGgFK9plRCMC6VgY0bDgqnZENTAoJiIiWsFKtqopFkKgOxnFZI5BMZEfBsVEREQrWNGZaAcA8UjYLPtMRJXYko2IqA3de++9rR4CrRAlu4x0XB3uY5EQijaDYiI/zBQTERGtYEXbzRTHwiEULbvFIyJqTwyKiYja0L/+67/iX//1X1s9DFoBSpZENCwAOJlilk8Q+WJQTETUhm6//XbcfvvtrR4GrQAlu4xYJAwAiIYFyyeIamBQTEREtIKpFe3cTHHJki0eEVF7YlBMRES0gpVstXgHAMQiYRSYKSbyxaCYiIhoBVPlE96JdgyKifywJRsRURtKJpOtHgKtEN4+xbGIYPcJohoYFBMRtaGf/exnrR4CrRAlW1a0ZCvZrCkm8sPyCSIiohVKSomit3yCLdmIamJQTETUhj7zmc/gM5/5TKuHQcuczgrHvH2KOdGOyFdTQbEQYocQ4kkhxGYhxCPObX1CiDuFEM87X3ud24UQ4ktCiG1CiCeEEKct5R9ARLQS3X333bj77rtbPQxa5kpOAOyWT4SZKSaqYS6Z4jdKKU+RUp7u/P+vANwtpdwE4G7n/wDwVgCbnH+XA7h2sQZLREREzasOiqMRwaCYqIaFlE+8A8ANzvc3AHin5/ZvSeVBAD1CiDULeB4iIiKaB10qoWuK42FVPiElJ9sRVWs2KJYAfiGEeFQIcblz25CUcr/z/QEAQ8736wDs9vzuHue2CkKIy4UQjwghHhkeHp7H0ImIiKgenRV2F+9QX9mBgmi2ZluyvUZKuVcIsQrAnUKIZ70/lFJKIcScPmFSyq8B+BoAnH766fx0EhF59Pf3t3oItALo4DcacSfaAajoSEFESlNBsZRyr/P1kBDiRwBeCeCgEGKNlHK/Ux5xyLn7XgAbPL++3rmNiIia9MMf/rDVQ6AVQNcUx8JhAG5tcdEqA/GWDYuoLTU8TRRCpIUQnfp7AL8H4CkAPwFwiXO3SwDc6nz/EwAfdLpQnAlg0lNmQURERIeJLp+IhiszxSW2ZSOapZlM8RCAHwkh9P2/K6X8LyHEwwBuFkJcBmAngAud+98B4HwA2wBkAXxo0UdNRLTC/a//9b8AAP/4j//Y4pHQcqYn2kUj7op2ANiBgshHw6BYSrkdwMk+t48COMfndgng44syOiKigHrggQdaPQRaAUpO8BuvmmhXYFBMNAur7ImIiFYoZoqJmsegmIiIaIWataKdp/sEEVViUExERLRCFS3Vkm12n2IGxUTVmu1TTEREh9H69etbPQRaAdwV7ZzuEw3KJyy7jJGZIlZ3Jw7PAInaCINiIqI29O1vf7vVQ6AVoGTVKJ+oERT/5PF9+NSPnsSjnz4P6ThDBAoWlk8QERGtUNU1xfprre4T+yfzyJfKmClYh2eARG2EQTERURu68sorceWVV7Z6GLTMmRXtnAxxvEFNca5oAwAKJdYcU/Dw2ggRURvavHlzq4dAK0BhjuUTWScoLtr2YRgdUXthppiIiGiFKtn+3SdqtWTLlVTZRJ6ZYgogBsVEREQrVHX5RLRB9wk3U8ygmIKHQTEREdEKVbTKCAkgHHJasjWoKc6yppgCjDXFRERt6Ljjjmv1EGgFKNllkx0G3DKKWt0ncswUU4AxKCYiakNf+9rXWj0EWgGKdtlkh4HGi3dki6qmuFDiRDsKHpZPEBERrVBFq2wCYQAIhQSiYVFnop26nZliCiIGxUREbejyyy/H5Zdf3uph0DJXXT4BqMl2tTLFOSdTXOvnRCsZyyeIiNrQ1q1bWz0EWgFKtqwonwDUZLuGE+0YFFMAMVNMRES0QhWtMqJhUXFbrG6m2Da/RxQ0DIqJiIhWqKJP+UQs4h8USymRLelMMSfaUfA0HRQLIcJCiMeEELc7/z9KCPGQEGKbEOImIUTMuT3u/H+b8/Mjl2jsVGXboWnkOWOYiIgcparuE4DKFBd8yieKdhl2Wa2Ax0wxBdFcMsX/E8AWz///CcDnpZTHAhgHcJlz+2UAxp3bP+/cj5ZYvmTjbV/6NW5+ZHerh0JEi+CUU07BKaec0uph0DJXsiu7TwBOTbFP0KtLJwDWFFMwNRUUCyHWA3gbgK87/xcA3gTgFucuNwB4p/P9O5z/w/n5Oc79aQnNFCwUrDLGMsVWD4WIFsEXvvAFfOELX2j1MGiZUzXFPuUTPpnirCcoZqaYgqjZTPEXAPwFAP0p6QcwIaW0nP/vAbDO+X4dgN0A4Px80rl/BSHE5UKIR4QQjwwPD89v9GRkC5wcQURElYq2RNSnfMLvWJFlppgCrmFQLIS4AMAhKeWji/nEUsqvSSlPl1KePjg4uJgPHUjZEntLEq0kH/jAB/CBD3yg1cOgZa5k+ZdP+B0rWD5BQddMn+JXA/h9IcT5ABIAugB8EUCPECLiZIPXA9jr3H8vgA0A9gghIgC6AYwu+sipQqbA9eqJVpI9e/a0egi0AqhlnisrGKPhEGYK1qz76iWeAXafoGBqmCmWUv4vKeV6KeWRAN4H4L+llO8HcA+A9zh3uwTArc73P3H+D+fn/y2llIs6apqFvSWJiKia34p2tTLF2RJriinYFtKn+C8BfEIIsQ2qZvgbzu3fANDv3P4JAH+1sCFSMzJcmpOIiKrULJ/wuaqokyshwfIJCqY5LfMspbwXwL3O99sBvNLnPnkA712EsdEc5Lg0JxERVSna5VkT7eI1Jtrp40h3MrrkCZabHt6FM4/uxxH96SV9HqK54Ip2K4TOFDMoJloZzjrrLJx11lmtHgYtc0WfTHG0VvcJp3yiJxUzP88VbVz5/cdwaCq/aGOyyxJ/+cMn8cPf7W18Z6LDaE6ZYmpfpqaYE+2IVoR//Md/bPUQaAUo2XL2inaREEq+5RMqudKTipqJds8emMKPN+/Dm04cwu+fvHZRxqQDbr8xELUSM8UrRNZMtOOMYSIiUop2GdFwZfeJmhPtvOUTtpspBoCpXGlRxwQAFoNiajMMilcITrQjWlne/e53493vfnerh0HLmF2WsMuy6RXtckUb8UgIyWgYhZL6uQ6Up/KLGBSbTDEbU1F7YVC8QrB8gup59sAUPnHTZthlHoSWi9HRUYyOssU7zZ8uT5gVFIdDKNkS5ar9QbZoIxkLI+4JmnWd8eQSZIpZPkHthkHxCpHhMs9Ux/3bRvGfj+3FaKbQ6qEQkccTeybwyR88PitAXQw66Iz71BQDs5Mo2aKNVDSMWCRkMsW6zngqN3uxj3mPy9LlEzxJp/bCoHiFyJXYfYJqs8pqu+BJE1F7ue+5Ydzy6B5M+6wwt1D68+6XKQZmZ2pzJcvJFIfdTPFSlE8wU0xtikHxCsFMMdWja/dYw0dB88y+KTy2a7zVw6hpxsnELkWAqD/vfjXFwOzjRbZoIxWLOJli29wGLPJEO11TzHIuajNsybZCcJlnqoctkJafc845p9VDWBH+6b+exWSuhB9//NWtHoqvjJMhXopSAv1592vJBviXT1TXFLP7BAUJg+IVgt0nqB6WTyw/f/M3f9PqIawI0/mSCezakb7KtxQnrHkn21tdU6wzx9X7g1zRRn9HzOljrCbi6WPLVH7xa4p55YraDcsnVgizzDPPvMmHPviwOwkFTbZot/UVkmkn2FyKz2bGOS6k4+GK23WmuPp1yRYtpGLhikzyUmaK2/l9oWBipniF8GaKpZQQQjT4DQoSffApMVO8bLz1rW8FAPzsZz9r8UiWt2zRbutWhEtZPpF1jgvJaOWhXk+0q56YnSvaSEYjiEdUEF0olU1N8WSutGjHFp2h1lewiNoFg+IVIuu5PKiW9WRQTC4dFDNTvHzkcrlWD2FFyBZttHOOILOEE+2yTmlGKlaZKY7XmmhXsisyxQXbNscWqyyRK6mJeAtlTtJZPkFthuUTK4CUErmi7Tn7b9/6OWoNy3SfYFBMwZItWm1dSz9TWLryCb3wRnX5RL2a4pQz0U7/XLf7BBavV3GBE3+pTTEoXgGKdhlWWaInFVX/b+MDALWGPuAWLWZmKDjKZYls0W7rfeJSlk/ohTeSVdldt6bYfU67LFGwyqb7BKCCV+9VyEa9iidzJewazTYcl35eLt5B7YZB8QqgJ0KYoHgRzr6f3DOJx3aNt/XBhJpncaIdBVDeuWrWztv9TH7pyid0Z4t0zH+iXdF2A95cyS21qMgUO9ljoPFSz1+6+3n8wbW/abg6H1tEUrtiTfEKkDFBcQzAwjPF5bLERV97ANmijUQ0hL96ywm49NVHLXic1XaMZPDk3km8/eS1i/7YVIkT7ZafCy64oNVDWPZ0UGiXJeyyRDjUXsXFquXZ0gXuOtBNVgfFPuUTWU9WOVaVKV7dlcD2kUzDDhTjmSJGZorYMZrB0YMdNe9XYvcJalPMFK8A+hJZ7yKVT4zMFJAt2njPK9ajOxnFb14YXfAY/Vx//w588gePL8ljUyUehJafT37yk/jkJz/Z6mEsazrQA9pz29c1v8DSlBJkChYiIWGCYM0b9Gr6imMyGjbdJ4pOUDzUlQDQuHxCP97m3RN17+d2n2D5BLWXhkGxECIhhPitEOJxIcTTQoi/d24/SgjxkBBimxDiJiFEzLk97vx/m/PzI5f4bwg8nQ3pdTLF1W125mrfZB4A8NaXrsYRfWlzeW+xDU8XUHBayNHSYp9iCiJvPexC94tLQdcTA0vUfcJZoa66jZp/ptgtn3CDZhu5ooU13Soonsw2CorVYzzeKCg2K9px30/tpZlMcQHAm6SUJwM4BcBbhBBnAvgnAJ+XUh4LYBzAZc79LwMw7tz+eed+tIT0zqx7kWqK902oVlBrupPoSETM7OjFNjxTAMBsweFgWrK1YWBA/t7whjfgDW94Q6uHsax5M8VLue1LKRtmR/3MLHlQbCHt00LNb6KdPo5UTLQrlZEt2Rjq1pni+scCkyneM1n3fqwppnbVMCiWyozz36jzTwJ4E4BbnNtvAPBO5/t3OP+H8/NzBFeSWFJ6x9+TXJyaYh0Ur+tJoiO+dEHxyLQKihmoLT23JRtPQCg49FU0YGmvkjz04hje+eXfYMv+qTn9nvcq3FJ8NrOeSXJeZqKdp32nLp9IRd1M8XShBCmBzkQEqVi4YU1xoaRe4y37puq2BuWKdtSumqopFkKEhRCbARwCcCeAFwBMSCn1J3oPgHXO9+sA7AYA5+eTAPp9HvNyIcQjQohHhoeHF/RHBJ0+w9c1xQsun5jIIxULoysZQUciYpYhXWzDTlDMHePSKzJTTAHkLZ9Yym1/PFMEAIw5X5t1uMonqnmXcXbvq8aSirkr2o1nVBCciobRlYg2rCnOWzbCIYGiXcaW/dM176cn/LJ8gtpNU0GxlNKWUp4CYD2AVwI4YaFPLKX8mpTydCnl6YODgwt9uEAzmeJFmmi3fzKHNd0JCCHQGY9gprB4a95ruaKN6SVsWk+V9HKqPAGhIDlcE+1067fMHK+qtap8IhpWF2+9xwpvpwodNE84meFULILuZLRhS7ZCqYyXrusGAGzeNV7zfiZTzGWeqc3MqfuElHICwD0AzgLQI4TQn7b1APY63+8FsAEAnJ93A2jYvmDHSAY/f/rAXIZDjuwit2TbN5nH2p4kAKAjHkG+VF70HfaIU08MMHt5OJQsrmhHwXO4MsW5onrsXKl2yYCfTHFpyydytTLFeqKdT02xt0/xRFZlvpPOlcNGK9oVLBtH9KWwqjOOx+vUFbs1xcwUU3tppvvEoBCix/k+CeA8AFugguP3OHe7BMCtzvc/cf4P5+f/LZtoL3DDAzvwiZs2z2Xs5HDLJ5yg2J7bjrnavokc1nY7QXFCnffMNQPSyKFpNyjmjnHp6YxMO87AJ38XXnghLrzwwlYPY1nzZoqXctvPO8GwNwhvxoyn5nlJFu8o2rOWeAYAIVSbtkbdJyayJXNbM+UTBauMRDSEUzb01J14qDPFdlmy+xC1lWYW71gD4AYhRBgqiL5ZSnm7EOIZAN8XQlwN4DEA33Du/w0ANwohtgEYA/C+ZgYyk7eQKdoolyVCbdZgvd1lixbCIWEC2IVkRAqWjeHpQkWmGACm85bJRC+G4YqgmIHaUmOf4uXnYx/7WKuHsOxVTLRbyqDYmmdQ7J1otwTjyxVtJKP+h/lYpDIodpeEDkPHqeMVmeIonjtYu04YUCcH8UgYJ61N4hfPHETRKpsA28v7vCVbIhbhMZ/aQ8OgWEr5BIBTfW7fDlVfXH17HsB75zoQvTPJlWyk41xoby6yRVvNGPbpPTlXBydVsLqmR7Xg0UHxYnegGGb5xGHldp/ga71cZLNZAEAqlWrxSJYvbznDUs5dyDtdF3KezPSe8SyGuhKIhmtfkM0ULOjeTEuTKbZ8M8WAExRXLfMcrlrow80Uq5riht0nrDLikZA5buRKtm9Q7P1bS7Z/4EzUCm2zJeraKm+NFTUnW7CRiod9Vymaq32Tqh1bdfnEogfFnkwxJ9otPfYpXn7OP/98nH/++a0exrLmLfta0kxxVflEvmTjvM/9Ejc/srvu780ULHTEIoiGQigtQb/2Wt0nADXZzvuaZAoquSKEMOUVEzmVKVblExFMFyyU64xTlU+EkYiGnf/7Z869z8sOFNRO2iYozjqXubKFhdXDBlG2ZDttdBYeFO/XQXF1pniR27JVlE8wUFtynNhCQeQtZ1jS7hNVQfFkroRcyTY932vJFCyk4xFEw2LR94OWXUbRKvt2nwBUpti7P5jOW+hMuPeNR0KYcFqyJaOqfEJKYKZG4sqyy7DLEvFIqGLxDz/e52UHCmonbRMUM1M8f9mCpSZHLEL5xL4JtcTzGidTrHeS08wUL2t61UC+1rTYdo1mKya0tZNs0fIsVHE4gmL1Ougra426NWSKFjoSEUQjoUUP2rMld+Kcn+qJdtP5EjoTUffnkZDZ76ecmmKg9lLPOhkTj4aYKaZlq22CYn2GPdeJCuSuWhQKCUScxunztW8ih95U1Fxy64irHeGiZ4pnCuhyAm7WuS49TrSjpfL7X/41/v2XL7Z6GL6yRdssarS0QXHZPB/glm006tYwnVeZ4sgSlE/oq661yidikXDFVUW/TLGWikXQ5QTMtf4mfWIQj7gt3fI1MsWFqppionbRNkGx3oksduuvIMgWLaScS2TxqhnFc7VvImc6TwDemuLKHeHITAH3Pndo3s8z4ulwUbSYKVhKUkpzuZI1xbSY7LLERLaEnaOZVg/FV7ZooyepuuYUljD40hP69FLJbqa4flCcKVjoiIcRW4LyCZ21rlU+EY+EKjK504VSRVCsM+xCAIloCF1J9bNa2W+TKY40zhSXLAbF1J7aJihmpnj+vOvbV7fZmav9k3lTOgHAmXhR2U8TAL7/21344+sfNgeBuZBSYnimgPW9TlDMneKSsjwZKB6Alo9LL70Ul156aauHUZfODh6czrd4JP4yBWvRVvqsp7qmWLeCm2pwhS1TsJGOLVH5RLF+prirqpuEyhS75RN6qeekM/muUaZYB8WJaONMsXefby3BBMN2N5Yp4rbH97V6GOSjjYJiZornSwXF6ix+oUGxyhQnzP9DIYGOWGRW+cR03kJZAofmcTCcylsoWmWTKeZEu6Xlrdkrsn5v2VgOQbEOhA5OFRrcszWyRfuwBMV6Qpmu4800mSmeKaia4khILH75hBMU18oUdyejFUF7dfmEzhTrhEu3rimu8TfprLA3U5yvscJfyS6bxw3i1atbN+/F//e9x7C3wURMOvzaIiiWEtD7g7kuk0m6fMKTKZ5nxiFTsDCVtyoyxYAqoagunzAZonkcDPUkOxMUM3u5pLzbg/cA9Ltd43XbK1FrjYyMYGRkpNXDqMvdD7RnpthbPrGk3ScsXT5RNdGuQU1xpmihIx5BNBxasvKJmpniRMQEuFLKWRPt4iYoVoGynmhXK9DXWeHKiXY1MsVW2TxuEDPF+qTphUMzLR4JVWuLoNj2LPOYYUu2Oct4yyfCoZp1XI3oHaSemKJ1xCOz+hTnFnAwrA6KWT6xtCyfSS3bh2fwB1+5H/dunX9dOC2t97znPXjPe97T6mHUpYPi6bzVlh0oskULXckIQmJpM5K5qrIJHfTUyqoCKhCdcSbaxZawfKLW4h3dySgmcyVIKVGwyijZsm6muDOuXseamWLfiXa1u0/ocVkB3P/rE4gXhhkUt5u2CIq92ap23LG2M7ssK866Y5Gw2flnCtacan51oFudWehIRDBdVT6hP9SHpueRKXZWs1vnlGkE8fLZ4VTRE9Q5AOnlW/dPtmeGj5YHbybwUJuVUJTL0lm8IrKgK2jNMJniqvKJfKlcM0lRsMqwyhIdcVU+sdgZUx0Up2os89ydjMIuS2SKttm/d/l0n9DHg1BIoMsJpP24NcVNZIrtMpLOfYKYFNHbCYPi9tMeQTEzxfOmTyK85RN6R/TRbz+KT9y8uenH0gG03llpfplinQE4tAiZYi4osbR0ICw82TJ9UjOeKbZsXPM1MlPAk3smWz0MQmUmsN1KKHSgmnZ6uC/myfeu0Sxu3bzXfS7Tkk2XT3i6OtSYbKcD53QsjOgij887llSdTDGgMr/TTplHR51Msf6diUZ9ipvOFDvlEwHc/+vX5YVD7dm1JcjaJCh2v2emeG5MNsDZ8Xlbsu0czeK+rcNNX5bL18gUdyZmT7RbaPlENCww2BEHwEzxUtPvfzoWMd/rE6DxGge4dnbdfS/g0m/+ttXDIFR2Fzg4j6tGS0knWFLxyKyevAv1vYd34RM3Pw7pJHTyZnnnMsplWTFhvFYNrh5fRyK6pOUTtRbv6PYsxqED9864X/cJN1DuqZsp9ptoV2tFO3einRXAFe2YKW5fbRIUezLFbMk2J9U7vrjnMuFEtohs0cYTTWbVTPlEE5liPdt6vhPtBjriiIRDCAlOtFtqOhOfirmlNfq91mUUy8l4ttRwAhMdHt7SgPlcNVpKOVM+EJ5z//bfbBvByEztfVumYMF2yjMAlZWOhIR63pJdsRRyrbZsep/aEQ8vTfmE8/iJSO2WbGp8nqC4Qaa4KxnFRKOJdp5MsV/piGWXUZZuV4wgXikseMoPp7kvayvtERQ7O4NwSJgPMjWnuuRBXya0y9Is0fnAC83NYNePlZgVFEdrZ4rn0ZJtLFNAf4eaER4NL36GhCqZTHE8Yk6YTFC8DMsnskULJVvCXuGz1q+44gpcccUVrR5GXRWZ4jYLijN68Yp4GNGwaHo/U7TKuOQ/fotvPbCz5n1ynpXr7LJaHKc3rfZp2aLdVKZYB8Vpp/vE4pdPuCud+vErn6he5hmoDIp7UrGaf4/JFEdDCIUEYuGQb6bYnKQ7VzeDuP/3dtnaPhycEoq/vfUp3N9kPNIq7REUO5nigY6Y2ZFRc/JmR1S5eMd0vgSdgH9g+2hTj1Vvot1M0aqYEKlLLYbnkSkey5bQm4qZ8S7mZU2aTR90UrGwOSDlS8u3fEJfdl7pZTcXXXQRLrroolYPoy4dCAmxsF7Fzx2YXvTSOXe+RWRO/dvHMkVYZVn3hFHvK2cKlvks9TtBcc4Jigec8rBaVzUy3qB4KconSnbN0gmgOiienSmunminfieCiRpXl3T2U2em41H/Tkj6fdCZ4qDWFOsuT0EpoZBS4lsP7MR9zw23eih1tUlQrL4Odsa5ot0c6R2y3hHpIFPXfQ10xPDIjvGaEx78Hqu6fKIzHoGUbmN6wD0oTBesOS+4Mp4pos85gMSYKV5y+rJsOhaBXVYZ1vwcyie2D8/gnmfbp3WbDnaa2aaXs927d2P37t2tHkZdOhBa05WY10I+gAqsf//ffo1vP1g7Mzsf3tKyuXSf0GUT9Up0vMs5632hPtHPFC3MFGyzCFKtGlydKe6MRxBtsnzi0HS+6cvt2YJluhL58fYd1n9rV6NMcTKGyVzJt7+5mWgXVb8Xj4R9M8UFu3IeTBD3//mSjeNXdyISEoEJigtmknd777fbIii2Taa4fYPiF0cyOO9z99WtM2sFc3bu7Ih0+YSeIXzeSatRsMp4bNdEw8fK1uo+4WQPvCUU+ZLbUmeubdnGM0VzAGH5xNLTiwJ4D0K5YvPdJ7563wv45A8eX7oBzpHOFK/0KwwXX3wxLr744lYPoy59pWpjf2reLdmmchYKVhkHJhd332om2sUic+o+YYLiOj2G/TLFfVXlE2u6E87jNOg+MYfFOz70zYfx2TuebervyBbrZ4o74xEIp++wqW+uyBSr3/UG1t3JKMoSFTXTmn4dYmF1LErUyBTrq1VBrinOlcroiEexsT8VmA4UevuoNfmyXbRFUKzPOvvT8batKX5q7ySePzSDF0faawM2mWJv+YTtZop/7yVDCInmSihqlk84rXO8q9rlSzaO6E8BmFstYckuY7pguZniBS5LTY3p5WPNsqp22bzXU3mrYfP8/ZP5tiprCkqmeDnQJ+Ub+1LzrinWmc+J3OLWt2c9NcVz2c+MzKhx1Ft4I+tZrEMf5Ps85RMzTvlENCxqZpxnqsonmlmCfc94DrvHsk39HY2C4lBIoCsRxZRTPpGOhRH21B+b8glPkqQ75XasqFawyoiFQ6aGOR4Jme3DS78PQe4+USjZSMbCOGawI3CZ4vkuLna4NAyKhRAbhBD3CCGeEUI8LYT4n87tfUKIO4UQzztfe53bhRDiS0KIbUKIJ4QQpzV6jrJUH7zORKRtu0/omqu5lgosNZ2p0UFx3Fm8Q88Q3tCbxMvWdePBFxoHxfmiDSHcnaGmswfTFZliGxv7VFA8l0yxvlyvJ6WoCTDByxQcTiZTrDMzVrkioKw1m1wbni4gXyqb9lOtpgOSlZ4pXg70dnREfxoZJxicK71f8Qu0FsJc+YqFVUu2Jq9IjZryidp/S95kiks+mWILMwW1fLMOOv3MePsUh4RvcHjLo3tMDa9dlpjKl5q+Wpkt1i+fAICuZMRMtPNOsgO8yzxX9ikG3BOG//G9x/DFu54HoIIdXToBqGOS34mrd+Kv+n977FcOp1zJRiISwjGDHdgxmgnEqn4rKVNsAbhKSnkSgDMBfFwIcRKAvwJwt5RyE4C7nf8DwFsBbHL+XQ7g2kZPUJYS6XgYqVi4bfsU62zGXFaIOxzy1eUTTkZE77S6kzGcurEXT+2bbDhbP1eykYyGIUTlbOVOkylW703JVisx6UzxXFoxjWcql5KOhpd2pSny9in2ZIo923GjEgqdAWyXINQNitvrsxhEectGSMDUz84nW6yD4kYnZ3NlMsWxCGJh0VR5AjDX8gl7VlA8lbfM4hRdyWjN4DpTsJCMhhEJh3zLJ/ZN5PDJHzyOHz+mFgnRk6dHm+wY0yhTDLhLPU/nrYpJdoBbU5ysqCmuDIp/9fwwfrdrHIDaP8Q97d8SUf/e0NWZ4iCWz+WdTPHRg2mUbInd47lWD2nJ6Vgl3+b77YZBsZRyv5Tyd8730wC2AFgH4B0AbnDudgOAdzrfvwPAt6TyIIAeIcSaes9RLkukYhGk4xGUbGk+NPPJOiwVveNut5pnv4l2RbuMCWfH2Z2M4iVru5At2tgxWr/0QwfF1aprivVzrupMIBENzelAOOaMq8+pKZ5r/1CaO1M+oTMzlqxoCVSvA0XBss3P/S6FHm5SSlPK0e4ZhyAolMpIRMMY6lxIUOyUT8yhZ/a+iRwuu/7hupPOdE1xMjq3iXajTvlEvYl2WU9LNr0d6qtfesXOdDyCrkSkZnA9mim6yYFIaFbGVD+O3mfqeSLjmaLvRDe/Mc4lKO6oCordTLGnptgZ70RWZcjHs247t0KpXHGVMR4J+WaKi3bllasgZEmr5Uo2EtEwNvSqxNK+iSAExXbF13Y1p5piIcSRAE4F8BCAISnlfudHBwAMOd+vA+CdMr3Hua36sS4XQjwihHgkVyggFQubD3C2aOG3L47hlL//Bfa2ycaiP/jtlsl2M8Xu4h2AynboWdcnre0CADy9b6ruY+WK5Vk9igG3plj3PdYBVSIWxlBXYk6tmGaXT3Ci3VIrWbMzxfmSKpUB6neg8E6eaoczfFXGob5f6Zniq666CldddVWrh1FX3lIH91VdKiiez2Q7Uz4xh0zx73aN4+5nD+G5A9Ozfla01Kpy+iRf98xt9uR72MkU50vlmtuYXsFuJu+ZaJeqDIo74mEnU+z/dx2cymO1MxlPXzHzlijpYHjM+XzqTLrllFE0ki3a5kS4Fjconl0+Uav7BKDeK30CpN+/vE/5RHOZ4mCVT0gpkS+VkYiEzPt/YLK9enwvBf05aZcrjrU0HRQLIToA/BDAlVLKiuhKqk/ynLZsKeXXpJSnSylPj0ZjSMcjZjZqpmjjuQNTsMoSOxtkNw+Xds8U62BYz/w9NF0w9V+bVnUiGhZ4el/9le30JZ1qeulPnSl2+1GGMNSZmFN2SAdgfXMMii+7/mHc8uiepp+HXLpWUWdmipaaaLeqU/VRrVc+4W2z1Q5n+N4Jfys9U/z2t78db3/721s9jLryTnZwqEttS/PJFE+ZTHGp6bp1vR/2Cw7P+/x9+NydW5EpWEjHK/u3N0NnigH/zhFSStOecqZgmZNFvU/TQbUun6gV7O+f9ATFzuQ0b4mbLuPQJWfeTPrITOOserZoIeWT5PBSQbHlWz5hlnn2qSmeyBVNIDftOS54yydqZoqd9yEWCTkr+a3sz3E1HRSqpJL63Bxos4VvloLbkq293++mgmIhRBQqIP6OlPI/nZsP6rII56tuZLoXwAbPr693bqvJLkukYmHz4csWLDN5a2KJFhe485mD5oy+GVNLEBTPFCyc8Q934dfPz3+Fl7xlIxZxZ/zqs3tvUByLhLBpVSeeaZQprlE+oQ8sM9WZ4mgYq7ric3oddQDWk3LH1uhgJaXEvVuH8Ztt7b0STrsq2pXdJ0pOpnhNdxJA/fIJ71WAdtiZZQvu52+lZ4qfe+45PPfcc60eRl0FS11d6ohHkIqF57WAhw6qrLJsumRO18RXB63lssTO0Sy+9cAOjGeL5pgy1z7Fej/oF3R7V1PMFCwzls5EBJGQwLBzIpk2E+38A+sDk3ms7lKfwaiz3/ZmTXXtsE4keIPrsQZ1xTpT3qh8oiuhMtnTBQtdVUHxyRt68LrjBrFpVYe5LRENIRYOYTJXMoGcfo0Klm3mtqj7hn2vLukkSEzXUgcsU+wteUzFIuhMRNpuNcjFIKXE3VsOms+KyRS3QXKlnma6TwgA3wCwRUr5Oc+PfgLgEuf7SwDc6rn9g04XijMBTHrKLHxJZx10HXxlirbZSBp9+OcjW7Rw+Y2P4Et3P9/07yxG+cS2Q9N471fvN4+1byKH4ekCnj80+xJgs/JFNYtVc4PivAmKAeAla7vwzL6pupmYbNHyDYoj4RCS0bA5YHkX+Vg1x0zxWKaEjnjEZBTUZcPGEwDtsgzEJaaloGv2TE2x05KtLx1DPBKqWz7hfW/bLVPcDjXOS+kjH/kIPvKRj7R6GHXlSzbikRCEEFjdlcCBqbmXu3m72jSbBNHJieosrM7gTuUt3LXlkLn6GAuHm8oUl8sSY5kijhpIq8fxyfJ66/EzRQt5z6IVqVjYUz4RQVcy4htYTxcsZIs2VnerTGHUucLnDdxN+URVTTHgdsioJW/ZkBINyye6klEUrTLGMsVZ5RPrepL41h+/suJ2IQS6U1FMZkvY7+yPs0Ubll12Jtp5g+L6LdlikRAic1h+e6XQyQV9wra6a27H0KVwz7OHcPLf/2JRu2s9uXcSl93wCB5wOl/lTaa49ceReprJFL8awMUA3iSE2Oz8Ox/A/wVwnhDieQDnOv8HgDsAbAewDcC/A/hYoyewpUQqHjaXdyszxYsfFB+YzENK4JfPN7/c4GKUT