From 27b3144ad010a9b9b39aaa71e1ec606f43d97043 Mon Sep 17 00:00:00 2001 From: Daniel Xenes Date: Mon, 19 Feb 2024 13:56:55 -0500 Subject: [PATCH 1/2] adds coreg notebook --- 000402/MICrONS/coregistration/README.md | 23 + 000402/MICrONS/coregistration/ScanUnit.pkl | Bin 0 -> 14870416 bytes 000402/MICrONS/coregistration/environment.yml | 187 +++ .../coregistration/microns_nwb_coreg.py | 145 +++ .../microns_nwb_coreg_notebook.ipynb | 1023 +++++++++++++++++ 000402/MICrONS/coregistration/ng_utils.py | 78 ++ .../functional_base_state.json | 391 +++++++ .../structural_base_state.json | 80 ++ .../MICrONS/coregistration/requirements.txt | 6 + 9 files changed, 1933 insertions(+) create mode 100644 000402/MICrONS/coregistration/README.md create mode 100644 000402/MICrONS/coregistration/ScanUnit.pkl create mode 100644 000402/MICrONS/coregistration/environment.yml create mode 100644 000402/MICrONS/coregistration/microns_nwb_coreg.py create mode 100644 000402/MICrONS/coregistration/microns_nwb_coreg_notebook.ipynb create mode 100644 000402/MICrONS/coregistration/ng_utils.py create mode 100644 000402/MICrONS/coregistration/ng_visualization/functional_base_state.json create mode 100644 000402/MICrONS/coregistration/ng_visualization/structural_base_state.json create mode 100644 000402/MICrONS/coregistration/requirements.txt diff --git a/000402/MICrONS/coregistration/README.md b/000402/MICrONS/coregistration/README.md new file mode 100644 index 0000000..8715538 --- /dev/null +++ b/000402/MICrONS/coregistration/README.md @@ -0,0 +1,23 @@ +# MICrONS Co-registration Analysis Notebook +**This is an example notebook on how query functional data from DANDI and update it with co-registration information to analyze structure-function data in one place.** + +This project was developed during the 2023 NWB NeuroData ReHack workshop held in Granada, Spain. Please contact us at info@bossdb.org if you have any questions. + +## Why is this needed? +Comparison and generation of functional connectivity networks and structural connectivity networks is a key goal of the MICrONS project and requires joint analysis across archives. This notebook provides a starting point for those who want a configurable and dynamic way of obtaining the latest co-registered functional and structural data. + +Our goal with this notebook to provide a seamless integration of generate novel insight into the stimulus-dependent and time-dependent activation of functional subnetworks. This will also allow direct investigation of causal connectivity estimation using functional data. Follow on work could investigate improvements to functional connectivity estimation methods. Even more exciting, however, is the potential to generate a wide range of hypotheses relating the structure and function of neural networks within mammalian cortex. + +## How to run +You'll need a fresh python virtual environment (ver. 3.8 or higher) to run this notebook. + +1. Install python requirements from `requirements.txt` or through conda from the `enviornment.yml` file. +2. Run `microns_nwb_coreg_notebook` notebook for a complete end-to-end demo of how to integrate the latest co-registed cells from CAVE with the MICrONS functiondal data stored in DANDI. +3. Feel free to import/modify `microns_nwb_coreg.py` or `ng_utils.py` script functions for your own use-case! + +## Citations +>O. Rübel et al., “The Neurodata Without Borders ecosystem for neurophysiological data science,” eLife, vol. 11, p. e78362, Oct. 2022, doi: 10.7554/eLife.78362. + +> J. A. Bae et al., “Functional connectomics spanning multiple areas of mouse visual cortex,” bioRxiv, p. 2021.07.28.454025, Jan. 2021, doi: 10.1101/2021.07.28.454025. + +> Z. Ding et al., “Functional connectomics reveals general wiring rule in mouse visual cortex,” Neuroscience, preprint, Mar. 2023. doi: 10.1101/2023.03.13.531369. diff --git a/000402/MICrONS/coregistration/ScanUnit.pkl b/000402/MICrONS/coregistration/ScanUnit.pkl new file mode 100644 index 0000000000000000000000000000000000000000..0e960c44695f79881296322a1bdb972dcae755ee GIT binary patch literal 14870416 zcmeF!WtiB7y{_viPH=a3cXxMpCqQrllZ4{#R>7ePRkVWB;!+A#q=kBs7A?}^wh8m} z!@90@KJ0U@v-e(m-T9DbCNs&*y!rj_NlqMh@56?V+-Lac#rHmR)QH9R9X0%r5yRFU zK4$p(pT0CWu+T|^pE+)DoCS9rZ(v|xk3;qwHTWNY#{nb9j2Ja?_`#zWKVM>=5SJ|m7CF?#X6hL0XGY>$y6j@V

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zb^YS2k6nA_i6?J7@r&#CTz~S3#~#1u;=wCtSMI&?s^46E?V*dWKXdh&>o+dGaqamg zkKXv7UtK)(@Ee}1|H;)GSAY8S)u$f6_~!HWqfb44 O#dn^)`0g{;?*D%`wnq5? literal 0 HcmV?d00001 diff --git a/000402/MICrONS/coregistration/environment.yml b/000402/MICrONS/coregistration/environment.yml new file mode 100644 index 0000000..c596870 --- /dev/null +++ b/000402/MICrONS/coregistration/environment.yml @@ -0,0 +1,187 @@ +name: neurodatarehack2023 +channels: + - defaults +dependencies: + - bzip2=1.0.8=h620ffc9_4 + - ca-certificates=2023.12.12=hca03da5_0 + - libffi=3.4.4=hca03da5_0 + - ncurses=6.4=h313beb8_0 + - openssl=3.0.13=h1a28f6b_0 + - pip=23.3.1=py311hca03da5_0 + - python=3.11.7=hb885b13_0 + - readline=8.2=h1a28f6b_0 + - setuptools=68.2.2=py311hca03da5_0 + - sqlite=3.41.2=h80987f9_0 + - tk=8.6.12=hb8d0fd4_