/xu5wQe3jFu1jrXWdOFPGa+VFkHrHdKw9MFk40FVFA8minWzeTkSmUkamQWOhIR8xrozEgiqi7/zKUV03i2WDGuWMTdKUop8dTeSfz86QO4dfNekwnUz9vqHcdyNav7hKW6TySjYfSlY3XLJ7zbS64Ndmbek9J2qHEOunzJNkvMb+hLYedocz10vbyT5ZqtK84520F10Oo9qBetssmURiPCBJxP7pnE3976lG83nslcCVZZ4qhBJyh29j33PHsI920ddp7b3e6m85bJfCWj6hhmJtrFVKa4WNUCEQAOOgHlaudqTTSsrvR5J52Z8ons7KB4pEGyyLuaXz06cWKXpeky1IiuQ/YmKWYK1qzuE2pFu9oT7aJOpjhoyzy7V1rVsXp1dwIHFrBE+lyU7LLvScgLwzOYzJUqSocWSm+v+qRwudQUN/wUSCl/DUDU+PE5PveXAD4+l0GUpVSZYh0UF21zMB7LLH75hL7ss3M0i52jGRzRn274O9MFnSme/4FYB4564oS+bL2QMyc90UXTmeJ8qVyRKT5pbTcA4Jn9k6aObdZjFW2sdmqcqnXGI26m2NJnuiEMdKj7j0wXzIS8esY8SzwDqJgA8+D2Mfzhvz9ofnbdxa/Am1+y2hw0D0zlIaWc1TKO6iuZ8gmnptgum5OpnlSs/kS7OdYU/3LrME5c04XBTv/taKEy3vKJFZ4pXg4KVtlcqTqiP4Xf7Ryf82d0IZni6iysfqyTN/Tg8d0TZpuPO/sZKSXufOYAvvXATrxu0yDOPWmo4vdHM+q4c4yTKdZB+jV3PodEJIzXHzdYmSn2rGiXiKrJ4vr40uHUFOtxevfTOsu6usudaAdUlk/oDPF4RtVaT+SKSEbDyJXshpnirGc1v3q8x4jqmuJaepyg2HvRUZ8cxD2f+3g0VHeiXTwScvrUB+tz7L3SCgBDXQk8f/DwlAb+z+8/hmg4hC++79SK2003lUVsJKATGNV95VdCpnjJlZ2a4pQpn7BMXdZSZIq9Gcdfbm0uW+xmiue/0eigUmfmJnOLkSmurOPSE+0AoCflBp8nrukEADy9t3ZdsapB898xdiQimKnq1RyPhN12bU1miiey7hLPQOVEOz1B5ep3vhSAe1DwZul1B4wv37MN//DTZ5p6zqBzm+W7s73VpMoQelPRujXFh6YKZvtqVFNcsGx86PqHceMDOxZn4D6YKW4vhZJ7Un5EfxrTBWvOJW/ThZLJUja7qp0pk6iq19WZ4kvOOgKJaKhioh2gtn2d/b3BZzsdnlbPf/Rgh/P4zgn5ZMHs4/Q2GBLqJC1XshEOCUTDoYpJael42NTpVo9TZ1nXdFcHxe5nTGftinYZmaKNyWwJ/R0x9KSiDV/jbEk9X7OZYgCzyifq/c5EtlQxOWwqXzL15Vo8orpPVJfseTPFkVDzNcVbD07jFZ+5E5+9Y0tT3TfalT6p0ldYVnclMDxTaLiOwGLYOZr1XUFPj2kx50zpBIZ+bH1FJd/mmeK2CIol1BmtzhRP5Upmdu3YkpRPqOBrsDOO+7Y2PkOTUtYtn5gpWE2d7eodtg5C9NfFLJ+IeWq6qnd4R/Sn6rZly5Vs35ZsgMp6mFnGlrtSlDe734yxbGWmWPXnVK+dviT6qqP6ALgHJG8mSV92vOPJ/bjzmYNNPWfQWbZEOCTMpU3dfSIZDaM3PTtT/OV7tuEHj6iuigen8mblwkYT2w5NqR17vSB7oZgpbi95T2/aI5ztZGeTyxBr03kL653fbTZTnKuRKdb72LU9SXzxfafiI68/BoC7XyzaZfM7v3p+ZFaAoDPFpqY4X0LJLmM0U3BbjzkH9/6OuOo+UXKz5amKoLgyU+ylA8pVXbqmWGXWK4Pigll2eTxTxESuhJ5UFP3pWMPL3BmTKZ5LUNxk+UTKLZ9Y16PKP0ymuKqmGJh9ubzkqSmOhpvvPrF51wRGM0V87Zfb8cZ/uReP7hxr6vfazaxMcXcCdlk2vVKh9pPH9+H1/3LPnPo8Z4u272dMf54Ws+WszjrnipWfG7ss2/rqQFsExYA6q9aZYm9d2lIcYA9M5tCZiOC8k4bwwAsjDSdg6IlegH/w97Yv/Qpfvmdbw+edrsoU62BkQeUTJdss3AHUDooBVVf89P7abdnyRf/uE4CapexXU2wmRzZbU5wpVWSKY2H3Ept+bQc64giHhDmQeINiXUKxczS7qPVPK1nJLiMaFubAW7RtcwLUm4rOqin+9oM78dX7XgCgg2IVIDTaTnWpRb0FFRbKu9Nu99q0hfr0pz+NT3/6060eRl0FT/nWkQNOUFynjabfRN/pvIUNvSq4aramWG8H1ffX2dyOeARvfslqnLaxF4B7Ba1olTGVs7C6K4FoWODGB3ZW/P6IUw+8rjeJWDiEqZyF4ekCpPSuaqq2u0ETFLutLJNOkiASEohHQuhKVK4Ap+2fzKM/HauYcAy45RNSSoxmimbV0PFsERPZInqSMfSn4w0DKO/rUE+X5xhRvXhHLd1JlakeninguCGVUZ/OOzXFnquW+m+rPnl1M8UCkTn0qd87kYMQwI8+dja6klFcdsMjvlnPducttwGAIafkZK4TyZ/dP4Wdo9k5xUiZguUbFFeXOiwGfWKmH9N7pbGd991tExSnYhHTU/HFEbVT7UxEGi5BOx8HpvJY3ZXA6zYNIlO08ZizTGUt3qCseqORUmLXWBaP7qz/GIA3U+xMnMgsvKNFftaOyFs+URkUH9mfxv6JfM0OFDnnkrof7yxq75luR7z58omCpSbk9aW9E+3cnaJ+bZMxddlRH0hmCu6H+MBkHqOZImYKFqY99XxUW9EuIxoKmQPvTEHNTE9Ew+hLxTCZK5mTPrsscWi6gBeGM9g5msFU3jIH5kblE/oKjPfz0qx8ycb/+N5j2N0gy5hxtpFoWKz49/7cc8/Fueee2+ph1KWuVKntan1vCkK4SY2n96lJs9qvnx/By/7uF7P26dP5Evo74khGw02Xy9WqKa4VDMY8V0mm8iVs7E/hbS9bg1se3VOx/x3NFBESQG8qZvZ5utxupmCpHsXO/Qc746olW8k2AaCezJqOR1SnhqR79dPLu3AHMLt8IlO0UbDKONYp4xhzMsXdqSj6O2INl3o+MKm6gAx1+c8f0byJk64myyd6kjGTKDpuSJXlTeVKTicS7zLPTtlV1RUmHfirTHHz5RP7JnJY1RnHqRt7cf2HzkBYCFz6zd/OOcPaaqb7hC6f0At4zHEi+UxVPNGMrDMpvvpEJFfSx+Da++7/fetT+IXn89z4uSpbuHr31+28726boDgdV2vAxyIhExSfsLpzTm94sw5MFbC6O4Gzj+1HOCQadqHQGYJwSJhLAVqmqAKMrQcbt1XTi1+YoHgRaooLVb2FY+HZjda1vnRMrYbk0zezZJdhlWXdTLHesefMKnohpJ2DTzOZYn2G2lNRUyzMTjFXtBESKrDvTrr9PSvKJ6byFZmopWjZt9JYtkQ0EjInTPp9TDoT7crSvW0049a2/fixfQDgCYrrb6fVK1zNxQvDM/jJ4/vwX0/V3+lmCxaEUNt2O2cbFsPmzZuxefPmVg+jrrzlBkKJaBhruhLY5QTF//Rfz+Gvf/SUue9zB6cxU7DwYlUmeSqveuT2pKJzL5+oUVOcrgqKveUJ03kLXYkozjtpNWYKFnaMuCdiIzMF9KXVlSq98Iae9F2Wal+tD/IDHXFnf1oyAaDOGOug3C2fqBzn/sm8mWQHAJGq8gk9kW6Tk4kdzxYxmS2hJ6mC4kb7vb0TeQiBmpOqNW9v4qbLJ5Lu/Y5dpTPFpdkt2WpkivXnVvUpFk1f/t8/mTe91Y/oT+Mbl56BA5N5/Nt/N75K207cK61O9wmzGqR/UPyx7zyKa34xu1+5DoqbvWLqPaGrvnKh4xpveVq17z+8G7+YQ8miqSmummgHMChuip7glY6FscvJFh2/uhPTeXVWI6XEdfe9gP2TC1/2+cBkDqu7EuhKRPHSdd14eEf9LK/eoa3qjJtMlTZj2oUVGl76cyfaVdYU5+YQFE/lS/jxY+5aKPmqOmBv1lgvyan1d6j/j2Rmn1lni5WXdKp1JaPIOP0ovc3HvasQao/uHPOdCFG9mh2ggni7rJrhZ4tqop8QomJ5VP36dyUiODCVrziIsYSisZJdRsSZCAS42bVkLIxeJ2uv35uDk+628ePNajvTNcWNMsUHdfnEPHpd6s/Rlv31F5jJFG2kYxG1hGwb71gXw5VXXokrr7yy1cOoq+DJFAPAxv4UdoxmULLLeGTHGCZzRXNlatJsY+7Bv2DZKFpldCYiagJX0+UTtTLF6vbZmWK3vnUqV0JXImL2Q97s9PB0EQPOflInArwTs6fz7mIdA53O/nSmaPabqeqg2Mm+NsoUx6rKJ3QmWAedozPemuI4xrPFusHkfierGg3XP8RHwiEz1mYn2nmTGpucTPGE08rO71g0O1OsyrmEEM6Kds1ninUNMwCcsqEHv/eS1fjx5r1tHWRV06+HTkD1O+WCtTLFj+wYx0Mvzq6frk6yNVKwytAvdfUVGX2iVysW0X2oG3U98TKZ4qJfprh9ExptExTry06pWARWWSIkYC4djWeL2DWWxT/+7Fnc8WTz6Xs/ll3G8HTB7JBO3dCDJ/ZM1N3B6MzXqq7ErI3Ge2l/W4NFOKovd+gNcy79X29+eDeuvGkz9k2ok4N81UHJ231idqZY1S75ZRlMSUSNiRk6ozCdVyULehW96prifRM5vPvaB3De5+6blfXTz1vRfSLiZkhyJcs8vzczPZO3kI6FsbYniQOThYpM8XK7dNYKJVuqnqAmU6zeq2Q0bN4LExQ7O+a13QlzxWZtT1KVKzSYaHdwcv41xXpSxjMNguJs0UIqFkY84t/uiQ4fKeWslpBH9qexayyLp/ZOIlu0UbKlCWB1wLt/sjLIBFRA1puKYbLZTLHpPlFC2RNUZQoWQgIV+0TALSsrWmVM50voSkZNeZm3JnM0UzBtJtWJuVURFM8USua5B537jWYKnqDYSe7E3ex5LBKqCN7zJRtjmWJFpri6fEKf7B810IGQAPaM52CXpaop7ohByvrzbfZN5rDWE0DWo48TzWeK3ePKxr4U4pGQ2Q83kykuWmVznIqGm1t+W0qJvRM5rO2pzHy/74wNmMiW5pTBbDUdEOruE+GQwKrOuCk/85JSqk4fPvXGep/ZqJTG3N+TrKi+ItOoJZvO+jb7XOqxnJpin64T7XwS0zZBsV55R+9MBjriGHAK0CeyJZM9XuiKKyMzRZSlW2t16sYe5EtlPHugdkCrD/JDnXFkilZFTa73stjWg/WL/qtriueTKdZlGqa+t0afYkDNEvbqdzIjeod7cCqPT9y0GbmibcZQs3zCM4s67ynZiITVZXn9t+mdY8Eq46PffrQiq60z5NV9igFV96oyxU5QnHRriqfzJXQmohhyVv7ZMZo1v8eguDGdmdGvmT7ZSHiD4ozbCxoA3nHqOvP7Q50JJGo04vfSl5nnUz6hM3wvDM/UPUhmCjbSzoqIK32Z53ZXtMuQsjIQ2tifwshMEf/97CFzmw6G9ee5OvMKqICsJxWt2ZLtmX1TuPFBd1KczkKVZeWBfKZgocOp5/Uy/dst2yxpXH1CCKj9Sb/JFEcw7SmfANT+3gTFnbpHe9HsD/VXb/nGqs449k24f7P++72Z4uryiTHnat5ARwy9qZg5Qe12MsXqPrUDlP0Teaztbi4o7kpGkYiGGmaVvfcH1Gvam4qiKxnFiNPKzrst1MoUF62yOUGPhkNNZYrHs6o8ozrQf/UxA1jXk8TND+9uauztwJ1o575WQzVWtcsWbRTtMg5M5itO/gBPprjJQNVbplkdFLvdJ/z3qXqdhrlcmc3qdQ18MsXtnNBom6DYmykG1Eaid1pjmSJ2j6nM6EKDYn3Q1/0h9ezkx3ZP1PwdveNe3Z2AlJVv6ExFUNxkpthpxj45j5Zszx+aqXje6vIJvfMXArNWKNI7e70zvW/rMP7zsb3YcmDK7Ojr1RQDKsuYq+qNnPYs7KFfqy//0WlIxcJ4cq/b7UK31+utmmgHqB1l1tP9otvJ0ujH7ExEnGVkVU3xy9arxUjmcuYaVFa57KwepQ68+oQqEQ2ZExT93hyayiMkgN8/eS0AdZDrSkYQjzYRFE9XTkiaC/25LtkS2w7VPrnUmeJENNTWl+CCQO8HqzPFAHDLo3vMbRNVK7IdqAiK1W1dCZW5rZX9vPmR3fj7nzxttivvCbQ3MaGD4mp6rsVEtggpVWZaZ4q9ZW+jM0WTKe42NcV50xptximfEAImOPXuD6vLJwDguKFOPO85NhyYnB0UV5dP6Jak/ek4elJRExTrmmI1Vv+EQK2sai1diUjTpROAO4F7dVcCQgh0JiKmx3zcsy3o7aJ6v1Gy3UxxpMmaYn1ldE1VoB8KCVx4+gb8ettIw0m67SJfUnNnvFd19bGtmj5WF+3yrPa0ZjGweQTF1SUXbp/i+pni4ZlC0/v2TFVHi4qgmJnixpJm1q76uqozboLiCad8Amh+kYhaqmflru9NYqAjXrcDhckUO7/j3bj0eFKxMJ5vkCmecS7tFe0yhmcKpjVNs90npJTY5jzHtBN4eHtkAm6Q2Z2MIhSqzJaYAMjJQujC/rGZohsU1yqfqMgUlyuC53Q8bF4T/Vr1pmIY6IhX7LjH/conPJcNc95Msbd8omChIxHBUHcCIzMFbB/O4MQ1nUhGw3OqcQqqoiURCYcghGrLpoOIZDRsDrB6Wzg4pS4fn7C6EwMdcQw5B75mgtCDkyqg1vXhc+E9uaxXV5wpqJpiZopbTx/kvIGQrj/fP5nHJqceVp/81y+fiKA7qcon/A66MwULVllW1D7q8gNvvW6mYM2aZAe4+0Wd0exKqrr0ZDRs9kvZooVs0XYzxc68hgNTedODWdcUJz3tKL2vQSo2O1O8aagD24czJvirTswA/uUT6VgYyZhain3PuDr+9aRipua51lLPOqtaHUDWMtARR18q1viODl0+oYP6zkTULG3ddPmEJ1NcbKL7xF4nKF7nUxLy3tPXQwjgB54TsXaWK6pElvdqxlBXvKLWXvNmdKtLKMxEu2bLJzxxRvX8J72/ztaYaKefq2iVm47BZi3e4Vl2vZ0XXmqboFhP2EpG1ddVXQmTURzLlLB7fLGC4sqzdCEETt3Yg827Jmr+znRezXjXOyNvtloHgS9f3103U2w5S+vqyz8vDqsz//50rOmM18GpgpnENOP0hQQqD0r67LO6nhhwVqCLR8yHyCylnS2aSxy1yyfc1kLVi3ykY26mWNerdiYis1oHjWWK6IxHKi7TmYOBJZ0soDtru2CpSX26fGJ1l8rUTxcsHNmfVo/PiXYNWeUyYk6WWPVedSfapWIRrOlO4AVnezzgTAASQuD9r9qIc09Uy+AmGmSKZwoWMkUbGzzBw1zo7ScWCdUNirNFC6l4MDLFn/3sZ/HZz3621cOoSQc73kBIdyoBgDe/ZDUANxie8i2fULfpzG3RLvvOsch4rkQVLdUpRycpvAd4fQJdTV8l0ZOM9ZUv74qOel8y4GSAuxJRlGyJ3WNZHOME+DOFErIldfLurcHVAaDef1Vkild1omiXscPpyqGPQUN1uk+MZQroc443vU6HGEBlafXckFoJAZ1Vbbam+C/fcgKuufDkpu4LuMcWHdR3JSKmjK2ZiXZF21tT3FymeL/5m2Znv9f2JPHKI/vm1C6slfLW7PUAhroTmC5Ys66EezO6+n3V5lo+kWuifKJ2TbGnbWGTx9xM1US7Qsk2204777vbJijWC3f4ZYrHs0VzaWTh5RMFxMKhijPjUzf2YPtIBuOZIrbsn8IDL4xW/M50Xl2S02f/3p22Pvi/4oheHJou1Jwoos+aNvSqg8Z2zySmol1uasfwvGciX6ZgmYNSwico7vEJigGVLdaXW3TGYizTRKY4UVlT7H3OjnjEvC9Tnsuhqsm8+wGayBbRm67MSLgrTdmqfMJkit2JfdMFp3yiO25+74j+NPo74uayHdVWssuI6INQJFTRkg1QM9z1tnVwKo9VnerA82fnHYe/fftJ5r71gmJ9oNeTY+c62S5TUGURxw91YsuBOpniYnAyxWeffTbOPvvsVg+jJv36e/cFnYmombtggmKdKXYO8Psn3V7pU96aYmef5deWzT3pLpmDrA7KpqqD4jqZYn1A1+UCPamYGdchJ9s52KUn2kWcv7PsaT1mIe9k+rzZYN3fPVV1xRNQXZQAmBKK/ZN5dMQrSxb8uk/o8gzvHIyeZBQ9yShCovZl8311Akg/G/tTeOm67qbuC6hExhH9KZy0pguA2tfrTONcM8WRUHM1xfsm84hHQhWvhdd5Jw3h2QPTy6KEIlcsz+rypK96VNcVe4Nib3lFuSzNRLb5TLTzPq6UEo2Wea4Iin26V/nJVmWK86WyCYrbed/dFkGxgLtT8NYUJ6JhpGLq8tbuRSyfWNUVrygtOHWDqiv+7m934cKvPoA/v+Xxit+ZypfQlYiaHZ5f+YR+jK01OlDMOGdN652Vm9yZ/U5JRhM1Nt6JfDMFy5yBe+t7QyE1oaqrTlCsDwz6kvm4NyhuNNEuZzlBsfucqXjEfED1SUJHIoKBjlhFNmMsW5odFOtV1iz1wXQn2rn1ftNOH1NvZuXI/hQGmSluSsmSJlMWDYc8NcVuUPzCoQzKZem0iorPeoxGmVm9LR3r9FWt7svaiA5mTlzTiS37p2vWrWULqkNJPACZ4vvvvx/3339/q4dRk379veVbgFoi+ejBtOmxO5ErolyWmMyVkI6FUbTKJvDV+4suT41v3aA4byFbUt8P6aDYs61lCpa56uilgzWd0dQBb286agKEYacmftBTU6wd7Sz7PO1MtEvFKoNiHQAmfconjhnsgBCqTzOgOklU9w/2K5/QVya9LdC6nLK4vqqEg9dcM8Xz8Ys/ex0+/NqjAVR2rfAu3lEvUxz11BQ3031C1UgnZ02g1M47SV3RumtL+3ehUJPjKz8zOijeX1Ui4f0seH/mzejOdaJdJCQqWh8W7XLdFXuByrirers7OJXHb31axs2qKbaYKW5aSAizsesJd6ucmb29qRh2j7tLGdZrLt0MvZqd18vXdyMkgH/5+XOYLljYP5k3GwngTvTSAXu2onxCtQtzswH+dcX6Uoe+vLzduVyt677yTdRgbjs0bTYq3RoNQMUyz4DKivTUqBHrT8dmlU+MZooN+xSnY2GEhH9NcUc8XJEp7ohHEA4J02Rez5odmS6YLJLmPRhUdp9wM9PTzmPq900I9Tr2p+NNn7UGWansHoRinhWkvEFxrmRjx2gG49kShjpnZ5gS0XDdOjA9yW6+mWI3KO5SS8hO+7+vKlMcRiIAmeJPfepT+NSnPtXqYdSkX/941T7j79/xEnzxolORiKrWeZPZEmaKFsoSOM7ZT+qsl95OOpyaYgC+HShmzKTbktlX+dcU2/41xc5EO30Sra989SRjJvDQ25w+9nhXeFvbk0Q6FsZ03jITgr3Bd3VLto6KLHIYG/tSeP7gDAqWjQe3j+KMI3srxqfLJ/QVw9FMwWRF9QqgyWjYPE9/OlazfGL/ZB6xSGjWvnYxxSNhM/mwIiiOzs4UVwdAJdvNFMfCIVjl5son6mW+j+hPY9OqDty5DFqz6SsNXies6UIqFsY3f7Oj4nZ9NWBNd8KUkABuHKSX3G5m8pv+3Ax1JSquaOeL5Vn3qVavfOLae1/A+772AB7Z4QbGaqGQygl2hVLZnPiyJVsD3qytbs2ms4K96Sie2KM6GMTCoQWXTxycKsw6S0/HIzhpbRd6U1FcevaRsMuyotWXqmmN+GeK86qGbV1PEqlYuGZdsT7T2tCnguDtIyp4NpniJoLi5w/O4PihTqRjKgjN+5RPAKpVkM5IV1OBqlq1TJcejGeKDfsU6wU1JmvUFHtr/vROsj+tVnzSNX8HpvIVk0sAb/lE2ZnAUtn0fmymiHypjM5EFH3pGGLhENY4VxF0TfFcOx0ETcmTmfG27NPv9aZVKlD5jVM2NOSzClY8Eq57dq97bHovM8+FniB1onNJtla/YlVTHEE8yj7FrVYrU/yStd2mO4xepU4fhE9Yrd7fA6antUoqhEPC7QbhkynOeDLFunxCB6/emmK9r64Wq8oU6/uojhc6U1xASKjFFABUXG0b6oqjMxE1fYoTUTVmfUyo7j5Rna3etKoTWw9O46HtY5gpWKZWX9Ofz6ItIaXEWKZoxqHLCHs8LTYHO+O+LbwAlVVd48wLOBy8ZSAJn0xx9cmrt0+x6j7RRPlEEy3mzj1pCA+9ONZ0r+tW8asp7kvH8D/O2YS7thzEPd52htkiuhIRrO9NVmSK9foIG/tSKNrlWYuK+dET+tf1JivKJ/SVF+99qnkfv7oN6lhGtbn9n9/fbD6LBUtlnyMhgazTxpaZ4jnwNkkwmeIud4egN4ZNQx0LKp+QUuLA5OxMMaBaiP3kT1+D1xw7AKCyqF0FelGTBajui9mZUJe0juhP16xp0uNe15OCEDBLoepMcaOgWEqJ5w/N4NihDtMCza/fIQDc/JGz8D/etMn3cfrScYxlihiZcZfzHc0UG/YpBtyOEN4+xUB1Szb3oGRaB2UKpmF9dVBsDgZW2bTbAtylRPc53UI6E6r36KquOI5w2j71m2VWF3aitNJZtrd8wv2w6WBGB7K/eX4EQOUEIHPfaKhuG52DU6pOUp9wzj0otpGOh3GiEzT5nVwWrTJKtlSZ4iZaxNHS8us+Ua0nGcNErmiysSf4ZIrd+l6nfMJnVbvpwuxMcUcigs54xJQDSanqLL31vJobFFfWFPemYph0FgAZ9izxDFQugTzUlUBnImKu0FV3mdBJgqMH0zjnhFU448i+iuc/fnUHXhzJ4I4n9yMRDeHVznHGjM9zxWwqb6FkS5Pp1RljbznHsas6sO3QzKzetYDKFDfbo3gx1MoU65KV6gCo6Cw7D6ia4lKD+TQlu4yD03msaVAOct5JQ7DLEvduPVT3fq2W88kUA8Afv/ooHD2Yxt/f9rQ5kRh3Sg7XdCcraop1X3fd7WWsiTJCnV1e252oKMvQnyeVbKtdPhENC3QlIrOuUEzlS+hLx3BwKo9P//ipisfs71CTRIvOSrg62dXOV/naIij2ftjffvJa/M0FJ1WUT2gnrulaUKZY99j1Ww/+iP40NvSlsKZndm2PWz6hNmTvLE5dLgCoSRC1lnrW4+5KquVMrbJERzxishG5Uv2/a3hGLSN93KoOdCQimK4IimdnimtlfPvTMZRsiRecXrDpWBjjWbemuFb5hB77lHNQiFcExaolm5TSqf9Vf5Pu9zkyUzSZoeo2QToonimoy6veFe0AYO+4Cor1a/znbz4eH3vjMc7j1162mlxF70Q7TxmFvq0vHUN/OoYHtqtMsd9JY6Mg9OBU3mTTgLmXT0wXLHTEo+hORdERj8yqrQPcz10qFjEr2vEqQeu4fYprH0a6dabY2S9uWqXqa72ZYpO11eUTVZk+KaWbKc5ZJpuVioVV2zTnpFhnp/zKJ/TJ4FimgKSzyhygAvGyVOMYni6YBTkA97iUiqmuPR0JdfLvnRDcURUUp2IRfOPSM7DR04UDUL2KrbLEfz62F6/dNDhrP2u6T3iW0tVJBT0Pw5spPn51JzJF27Qq89o3kTPHscPBW2binWgnhHA+p7UzxbGIW85Vy8GpPKQE1jX4m05Z34OBjlhFprUdqVVo/U/c/u7tL8GO0SxufWwfADUhricVU+UTngmq1eWYzZQRZouWM1kxXrHMc84EsHHkamSKZ/LqSt5AZ3xWK8DpvIUTVnfistcchdse34fJXMl8XnUMkC3YyJdUS7ZYuL3ng7RFUOzNTK3tSeKy1xxlLv3os+TORARre5LIFG3fs+NmDM84Eyk6Z08kMs/vBG2VmeI65RMFq+JSnF+WA3A34o54pOJymBtoq43kb299Ct99aNes39e1ypuGOtEZj2Amb5llE+sdlKrp11Nfnj5hTZfpUxyPhEyWxI+bKa7uUxyBXZYoWGVM+WWKZ4omyKnOFOudqD5oVtcU75nQmWL1/3ecsg6v3TSoHj+tV5RiUFyPZcuKgxAwe5s5ZlWHeQ+GumpMtKtTrqCC4oSpPdeZ4mf2TVW0WLvzmYP4k289Muv3MwULHXH3KtGhqdnvqb5Ck46rTLF0MhB+Dk3PXgGK5m9kpoD/fetTFQFOrTkNXjpRoOuEBzrjGOiI+wbFSWf57uqa4lzJNi3JpvMlz1WtiOklDLhX46oXLQLc7b4sKzOb3g5Hh6qCYr3PGTKLVERN+YYu86oOimvRJUpFq2wmhXlFnP1uqSxNHaluvWaOF0k3QXTckHq856pWYrXsMg5O5X37+S6VWhPt1P9Ds7pPqJpi9fdGQmJWTfGByXzFPkOvBtho4mAoJHDimi4zib1d+U200167aQDxSMh0A5rIltCXimJ1dwJFq2y2Db2t60xx9WIcfrJFVW/fm4oiU7TNBEedEOvviCFb8o+v9ATWgXR8VqZYx0cnrFHb5OhMweyrdQmQPrbEo3qS9DLOFAsh/kMIcUgI8ZTntj4hxJ1CiOedr73O7UII8SUhxDYhxBNCiNMWOkB9dryhN2UOms10avCjJ+v11mlU3pNSS17qIE5nP73lE966mxnPTr27TqZYb8Qdzkapx6GDS/2Ytz+xHz96bHYTct3OZ5OTKc54MsXVO6J6dO/LLfvV4524phPTBQtTOatmdlnrSqgDUPWKdvrAMFOwzGsFuEHraKaA/U4ZRPUlMJ251HVgbo2eyuboTHGXT52gW57BDhT1lOyyOejq17v6vdYlFPFIyLfHdaNlng9OFUzw0BGPmEzxp3/8JP7uJ0+b+/1m2wjufObgrMfyLrpQa3UnN0MYMSdTfnXFk9kSXvtP9+C2J/bVHO9y8IUvfAFf+MIXWj0MAMB9zw3jhgd2mvkdgHtZPF7npLynKlPcnYxWvL/e8glAnRDtqApqvCVzeqIb4GSKE+5y8Drx4D/Rzh2jt1ZY98Ifz6rJnas8QXEsEkIyGjYniSoZofZ/ugWbLtVolJg4ejCNkFCThN90wqpZPxdCOJNg3c4c+jjR51NTfJzT2eO5qjKjQ9MFlOXsK3JLqbNGphhQ+/H6NcUqU6wzoPdtHcabv/BLvO1Lv8JX73sBuaKNnz21H0Bzf9O6nqRv9ryd5Iuza4o1IQTW9ybNYmVjmSJ6nUwx4F7Brg6Km+nClHHKE6tXcjSZ4nQcUvovrKEnQvutDaCP+X2e5cd1GYa+mquD9oQzWXS5l09cD+AtVbf9FYC7pZSbANzt/B8A3gpgk/PvcgDXLnSAOrO5sS9ldnbzLaHwW1GtmhACa7uTJpORL6lG8Z2JCGKRkFM4XtmnWAeFOiiutSIToHbYfZ7LYTo4yTlnaBPZIrbsn551trZl/zR6U1EMdsbNYhm1yifq6fdkikPCzTjsm8jVrScGVPnEWKYIuywrM8Ux932Zzltuu6NUFEKo8gn9Ya6+NK8va+oPaNIzQaUrETU7OL+G/PrSzEJXtcsV7aaXylyOSp4aPn0wqt5m9OpjOrCtloiGkSvZvtt2uSxxaDpvrvh0JqImU7x7PFfx2ur3ufrk0dtfdqgr4TuJSO9o0/GwGxT7XIbbP5VDwSpj52j79yyt55RTTsEpp5zS6mEAcA9q3jkTpk9xvUxxqrKmuDsZrXh/vZliAHjdpkH8cutIxUmTd7XDqXzJJEXc8onKTLFfUKyDTqAys6k7Xoxn1TyL6quIfemYuXqoa4pznqBGb7ON9p2JaBjHDHbgFRt7zX6rWiQsULLK5mqjzgzrq5SrPPvOzkQU63qSs2rv59qjeDFULGJS9Tr4tU4sWt5uOE7XjbLEzQ/vxoe++Vus6U7g905ajf/7s2dx2mfuxDd/swNvOmEVjqwqSfGztieJkZliW2ci85Z/+YS2sS+F3WPqfZww5RNqGzRBcd6daAc0mSku2E5Q7K4UDLhXvgc7YxX/98oULaTj4VkLcgGq+0uXpz+56malPou6vaH+/McjobZfeKlhUCyl/CWA6iZ07wBwg/P9DQDe6bn9W1J5EECPEGLNQgao38CN/amKjOR86DfGe8btZ01Pwkzw8q64BKgMW3X5REdc/aw7FUXRKvu+4ZmCqueJht12aT2pWEVJxrRTVztTsLBnvPJs98m9k3jpum6ViXN2zu7iHXMvn9h2aBoDHXGz0TYVFCei5gORqKop1q/FVM7N/ETCIfSmVOug/ZO5ipMALVZdPuF53K5kxLRJ8mYjNG/QvRCfvWML3n3t/Su2PrVklxF1MsX69a5+r3Wm2K+eGFDbWK1yhdFMESVbuhk1p+a9YNkYni5UlBT5BcUlu4yCVTafb10+Uf1+ZLyZ4qhu92Rj91gWH77hEXOyrCedLPcTnbvuugt33XVXq4cBwH0tvfulWt1vvLqTUeRLZRyayiMRDSERDZv6SEB1k/B+tt/8ktXIlWz8ypn0CVS24VRBqbNMeSyMLs8JWMZzNc6P3va9NbA6G/viSBYlW5r9ofb//uhU/Nl5x5nH1X2K9cl79US7er7y/tPw+YtOqfnzaFgtZKGDlW5nbKGQwA8+ehYue81RFfc/fnXnrPIJfSK4lD2Kq3lfz1h1ptjnCpO3JZue12DZEt9+aCeOX92F//zY2bj2A6fhf7/9JJx9TD++9ydn4j8uPcPctx5dNuI3J6Fd5Dw16X429KWwezyLoqW6SvSmoiZTfMCJS3Q3iKHuOGLhUM2rpVP5kmnzp1ZijMya0KrnM+kru35LPc8UbHQ4C3KNZ4vmMW1nEZHOhJvsq8wUu9ljAE6bxuWfKfYzJKXc73x/AIAukloHYLfnfnuc2+ZNXzra0JusyEjOhz6bql5AotrqriT2T7g7bcC9fJ+ORcxZULksK2qKu5OVlyW8vPfTG09vKoqUU5uWK9oVxe/P7PdeprSx9eC0WXWoU3ef8FlRqhG94asgJmFei70TuYaP052MQscpiZg3KFZ/w1imaLLq7vOpyy0HJvO+l7909maiqqYYqNzZ+rVZMkH3AifavTA8gxdHMuaS1UrjbcmmM/PV77UOilf51BN77+93wvfYrnEAbrstlVEr4aDTpm0y61490du4dzJVpirDt7orgaJdNuVOmt5Zp6vKJx7cPoq7thw0dYj6ALHcg+Krr74aV199dauHAcA/U+yWb9UvnwCAnWNZs39c3Z1QrR2Lagl3b2nUmUf3ozMRqViyd9ppPxUOCUzlSp7yCTVpWWeK9UlTw6DYWz6RcpMEwOz5Jqdt7DWTmToT0VmLHLk1xY0PpZuGOs1j+YmGQyjaZUzmSgiJytrol6ztnlXWdNxQJ14Ynqno3nDTI7uxtjthFhs5HPSVwWhYzJqToi6V11vRTtdSq3rZE1Z3IhVTnYY+9Oqj8I1Lz8BZx/Q3PRZ9MrB3vD1LKHRrsuo2hl4belOYzlvYOarKiHrTMfR3xBEJCRPsT+ctxMIhxCNh9KVjvgt42GWJN/zLvfiOM0cpW1DZ3uoJrXo+ky5H9FvqWc/5GOiIQUpgzNkf6Ks41UFx1tQUV5dPrIBMcSNSHe3mnGITQlwuhHhECPHI8PBwzfsdPZhGMhrGKRt6zSX0+WaKx7MlRMPCtH2rZW1PAoem87DssidTrJ475ckU61Xqmg2K9UFfHyh6UjEknNq0XMmuCBSe2e9mAJ47MA2rLPEyJyjWNcW6FmguQXEyFjaB51BXwlzyyDY4ewUqDybeD7X+u/SJhDfzoy63FLBvYnaPYsBTU2zKJ9wxeA8CtQ50/ekYRqYXFvzoS7nVy3uvFJYtZ3WfqM4Ur+5KYE13wpTTVNPbmF9btvtfGEUiGsJpR/QAcMsn9NUW1Y5H7QT9MsUzVRm+oRpLnppMcTzsCdLdz43un6mD4WYuKVJz9Gu6e9xbPqFqQ0N1JufqA/COkYz5Xr+/u8ezKFjlihPeWCSEc05Yhbu2HDTZKJ11Wt2VMOULMWdScFdSXZWwy9JkjP3KJwD3hND7fF1JdbVJrxa6qs4kbO/vJZ0gWG+zc5nXUUtUl09kS+h2Vq6r54TVnSjZ0tRg/27XOH774hgue+3RTWVVF0u91yAemT2pqmi7NcVm8Sbn7250FbcR3Z9/X5vWFauOOfXbGOq1DB536vd7UzGEQwJDXe4VlowT4AIqaPZLAEzmShjLFLHN6TSVcSaI6td43JRPVHWK8Cuf0BPtTMmi+l09ybUrGVVLn8fCGJ0pmky2vr8JiiPhhvNTWm2+n5yDuizC+ap7oOwFsMFzv/XObbNIKb8mpTxdSnn64OBgzSda25PEls+8BS9b3+2WT8yxB6qm63MaNTVf051EWQIHpwvussVOiUQq7gmK85UH83pBccZTM2kmTiSjiIXVzj1btMyGIwQqZt8+uVd9OHRQnI5H1GU253nqnXX60Wd0Q13xiqx5MzXF5r6e4FX/XfoD68389HfEVabYZ+EOwKd8IlZ5wAJUNrlW4K+D7rl4Ys8E9ngO7nplP92SbDnYOZppqruClNI5CFWVT1SdAAkh8F9Xvg5XvOEY38eplym+/4URnHFknzko6tpL74FJdxTQ77P3qkh1LagOmqon27n9NCszxWOexReAlZMpbifjGfW+6VpHAE5rxvr7Hn0A3jOeM+UAun79Q998GMDs0qg3v2Q1xrMlPLxDXYHQVxLWdCdMn2LTpcbTAlAHz3MpnwiHBLqTUWw94J8p9vLOa0hVlU80Sig0w5RP5Eo1VyT10iewzzpj/9p929GdjOJ9Z2yo92uLLhIOIRUL+2bLqzPFZn8UqQyKcyUbMwWr7nyfZqg5EWjbyXa65LHesVZfTXhyzwQAt8RnTXfC7FNnCpbZHr2r1Hrp/Z8+PuacuuDqRXLclmw6QTY7vtJJvf5aQbG+Cu4sDqZX/XUzxbr7RKjte8zPNyj+CYBLnO8vAXCr5/YPOl0ozgQw6SmzWDAz0a5GL71GJrIls4HVY3oVT+RMUGwyxVG3fMK0APIsGaqeZ/YGOp33Zop1/8kohBBIRcPIFcsmYDhxdRee2ecGxU/tnUR3MmrOgvVltdGZAqJhMeesQH/azdj0OJkSoHHG2Xsw8U6u0QeoA1O5WfcbSKvFV/wW7gC83SeKFY+lHkf9nX6lE+Zv6Yhj30R+Tn1xL//Wo/iXnz8HQL2H+n184IXRZVFXfGAyjzddcx9+7rnEXIteoCViJrb4Z4oBdVIXrbEt6QNe3lJlPl//1XYUrTKGpwvYenCm4hKnLp/w1vVNOCUUfpliUwua0EGx2vEeqs4UF9xMsQ7AC5ZbdjTs7KhHPSs10uLQJx77J3Pmcn3BshtmSHWiwCpL8/3JG3rw7x883ZycV+8XXnfcIGKRkNm+9cIda3qSmHK6T+i5B2Y5+Jw1azuqprd978k9oDJx+jnqBcXek31dPjaXmuJGomGBol3GRLbo2wGm2tGDaYRDAlsPTmP78Ax+/swBfODMjTUz5UupMxFpKlNslyWkdPf7uj+znhfSqLSxkVgkhKHORNsGxc2sB6CD4iecZJiOF9b0JCu6T+hEXW+N8gmdZNOvbaaoaoo74hFEQsIkKnIlGxHn5BCYnSnWfcJ19wnADbTd+MjplJKOYzSjMsVCuAm4CU/3Cd1jvl0105LtewAeAHC8EGKPEOIyAP8XwHlCiOcBnOv8HwDuALAdwDYA/w7gY4s5WHdC13xbshWbOgNf65npWV0+kYyFzZmV/llHjfKJ0ZmCyfhmipYJZvVBYKgz4T5myTIb9tnH9GPvRM48zpN7J/EyZ5Kd9/lGZop1Z37XojfU1V0JRMJuC645lU9EZ2eKdccObxA70BE3OwK/muLZ3Se8E+3U89U6yAHAias7sXcihzP+4S789Y+ebBjUZgoWDkzlzYQUHXi98sg+HJouYHub97gEgD3jWdhl2dRYdWP8aNXlykYZvmp6O8uXbPzimYO4+qdb8J2Hdprs+quPcVfo0uUT3gPTpFMLqsdTWT6hM3xOn+JOXT5ReQXA1JJG3axUoeT27qwunxjNcAnwxTKeKSIRDaEs3TKpQqncsJbWezm8x7P/OO+kIfzkT1+Nu696/awlj9PxCE5Z32MSAzrYXdudcE5iS2Y/ofddU/mSOblN1Qg4YuZKRmXAqR8jEQ3VzDID7tVC73O8btMA3nXqOgzVCaabFQ2H5lRGkIiGcWR/Crc/sR/v/eoDSETCuOTsIxc8jvnoTER9a8urs4J6oq6bKVb7f70fbiZp1cjankTblk/o10K39PPTlYiiOxk1278+Xq/rSWL/ZE7NZcq7fd0bZoqd/WK2oFqyCSGc5c3VPliXTqZ9Ws4Cbp/wdFz1KQbcQHvazLnSbVjVHKJMwUIqGjZXVPSVpkSk/Vcjbab7xB9KKddIKaNSyvVSym9IKUellOdIKTdJKc+VUo4595VSyo9LKY+RUr5MSjm7S/8C6B3WfCfazTlTPJnDfVuH0ZOKmgN1Oh429TLTDconvnj387jougfMRqzP4F++vhs3XX6mya7pjha6HELfvmX/FApW5SQ79XzqeUZmCnVrk2rR/QT1pCr9oat1MNG8GWDvh1pv+Psn/WqK3YOFX6ZYt0qqXrwDcF/Pepnij7/xWPznx87GmUf34zsP7Zo1OauaDob1LHodeL3j1LUAlkddsS4T8GtbVq3kNMaPVpdPzHG78ZZP6Of/4t3P4+dPHUBnIlKxfXYmVHnP9uEZdyKlp1ct4J8p1p+PWCSE/nRsVvlExlmRKeJMMAFUtlK/59XlEwWrbE7IlqPrrrsO1113XauHgXJZYjxbxEvXqvdYlx6pRQjqb0feJER1oCeEwDGDHb61s4NdcXOSM5O3EBJuFnd4umD2OTp7O5lTQXE6Fq5Zi+uWT1RnitW4VnX6tyPUKmqKnf3UpqFOfP6iUxalhtctnyhWnEDUoxerOGogjR989CxznDrcupyWpdXi0cqsoF4worqm+JDz2V1o+QSgSi7bNihuoo0hoOqK9eumPzfrehIo2Wo58kzRU46ZjmE6b+HL92zDeZ+7z3w+xzNuplhKiWzJNvOpupNRUz6RL6kWg/rYW73UszvnI4yupMoy60BbT3L1NhHQE+1ScXfBM+9EO782fe2kLVa0a1YyqlbLWkj3iWY+dF0JtdTsY7sm8ItnDuKi0zd4gomIyRTrjcV7mV8I94C/eyyLqbxqr6Zamqj7CSHwqqP7zQ44GXWC4qxaGUbXDm/ZP4XnDkyjZLuT7AA3Yz4yXZhTOzZNXwLRtZu6xrlxpth/5aJYJIRY2F3wxHs//VwAfJfXBlTApstjvTsLHYR3xmsfIIQQOG1jL9592noAbrawFj2jd2SmgHzJNoHlq47qw+quxLKoK9Z/Y1NBsaWD4voT7Rox5RMlGyMzBURCApO5En765H6ceXR/xaxzfVL03IFpbHIWGZj09KoFKrtPmEUXPPXkq7oSs8onsgXbc7laj6fs2flXZoqrv19ujj/+eBx//PGtHgYmcyWUJfDy9T0A3Ml2+VK5bucJQC0jrzsMNFMSoA12xDGsg2KnnlFfOTo4VTD7qn5PS0nvAjB+4qZ8onIc+phQr3QCqN+PdzFEw8Is3tHMFU0A+Mu3nIBvfugM/OCjZ1WcmB5u63tTFSvTavFIuCIA0pniqOk+ob7qE9qFTrQDgHW9SeybUCtafuXebXjHl3+z4MdcLGZyfINjre4/nIy6k4rXOeWTeydyFUk2XXLyLz9/Ds8fmsHju1XZhS55msyVMJW3ICWQ8pRw6vIJXaOvf5arKp9w+8OrriD9HTGzr62+kt7foYLimYKNjngE0bBa20EHxfHIyli8o20IIczCFXMlpZzTzmZ1dwI/e+oAylLiA2ceYW5X3SfU85tMsbNBhEICXQl3VbsDThZyy4EpzBRKNS/NpWJhZxZ9ET3OAh0DHTHct3UYd21Rcxi9QbEOEkcyxXntnIe6EggJN3OrM8VzqSmuDqDT8bD5u72Z4gFPUFxrRSJv9tKb5dHBdb3yCc2bRapnh2dBhz3jORNYDnUlcNYx/Xhoe/vXFeu/8YDPUsjVrHJl+YSecDfXiUHebg8jM0Ws703iXaeqbotnV7VM0ieJ49kSTlyj2rTVyxS7tfnu+zzUFZ9VPqFXZALc2dt+meKxTNHUJevLdsvRbbfdhttuu63VwzAH15es7UI4JMxku0ITmWJ9qRYAuueQBRzsjGM6rxYo0gu76O3q0HTebAdHD6TRmYjgd7vGKyYf+Yk6SwtXZ4r1MaG6R3G1yol2SxEUq/rb6bzVdHC4oS+FNx6/quHk8aX2j3/wMvy/Pzp11u2JaKhyaXCn/Ve8unxiETPF63qSKNpljMwUcNPDu/H47omKtnWtZHp7N8oU96qg2Htl29tubtrT4vU1xw7gdccN4pr3ngwAGJ5WxzRvnbHOHuvttjcVNftGVT4RMYmS6jlb1VfyBjriZl9bXVPcn46haKu+5Pq5krGwWz4RDSERCfsuurRYDk7l8eACklvLKigG1Bszn0xxtmijaJebrlnSAeMbj19V0Vsy5SmfqO4+AVQu9awDrqf3TiJfKtcMinX5xHi2hF6nO8aZR/fj3ueG8aW7n0d3MmratADuzrloNa7p83PRGRvw3T850xwMdFDcKHuYioVNRrD6YKg/MCGBipZ3ui+y38Idmg7Yqg80JlO8iEHxrjG3DnfPeBYHpwpIxcLoiEdw0poujMwUTW/qdqUzaNWZVK9th2bw6M5xc7kyUlU+MdeTKXeiXRkj0wUMdMTxl285Aee/bDXe9rLK9Xm879emVR0mqzzpZCYGOmIVC3pU73SB2Us9SymxazTr9oWNuJniiaybKbadS/267/JCe1i30jXXXINrrrmm1cMwB9fBzjjWdCcqMsXN7H90hrjZkgDAPZkemSmYST56f1CypdlXhEICrziiF4/sGK/o8OPHTLRLVGeKnfKJGj26tYqkwJJkikNmVv9cXqt2kPa8P17xqgBIzzPQi2zospPhRQ6KAeDercOmXK7RceFwcWuK628/652YwzvxUP9d5qqIc2XtqIE0vvXHr8S7Tl2HSEiYEwxvKaHuL67LjvrSbtcmVT6humAloiFknRVez/3cfXhm35SbtPB0B9LPMV2wkIiGzHFFl2buGc+Z8SWjYXOFIB4Nq/KJJcwUf+WebbjkP35rWjrO1TIMisOzal6aYRbuaPJDpyfbXXzWERW3p6IRFK2y6mFcsCBE5WVfHRQXLHfp4Ed2jjtjrxEURyOmplgfQL74vlPx8ytfh39+z8vxb390akUmQJdPAI3POP10xCM482g3u2eC4jrF/4DK+ugsS/VBQR+MOhPRirHq8ol669abS/rVQbHzWvjtbKs1nSkeyZqdy57xHA46yxMLIcyZ+P7Jxa9Hk1Li9if2LUrGYtjpy3xoumC6S1T721ufwp/dtNlkiqtr+OYaFOtymXxRlU/0d8Qw1JXAV97/iorlZ4HKKwVre5LoSUUxkXMzxRv7UqYWDVD9vmPOao/aqq4ERmYKZsf2o8f24pGd47jwdNVuSmeKR2cKsMoSAx1x5Etl7BnPQkpg0yrVroq9ihdO78f60jFs6E2ZA2wz3ScANxM7l/IJ3d90ZKZoyic6K4JSd196+hG9eP7QTMWB2I/f4h0A0JNuLlMcj4RMKchitGCrFg0LTxnBwoPDdpCIqgVJdPtIve3oTKjOFA/PFBCPhBblddX78et/s8PcdqjNguJGJ5MbnFIJb7zSmYiiMxHB7vEsskV71lWRUEhgoCPuBsWeTLG+uqMTVqu7EhieVvvXbNFyWww6i5M9vmcC2w7N4MHto7OSFt5l2r0r2AJuZ6v9kzmknDjFm+zSmeKSLWseuxZq+0gGBauMnfNcjGvZBcUdcbd8YipfqjuLccdIBld+/7GKBv/dTWaKzz1pCOe/bDVev6myh7IOSLMltRpTRyxSccm/JxXFRLaEQ86l35AANu+ecMbu/4FPxcLIFS1MeGqewyGB41d34sLTN+C1VWPw1tguRm1bs5liwD2gVH+o9YZfndXtiKsJGH6T7DR9Ka06U9zMRDutM6561w771BT/4ukD5oO9czSDM47sRSwcwu7xLA5O5s2l9rXOBMulmKTxu13j+NPvPoY7nlx4h0L9N9pl6ZsJzZdsPLJzHPsnc+bSpc4Uz7+m2AmKLRUUD9QJILzv19qeJLqcSR36M3hEf7qyT3F+doZvqCsOKdXfemgqj7/7ydM448heXOrMrtfbjK5jP86pXdbL3upM8dgyLp9oF96VQDf0Jc0k1WYzxTrrOZd6URMUTxfMaqDeuQrefcXpR/YBAJ4/NFO3fCIW8d9H6fE1qikWQlR0IVps0XDItIZr9jjV7twJserkdtdYFuGQMJPZ9f5oeCq/KFliwK29fWb/lMluNjP/4nCoXhGxFl1TXP2ZWdeTNAvN+F0VWdXlljaMZYumjetO5wqp3m5XO2sxDM8UKhbuSsXDyBZssyDMrrGsTx/5OEZmiijZZUznrYrPk44lytJNFuol0YXQaw6481OWgr468LzzOs3VsguKveUTF3/9Ifz9bU/XvO9dWw7ix5v34el9U3POFJ930hC+8v5XzJrJrDeeXNFWB/OqHWyXs+yo/hCeurHXtJLqqDFhLBULmxXtmmvFE/KUMSz8LWy2phhws7bVGeq0J1PsJYTAS9Z24eXra08CcTPFla9l9xwyxUIIDHbGMVKVEXhq7yQuv/FRXH//DuRLNvZP5XHUQAfW9SYrMsWAp2ZrYvF3oHoJ6Sf2TDa4Z2Mj0wWzIzrkU1f8u12qbKJkS9M+y9QUm8U75tiSzdnOZgoWxrOlBkGxN1OsemFP5IqYzJUQCQms7VHL/OrskXd1Jm21XsBjMo9P/ehJFKwy/vk9J5vPY9RZ9EaXWOiFDHRQfNSA6uHKXsULp08s+lIxrO9N4dC0mqSaL9lNXanSAZ7u496MgU6dKS6oSUWxykyxNyg+eX2PyTg2Kp+IhMSsgERntxqVTwBu6dpSlU9oy618opbqAGj3eBZruhNun+KQmylejEl2gDpe6GD4XaepeQ/1Ss0OJ1NT3GD7WdebhBCz4xUVFKt9nN+2PliVKdYL5ezSmeKqtrD7J/Om+wSgroRnihZedILinaOZWSuO6n3zoekCpvKliuNzn6fcw9QUO9tAPBKCEKJifspiK9llU6Kjl26fq2UZFM84y3o+s3+qYpGLajqjsX14xtTXLLQPon6js0XbZDC8dPmEPli//jg3y1t94NcS0TBm8ham8s1NBBRCuEtrLsLOudeUTzSTKVaZ3+qTBbd8YvYH9UcfezWuPPe4mo+pJ8BUt4TrS8dwzXtPxjudCV2NDHbGZ2WK731OTVS8e8tBc2n9iP4U1vcmsWdM1RTroHiwI45oWCxJpnivsy0+4axSNF9SqpY8L1mrJrAdmJy9s3/Q01ZOd9swLdkWmCnWr81AE8vhhkMCqzoT6EnFMJkrmfKgnmQMZekuk65mKld+LvV78u+/2o67thzCn7/5eBw1kK64TzzidjzRXS6ecw4Y/R0x9KaiZpIYzd94VvUoTsbCZm7DnvEcCla5qX7XOhieW/mE+p3haaemOBHxbYmmv3/JWr3aZ+3tuiMeRm969oqmZxzVh7+94CS85tjaK6tqnfEoomFRc5GbhdCfUWAllU/MzhRv9MzRMcs827IioFoonS3+w1duREi0T/lEM4t3ACrD/snfO94E9dq63qS54uZ3VURlitU+cSxTxMa+FGLhEPaYmmKdKXaTDt4VIvWKvW5QnPWUT6j7DHkSFlNVmWJvtykdgOvSDP03e1cjbcSyy7j9iX1Nrd4KqOOTLst4/lBAMsUdcXUms28ih5ItsXu8dgCjg+IXhjPmcu1Cdzb6Dc4ULEz7XPZVWbGSCVbecLy7o61VBpCKqcl7UjafIXAnHC08KD6iLwUh6tf9al2JqG9ApT8AzWR1q8VqTLQDgHe/Yn3Dy5qad1asdu9zwwCAx3ZP4He7JgC4QfFzB6dRtMpY5Tx+KCSwuntpGr/rs9en9k4tqJZqKm+haJVNz9iD07OD4vtfGDW13zpDYFqyRfwnSjaiW+voz9RgR+3Pkb5strorgXBIqM+E032iOxU1mUPdJ1N1Zqkcj97x3vHkAbziiF586NVHzXqeRDRsPmc6U6yzKP3pOHpTMYzNLN+g+MYbb8SNN97Y6mFgLFM0bRs39qkTk6f3TaplnpvY/xy/ugNruhNNlUFp8UgYXYmIyhQ7E+ii4ZCb0araV5xxZC+A2vM2AOCjbzgGX7v4FbNuj4ZD+OPXHOXbZ7daZyKyJO3Y9Di0lZIp1u+Tbt21eyznGxQDizPJTjt6MI2jB9M4YXUnBjrih6184sBkvu68kWZrigHVg/+0jb0Vt+mrmYD/tj7YmcBopoh8ycZU3kJfOo7+jpjZb3v3zXq8uZLbRUbVFLtB8e7xrOkwoX9X75sPTanFzboqruBEzN+mg2j92DpWmUum+M5nDuJPv/sYfrVtpOF9Abe7VGciEqTyCTXRTteNqJ54/t0CdBuS7cMzpiXIQi/R6A95rmRjumChw2d1JLss8eJIBrFICC9d220C2Fo7bO8Ovjc9x6B4Econjh7swEOfOgevOKK34X039qV864N1AX91u6Nm1JpoN1eDnZVB8WS2hN/tGsfrjxuElMAN9+8AABzZn8b63pS5lOXtn7y2e2kav+udUq5k44Xh+X1YAbcX7wlruhASwMGqTHGmYGHz7glccLJajER329D9QGNhfUlr7q91Iho2f0e98olwSF3J0DXa3SlVUzyZVZni6kVuMp7+w1p/OoZwSCAeCeGf3/Pyij7IWjwSMp/9owfSCAlg+7D6e3tTUdVIfhlnijds2IANGza0ehgYzxTN1aRTNvTgyP4UvvHrF50V7RpvRxeevgH3/9Wbai6qUctAZ9xMtKu+ElVdavWKI1RdcWedoHhNdxKnbmy8j6unMxFZktIJABULgFRPBlyu9ITXZ/ZPIVu0MDJTqOjmFAlXzsdZLJ95x0vx3Q+fCSGEMzFs6TPFmYKFN11zL771wM6a9ymUbFNbOx/eoNhvWx/sVHMxtjlZ0r50FAMdcdP9QccaPSm1AuGBqTxynkxxMhbGeLaIvRM5DHXFUbIlth2aQcqzKI6eg3NgKo/pvDVr2XTdcUonEN02mnqSt9s5qBHdpODpfc2VHe5yroy+4fhVeGF4Zl4JqGUYFKvyiR2jbmut3T6zDKWUbvnESAbj2SI6nWzDQlSUT+RLvuUTgMpYre5KIORMmANq17t5d/DN1t3pSyeLlbVodiWkPzvvONx0+Vmzbk/XKZ9oJFZjot1cDXbEMZYtmjP1X20bRlkCf/qmYzHYGcfT+6bQlYigJxU1ExAAVDSdX9ejGr8vtr0TOVPftZC6Yh30r+lOOBmQyp39IzvHYZUl3vKS1UhGw+bkMeZkiPXZe3XtdzMS0ZA50awXFKufx0xWsTsZxXTBwmhGrdTVMysonr3oQigkcOHp63H1O1+KYwY7fJ9DX4YLObV3fekYrLJETyqKSDiEvnRsWdcU33TTTbjpppvm/fu3bt6Ld/zbrxf8Goxli+bSdjgkcPnrjsETeyZRtBsv3gGocq/59NEd6Ihj93jWLDELuMFidanVGUf2Ih4JNXW1ayGO6E9X7DsWk+4h3pWI+J4ELkfHDXUgEQ3h8d2T5njsDYpjS5Qp7u+Im2THUFf8sJRPbNk/hWzRxpN1SuQmciWkY5F595Ve1yBTrK966rkVPalYRUmDXqBDCIE13QnsGs3CKrstDtOxMHaMZCClW/r5zP6piufqS8cQC4dwcKqA6Xxp1rFE7yvSscoEjM4Uxz2Tthv53S4VFG/Z31x98M7RLBLREF59TD8KVtkcrwCVmf7M7c807FC17ILijphqibbNUy/iFxR7l/3cOZrByEwBPU1mYevRffge3D6qZl5Wl0+k3NW89CWKExoFxZ4dfLNny4uZKZ6LRDTsOzO6+qA1F275xNwDai99lqxbSN373DC6EhGcuqEHbzp+FQB1UBNCVAbFnhOCtT1JHJjKL2q7GCkl9k3k8NpNg0jHwguqK9aZ4oGOOIaqevkCwP0vjCAaFjjjyD6s7UmYCX46U/yaYwfwjUtOx4lrOuf83N7VqerVFAPAdRefjr98i1qNTQfBu8eyKlPsbD8Tpnxi9ucIAP7xD16O955eO1OqTwh7UjHTjghwd8q96diybsl27bXX4tprr53X7z62axx/fssTeHzPJL7/8O4FjWM8U7kS6B+cts6UNC1VKQGgPs/6Mq5OAuiT7uoT6P6OOH71F2/EO05Zu2TjAdQKct/+8KuW5LF1wmal1BMDKvv90rXdeHzPBHaN6nZsSc/PlyZT7OW3OuZSeGqvSnZsq3Ml8MHtozh1Y8+8n8MbFPt2n3A+l7qMrC8dM5lboDLWWN2dMJ8v/TlOxSNmddk3OMfMnaPZiv2zEAKrutQJa75UnrXv1vtfHYDrK8A6VtHBcaMFPPIl27ymW/bXnjvmtdOpWd/klNN5Syge2D6Kb/z6RXz1vhfqPsayC4p18PX0vkmzY/arK9ZnpWcdM4CSLfHU3slFORM9aiCNPzhtHa677wWMzBRmbZg6KJzKW2Y289tevgbnnriqZr2tdwff7A5xMWuKF0O9iXaNLGb5BKCyqeWyxH1bh/G64wYRCYfwxhN0UKyyFOt73WyFd9b52p4k7LLEIZ9a3flSNV5lbOhL4qXruhclUzzYGa/oFwmonchPn9iP0zb2IhkLY21P0kxmMLO9wyGcc+LQvDIVCc/lr3SD9+r41Z2mf7HepmcKFnpSMXM1RGeKZ3wyxc3QWUo9eVa//wPOQaAvFcN4ttT0JI2V4tBUHh/99qNY1RnHqRt78O0Hd867kT2gtl/vJKhENIw/dmq8l/KkfLAj7q4aGtflWeq99ttXrOpKVJQgLIVYJLTgk/daIiYoXhmlE9rJG3rw9L5JE4B5a4r1yTqwuJlir1WdcYxmimYho6XylDPp/4VDGd99zr6JHLYenMHrqlqszsWqznjdTit6H6gnHPemYmbSqrdrFaDqil90rribUgdP0Hz2Mf3muar3z0NdCbzgJCarE2H9JlNcGRTrDLEuo2iUKX563yRKtsQJqzuxfXimqRrknaMZHNGfNi05vZPtnnWyzTc/vLvuqsjLLijWG8Iz+6ZwyoYedMQjvplinTZ//XEDAFQB9mKdgf+fd7wUG/tSKMvZl6G9M6x1pvjsYwbw9UvOqFlT593BN9sdw80Ut0dQ7PYpnvsOParLJxb4t3iD4i0HpjA8XTBnu6/ZNICOeMQsOzzYEUcsHEJ3MlrxGq5Zgl7FuvPEup4kXr6+G8/sn6o7GePWzXvxf257xvdnw9MFRJzJa2opZDco/uLdz2PPeA7/89xNAFBR+63LJxZCv04DHfE5BdXeKwtdnpriiVwR5bJEtji7prgZeierD6bVmeK+dAx2WWIqH6xexf/+q+0Yz5Tw7x88HVe8/hjsncjhri0H5/VYuhdpdcDy/jM34tXH9uOUDT2LMGJ/3iXi9QHWzRQvTWDaSrp8YiVligHg5eu7kS+VcdeWg0jFwhUnWN6OG83Op5krXR7n18N+MT3tBMU5p/VntV9uVZO+X3/8/INiPRkcqDXRzskUH3AzxXq/WL2wzerupDlRcLtPqPsMdMTQ47RgVM81u2Wmnr9RnQhzM8W6zVtl1wk3U1w/yH3UqSd+/6s2oizd7HctUkrsGsviiL4Uup3jo7ei4NkDU4hHVC/wWx6pffVs+QXFzhuQKdo4akDVd/kFxXoFl9cft8rcttB2bGYM8Qj+3x+ehlgkZCYTaRVBcZ0FK7z0BilE80GlW1PcHm/hQjLFscXKFDsf/uGZAh5+cQyAOtvV47vrE6/Hh1+rMlyhkMC63qQ5cdHWLUGvYrO0aW8SL1/fg6JVNjVf1cpliX/5+XP4j9+86Ltd69XkQiE1gWQ8q1ZPfGbfFL72y+248PT1OPsYdSLora/0ZmTmyxsUz4V3Jn1PMmqWBZ3MlZApVmYC58JkivWKZM4Boa+jcvnysUWqK/7RY3vMQjzt7OEd4zhlQw9OXNOFc04cwvreJL7pWd1rLnT5SV9VwNKViOI7Hz5zwRPX6vFuZ3p/Z2qKl2DxjFYz5RMrZJKdpk+cfrtjDBv7UhUn1BUdN5boZEBPDFvKEoqCZeP5g9M43Zmsvs2nHdh9W4expjth5pbM17qeJGKRkG+3lHgkjJ5UFPucCdg9qaipKU5V7WO9SRO3+4T6emS/mg+is/rVWelVXe7kvVk1xR3+mWLThaLJiXa/2zmBjX0ps3hZoxIK1T+9bK4Gb1rVWdGreMv+Kbzm2AGctrEH33Qm3ftpj4hqDrxnRxv7UtjQl8Lucf9McWcigo39KZPOX8zLMy9b342HP3Uu3vOK9RW3ez/YQ13NBcW6zqc7GW16gsVi9ileDOagNZ+WbLpP8QKzP/ogOjxdwMM7x7G2O1ExW3d1d6KihdSbTliF1zlXEjS9o1iKTPH6nhROXt8DAPh1jRYzD744akp/fvL4vlk/H54umOBPB/T7J/L4q/98Ar2pKD51/onmvt76s2gTE6Ia0TuzuQbF3hPF7qRaBrzbWeVOL9lea2Gb+uPRmeKoMy712ev31BQD81vqeaZg4UPf/P/bu/P4KMtz4eO/ayaTjWxkIRAghFVWWUR2UFRaXCq2VY/WU231rdWjx1rbj6+t7/mo7Wnf0756ao/KsVot1g05lCqK4AaIWpA1rGGHAEkgYUsI2TP3+8c8z5PJngyTDJNc38+HT5KZSXLn5plnrrme677u9ew+7jsRn6+s4dHF2/jJwi1B2aq7o1RU17Izv5gJ1ouz2yXcOXUAXx867WSq2uJkaSUbD592uvb0DGIP2baqFxQ36j5xcZz3gqmrlk9kJseSFOvBmPpla1C/pji5w8onfOfJjuxAsfd4KTVe49S0NwyKa2q9fLn/JFcMSwt4kZ2tf8/YFt84pTmZYTfRHjcp1texnoaZ4rr4xMkUWx/tnvB2gNkwK+2fTGqYCLPPv/4dLcA/KLZ7VzefKTbGsOnIGSZkJpGZHEuPSHeri+3sReWZVkA/pFcc+wpLMcZQWVPLgaLzDO8Tz90zBjqPbUrYBcX+GaWslB5kJsdy9HQ5xtSv4Tl2ptx5Ag5K801SsE82ibGeRgd4j0i3E9i2NVNsHzTtyRBcbOUTl2cl89BVQ5g0MLnd3+tpoU9xe8REuomPiqDonO8F3d7+tTn/dsNIHr9+ZL3b4qM9JERHBDcoPltOXJRvm9r+yTHMGJLKi58fcGpq/S3eeIz46AjG9E3k/aaCYr8tlu1a6F99sIttx4p56sbR9d6U9fG7iuEJwmp2+7JXWnz7Xrz8x5Tk7G7msRbD+uagpU0XmlNXU9wgU2yXT8TameL2l098lnOCVXuKePvrIwCsPXCK6lpD7qky/mfjsXb/vEAsXryYxYsXt+t7tuf56vD82yveMXkAw3vH88Bbm9nXyiVI20/fyeaWP63lvew8oOMClpb49ye3z3f2m+6umSm2yie6WKZYRLjUSgb41xMDeDqjptjOFAe4TuSFVfu569X1jWIMfzuslmGzhqWRFOtpFBRnHz3LuYoaZg0LvHTC9vCcYcy/Y0Kz99t/r/1GNqVH05li/8C2Lij2Pc8GptXPFDdVU2xrmAibPiSV6y/t49T11nWfqNvZDlrOFB87U07RuUouG9DT6eC1q5VMsd2RbIA15pEZCZRV1bIzv4T9hb72bMN7JzB3VG9mDk1t9ueEXVDs/58zICWW/j1jKK+u5WSDJv2+oNiXKRuU6vvP6agnnT87Cwb1uxq0xD4Q23P5qCO3Gw1EtMfNI9+4JKAgPVjlE+B7Id1y5AwnSiqdhv7tlZEU3F7Fx86U0zcpxmlN9YvrhlNcXs381fvrPa6kopoPdxTwrbEZ3HxZP3YfP9eojurkuSonE2C/6Vq5u5AbLu3D9Zf2qfdY//KJYOzAFWj5hH/vavu5kWht6FHqZIoDWWhnZYp71K8ptjMj9mW80+fbnyH6ZNcJ56MxhjX7iojxuBnbP4k/fra30aKPYHYrsaWmppKa6jt519R6eX7lviZ3MPS32arDm+C3wr1HVASv/OByoiLc/HDBhnrH1Hlrd1B/u/JL+GLfSTxuF/NX+1ZqhyRT3ERQPDIjgczk2E45l3c2+3J4Yhf828b28202lJlcv52dyyW4XYJLAiu9a4uUHlG4XUJhAJnimlovf/nqEJ/vLWJ3MyVv4Os8ER8dQWZyLEPS4pxFaLbVe4pwu4TpQ5oPxtqqb1JMiwmftAZrK5qrKW6yfMIKnAem2Jli38eG5+f0FjLF/XrG8sL3JtR1tGjUp7j1zTu+tsof7fKsEX0SyCkoafGNyZFTZbitskiAq4f3wiXw8c7jziK7EX0SiHC7eP2e5jvIhF9QbP3HetxCRlKM0/PQv4TCGMPRM2V1QXEHZYqbY7/T9+9q0BL/htptFaqWbB3BE6Q+xeB7Id1qdXdoLVPcnL5JMQHVFDf3hM07W+48UQFGZSTy7fF9+ctXh/lwewHLthWwYsdxXlx9gIpqL7dO7M91Y/rgEliaXZct9noNJ0vryifsN12pcVH8et7oRr/Xv97d/zJloAINiiPcLufE6WSKY32ZYru0IZCFdvaxb5dPjM5IZMqgZCcgDDRTXFXj5fM9RST3iCS/uIKd+SWs2VvElEHJ/OLa4ZwoqXQ2ggF47rN9XP3MamfXrmBZsGABCxYsAGDZ9gKe/ngvz3y8p8Xv2ZR7hqyUWOeNga1vUgyv3DWR4rJqvvnsGu5/YxO3v7SOS5/6mCeX7qz32Je/OEhspJt37p3inGdSQhAU+/9OOwkw+5JerHl09kVzhSyY7Lr/rpYphrq64gENtmoHfAuHrbaKHcHtElLjIgPa1e4fB045Cbf3shtfubPtzC9hVEYCIsKQXnH1NmiqrvWyZPMxJg9MbtdW54Gyu/7Ybxzt4Lhh0iklLooIl1266Lvvkt4JDErt4QSjTvlEo0V6zWeKG2p+RzsvtV7TZGecdzYcYUBKLCOthfEj+iRwrqLGWZ/TlJ35xfRNinESQClxUUwamMzyHcfJKfAtsstKiW32+21hF1HZJ+n+ybG4XeKk9/0XJZ0pq6asqpb+VvmEncb379fXkRJiPPSM9bT5xB3dYBV9W9hPrkCCiYtNsMonoO6Sa3x0hLP1b3tlJMVQUNz2TLExhkcWZXP7y+uabPuTd6asXn0vwM+/cQkugX95czMPvLWZ+97YxPzVB7gkPZ6x/RJJi49i+pBU3tua59SwFpdXU+M1TlCaFOvhn6dk8l+3j2sykxcbGeEEocHJFNsnm/YHSPbxmuD3sbi8mr/+4zAJ0XVdQdrDyRTH1tUQL7x3qlM2FRPpJi0+ig+25TfKShjT9MkYrB7klTU8Nnc4IvDqV4c4fKqMWcPSmDIohSuGpfH8qv2cLK2ksKSC51ft5/CpMv57dcv9L8GXmW0rOyg2xvDyFwcBeDc7r9kFQ8YYNh8549QTNzS2fxKfPzqbH88azJq9RZwsrWRc/yTeXn/EOX/mny3n/a353HZ5JuMze/LH28ZxzYj0RkF2Z4j2uImPjsAlF88VsY7klE90sZpi8L2Zee728U22I/O4XR3+N6cnRHMigA08lm7NJz46gmmDU3h/a36TrdZqar3kFJQwKsOXDR/SK45T56ucTXOWbSsgv7iCe2Y03qq+I9i9iu1kQWSEr8tSwzaabpc4j43xqyVe+fMrnaA3MzmWQWk9GJlR//yc7pfwi2slw29fCbfjHLdL8LiFsqoabntpLbf+aW29dRo5BSVsOHyGf548wHmjZL8+vPj5gSbbqe3ML2bVniJuatCnfO6o3uwrLOWjXccZlh7fppaNHRIUi8hcEdkjIvtF5LFg/mw7CLRXR9ovgP5Bsd2Ozc4UzxqWxq9vGs3kQYFlDturV3xUvV17WmMHg+15FzltcArP3DKWcVatVjiza4xiPBce4NuXji4b0DPgXaEykmI4W1bNT9/J5v8uz3E2zGjO3zbnsWRzHusOnub5lfsA30rjXyzZxpFTZZRU1NTLFNu/Y8VPZrHkX6ax4uGZLHtoBgvvncJf75nk1KnfOTWLo6fL+d3y3UBdSyE78BcR/v2mMU63iabYJRShLJ+Auhd6+xhPiokkv7icVXuKeGD2kIAyKE6muIUs5m+/PYad+SX8ZlmOc1tFdS13L9jAFf9vNbl+O2PaPtl1ghiPmxvHZTAhsydLNvvqau16wH+7YQTlVbU8/dEe5q8+QI3XMH1ICq98eajJTEZZVQ2/X7Gbuc+uYdQTH3HPgg0tLjJpaO2BU+zIK+H+KwdT6zX1Vk6XVdXw49c3cv8bm9hz4hwnS6ta3K49uUckj107nB1PfZNPHrmC+XdMwOUSnlu5D6/X8MzHezHA3TOyALh6RDp/vmtiyHZYS4uPokdU4DuAhRO7fKIrBsUul/CtsRlNHkcet3R4OUyv+PZv4FFRXcuKHceZO6o3t0zsR97ZcmeHNX+vfnWIyhqvsynHYCsJt7/It8jrpTUHGZzWg9mX9Gr0vR3Bfn3wPy/ef+Vg5o3v2+ixdvDbcBGeLdrjZuXPrmTOyPR6t8dGRhAfHUFcVOu7L8Y6C+3qXoOiI9ws3HCUDYfPsPnI2XoJhb+uzSXa4+KWiXVNDMb1T+LWif14Y90RZj+9mlW7C+v9jj98speE6AjumTmo3u1zR/tKCo+eLnc2UWtN0NOMIuIGXgDmAMeADSKy1BjTdOPVdoqMcBEfFVFXxB3p9m0HerruxchevW8HzB63i+9PGRCMX98mT944ql2Nwj1uF1MHpXB5Oy73R7hdfLdB54twZWdIgpkpbs9cNjRrWCrLdySy4fBpjhdX8N6WfF64YzyXDUimorqW19fm8qc1B8lIiubu6QN5aulOJmUl069nDC+sPsC5yhpe+8dhvAZW7DgO0ChTDJCV2oMsGl9OtM0Zmc5dUwfw5y8P0SshiuXWz7LfELZFRmI0e46XBCWoCbT7BPiC4BiP28nuJsb4VqP3SYzmrmlZAY2nYaa4KXNGpvOjmQN5+YtDpMRFcsOlGfz6g12s2VdEXGQEt7+0joX3TiXTuqxmjOHTnBPMHJpKtMfNnJHpbMo9Q9+kGAal2qua4/nBtCxe+eoQES7hlsv68a9XD+Wqp1fz22U5PHHjSNKsXs4Hi0q5/43N7C08x/TBqXxvck/e+voID729hee/N6FNb1Ze+uIgqXGR/OTqoeSeOs+b63J5YPYQar2GexZscF6o11t1eC0FxTY7yExPiOaOyZn8dW0uJ0urWLm7kPuuGNyoS0CopMZFUV7V9jcQ4WxQahypcVFOHWd3EeF2Ba1danPSE6KaDGhbsmp3IaWVNcwb15dxmUlEe7bzXnZ+vbK897Lz+O2Hu7l+TB+uswKwIda29PsLS6ms9rKroITffXdMh5WHNOQsOPY7L953xeAmH+tLmpwNaD1PekJ0m6582ckU/85PUR4XJ0uruG5Mb9wuF8+t3MeckelkJMXw7pY85o3tW2+Nldsl/P7msdw2KZPH/76Du1/bwOPXjeCeGQPZeqyYT3MK+fk3hjVKrvROjGZ8ZhJbjpxleBuvRnbEtfdJwH5jzEEAEVkIzAOCEhQDLPzxFPol1Z20M5Nj+NvmYyzfUQDg9M9rmJ3rLBlNBECtefveKR0wkvBgP1mCUQpinxDaEhg0Z1RGIksfnAH4Lsvc/8ZmbnlxLXFREVTVeqmo9jJtcAq5p8p4+J1sekS6eebWsSTGelh38BR/+eowc0am88NpWTy0MBsI/Fj85fUjyD5WzG8/3E1yj0j+8E9jGWMtWmmLDKunZTDYl8HSAswU+2fA7Eb9j8wZFnB9aEyk7+9KbqXe9dG5w9lXWMqzn+7j2U99mfz/+M4YxvRL5I4/f801//m5E/Ab4FxFDY/MGQb4gur/WL6bWcNS62UrH7pmKO9m51FcXs2DVw2hb1IMP5o5iOdX7WfZ9gJiI91EuISyqlrioyN47YeTnEzzsF5xPPn+LsY99XGLb1YOHvYFuYf3FPEza55+NHMQH24/zuTffEqtMdR6DS98bwI1XsNP38kmLiqCob3aVzZ0/5WDeXv9EVbuLuSX1w3nRw2yLaE0tFec7z+lGxjTL5GN/+eaUA+j06XGRZGZ3LFvBNITojl9vopLn/yozd9TUeMlNS6KqYNTcLuEa0ak8+bXuU5HFvC1bpw8MJlnbh3rBL19k2KI9rh4wqrVT4uP4qYmsrQdxV4El9yGMreMpGgiI1z1NlFpqz6J0c4Oqy2xS179A+8YaxOXX80bjUuEtQdOMu+Fr4hwCeXVtXx/atNJzAmZPfnb/VP5+f9s5d+X5fDHT/dRWesluUckP5jedHnK3FG92XLkLCPamCmWllbzBUJEbgbmGmP+l/X194HJxpgHGzzuXuBegMzMzMtyc3MD/p3/2H+Sj3fV37EpKyW22UlSF5eTpZV8susEt0/KvOCfVVJRzeKNx7hrWlbQLvkWl1fzypeHKCmvxiXCnJHpTB2cQmVNLUs255GZHOusKs4pKGFj7hnumJSJyyXkW7uJ+ddHtdeJkgoWbzrG7ZMyWw0AGzp08jzb84q5cWxG6w9uRUFxOV/uO8ktE/u3+3t35BVzoqSCq0f4LsMVnavkg2353Dk18P+nEyUVrN5TyD9d3rbjJvfUeT7LKaRPYjTXjvFldfYcP8eijUfrdWCIjXTzr1cNdU7ib68/wvTBqU422bb16FmKzlVyjXVp0es1rD14in0nznH0TDm1XkOUx8WdU7MaXSlYujXf6RTRnAWP/wCA+37/Og/MHuw0yV9g1TgDXDu6N5MH+Tao2XD4NCXl1c4ct8dnOSeIcLu4Iggto4KporqWWq/pEmsnVNPOnK8i2uPu0N7Tx86UseCrw9S0s0vMrGGpXDXc93w6UFTKm+uO4PWLmexL9g0zlEs2H2ObteD76hG9nA0oOoMxhtfX5XLj2IxWO1qdKKlg9/FzAT3vtxw5Q2llTat/mzGGt9Yf4drRfZzXrxU7CkiLj3aSV9uPFbNkyzGMaVvs5vX6fqbd+q6lOT5XUc3C9Uf54fSsejXFIrLJGDOx4eNDFhT7mzhxotm4cWNQx6GUUuGsrMwX+MbGXhylDEop1VU0FxR3xNvvPMA/ldTPuk0ppVQbaTCslFKdqyO6T2wAhorIQBGJBG4DlnbA71FKqS5r/vz5zJ8/P9TDUEqpbiPoQbExpgZ4EPgIyAEWGWN2tvxdSiml/C1atIhFixaFehhKKdVtdMjqBWPMh8CHHfGzlVJKKaWUCraw29FOKaWUUkqpYNOgWCmllFJKdXsaFCullFJKqW4v6H2KAxqESBEQ+O4d3VsqcDLUg+gidC6DQ+cxeHQug0fnMnh0LoNH5zI42juPA4wxjXb8uCiCYhU4EdnYVANq1X46l8Gh8xg8OpfBo3MZPDqXwaNzGRzBmkctn1BKKaWUUt2eBsVKKaWUUqrb06A4/L0U6gF0ITqXwaHzGDw6l8Gjcxk8OpfBo3MZHEGZR60pVkoppZRS3Z5mipVSSimlVLenQbFSSimllOr2NCgOQyJyi4jsFBGviEz0uz1LRMpFJNv692IoxxkOmptL675fiMh+EdkjIt8M1RjDkYg8KSJ5fsfidaEeU7gRkbnWsbdfRB4L9XjCmYgcFpHt1rG4MdTjCSci8qqIFIrIDr/bkkXkExHZZ33sGcoxhoNm5lHPkwEQkf4iskpEdlmv3z+xbr/g41KD4vC0A/gOsKaJ+w4YY8ZZ/+7r5HGFoybnUkRGArcBo4C5wHwRcXf+8MLaH/yOxQ9DPZhwYh1rLwDXAiOB261jUgVutnUsak/Y9lmA7xzo7zHgM2PMUOAz62vVsgU0nkfQ82QgaoCfGWNGAlOAB6zz4wUflxoUhyFjTI4xZk+ox9EVtDCX84CFxphKY8whYD8wqXNHp7qxScB+Y8xBY0wVsBDfMalUpzLGrAFON7h5HvCa9flrwE2dOaZw1Mw8qgAYYwqMMZutz88BOUBfgnBcalDc9QwUkS0i8rmIzAz1YMJYX+Co39fHrNtU2z0oItusy4Z6ebV99PgLLgN8LCKbROTeUA+mC0g3xhRYnx8H0kM5mDCn58kLICJZwHjga4JwXGpQfJESkU9FZEcT/1rKFhUAmcaY8cAjwFsiktA5I754BTiXqhWtzOt/A4OBcfiOy2dCOVbV7c0wxkzAV47ygIjMCvWAugrj6+uqvV0Do+fJCyAiccDfgIeNMSX+9wV6XEYEaWwqyIwx1wTwPZVApfX5JhE5AAwDuvXCkkDmEsgD+vt93c+6TVnaOq8i8jLwQQcPp6vR4y+IjDF51sdCEfk7vvKUptZkqLY5ISJ9jDEFItIHKAz1gMKRMeaE/bmeJ9tHRDz4AuI3jTFLrJsv+LjUTHEXIiJp9mIwERkEDAUOhnZUYWspcJuIRInIQHxzuT7EYwob1gnJ9m18CxpV220AhorIQBGJxLfoc2mIxxSWRKSHiMTbnwPfQI/HC7UUuMv6/C7gvRCOJWzpeTIwIiLAK0COMeY//e664ONSd7QLQyLybeA5IA04C2QbY74pIt8FfgVUA17gCWPM+yEbaBhobi6t+x4H7sa30vVhY8zyUI0z3IjI6/guCRrgMPBjv1ov1QZWe6ZnATfwqjHmN6EdUXiyEgR/t76MAN7SuWw7EXkbuBJIBU4ATwDvAouATCAXuNUYo4vIWtDMPF6JnifbTURmAF8A2/HFOgC/xFdXfEHHpQbFSimllFKq29PyCaWUUkop1e1pUKyUUkoppbo9DYqVUkoppVS3p0GxUkoppZTq9jQoVkoppZRS3Z4GxUop1UlEJEVEsq1/x0Ukz/q8VETmd9DvfFhE7mzh/htE5Fcd8buVUiqcaEs2pZQKARF5Eig1xjzdgb8jAtgMTDDG1DTzGLEeM90YU9ZRY1FKqYudZoqVUirERORKEfnA+vxJEXlNRL4QkVwR+Y6I/F5EtovICmt7U0TkMhH5XEQ2ichHDXbHsl0FbLYDYhF5SER2icg2EVkIYHyZkdXADZ3yxyql1EVKg2KllLr4DMYX0N4IvAGsMsaMAcqB663A+DngZmPMZcCrQFO7tE0HNvl9/Rgw3hhzKXCf3+0bgZlB/yuUUiqMRIR6AEoppRpZboypFpHt+LZ5XmHdvh3IAi4BRgOf+KofcANNbQ/bB8jx+3ob8KaIvItvq15bIZARvOErpVT40aBYKaUuPpUAxhiviFSbusUfXnznbQF2GmOmtvJzyoFov6+vB2YB3wIeF5ExVmlFtPVYpZTqtrR8Qimlws8eIE1EpgKIiEdERjXxuBxgiPUYF9DfGLMK+N9AIhBnPW4YsKPDR62UUhcxDYqVUirMGGOqgJuB34nIViAbmNbEQ5fjywyDr8TiDaskYwvwX8aYs9Z9s4FlHTlmpZS62GlLNqWU6sJE5O/Ao8aYfc3cnw68ZYy5unNHppRSFxcNipVSqgsTkUuAdGPMmmbuvxyoNsZkd+rAlFLqIqNBsVJKKaWU6va0plgppZRSSnV7GhQrpZRSSqluT4NipZRSSinV7WlQrJRSSimluj0NipVSSimlVLf3/wHIoHrMPPQPdAAAAABJRU5ErkJggg==\n", 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\n", 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" ] diff --git a/workflow_miniscope/analysis.py b/workflow_miniscope/analysis.py index f0f9118..7831a29 100644 --- a/workflow_miniscope/analysis.py +++ b/workflow_miniscope/analysis.py @@ -51,7 +51,7 @@ def make(self, key): ).fetch1('session_datetime', # 'scan_datetime', 'nframes', 'fps') - scan_time = None + scan_time = None # BEING ADDED TO ELEMENT # Estimation of frame timestamps with respect to the session-start # (to be replaced by timestamps retrieved from some synchronization routine) @@ -68,7 +68,8 @@ def make(self, key): nsamples = len(aligned_timestamps) trace_keys, activity_traces = (miniscope.Activity.Trace & key - ).fetch('KEY', 'activity_trace', order_by='mask') + ).fetch('KEY', 'activity_trace', + order_by='mask_id') activity_traces = np.vstack(activity_traces) aligned_trial_activities = [] From 0f3383f90a8331e653c7e4926a7f71558c4a58a0 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 29 Apr 2022 18:27:59 -0500 Subject: [PATCH 6/8] Update workflow_miniscope/analysis.py Co-authored-by: Thinh Nguyen --- workflow_miniscope/analysis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workflow_miniscope/analysis.py b/workflow_miniscope/analysis.py index 7831a29..7548136 100644 --- a/workflow_miniscope/analysis.py +++ b/workflow_miniscope/analysis.py @@ -45,11 +45,11 @@ class AlignedTrialActivity(dj.Part): def make(self, key): # DEVNOTE : caimg->miniscope, no scan_datetime. so removed scan_time from fetch # safe to assume no sess-scan diff? - sess_time, nframes, frame_rate = (miniscope.RecordingInfo + sess_time, rec_time, nframes, frame_rate = (miniscope.RecordingInfo * session.Session & key ).fetch1('session_datetime', - # 'scan_datetime', + 'recording_datetime', 'nframes', 'fps') scan_time = None # BEING ADDED TO ELEMENT From 8e066c1b9ec0e790cdc0b3f9545b95daa0cb02dc Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 29 Apr 2022 18:28:17 -0500 Subject: [PATCH 7/8] Update workflow_miniscope/analysis.py Co-authored-by: Thinh Nguyen --- workflow_miniscope/analysis.py | 1 - 1 file changed, 1 deletion(-) diff --git a/workflow_miniscope/analysis.py b/workflow_miniscope/analysis.py index 7548136..aedb9f8 100644 --- a/workflow_miniscope/analysis.py +++ b/workflow_miniscope/analysis.py @@ -51,7 +51,6 @@ def make(self, key): ).fetch1('session_datetime', 'recording_datetime', 'nframes', 'fps') - scan_time = None # BEING ADDED TO ELEMENT # Estimation of frame timestamps with respect to the session-start # (to be replaced by timestamps retrieved from some synchronization routine) From 97d4d532185ad5b50be9a4a937451e807d74c969 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 29 Apr 2022 18:28:35 -0500 Subject: [PATCH 8/8] Update workflow_miniscope/analysis.py Co-authored-by: Thinh Nguyen --- workflow_miniscope/analysis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workflow_miniscope/analysis.py b/workflow_miniscope/analysis.py index aedb9f8..44f17bb 100644 --- a/workflow_miniscope/analysis.py +++ b/workflow_miniscope/analysis.py @@ -54,8 +54,8 @@ def make(self, key): # Estimation of frame timestamps with respect to the session-start # (to be replaced by timestamps retrieved from some synchronization routine) - scan_start = (scan_time - sess_time).total_seconds() if scan_time else 0 - frame_timestamps = np.arange(nframes) / frame_rate + scan_start + rec_start = (rec_time - sess_time).total_seconds() if rec_time else 0 + frame_timestamps = np.arange(nframes) / frame_rate + rec_start trialized_event_times = trial.get_trialized_alignment_event_times( key, trial.Trial & (ActivityAlignmentCondition.Trial & key))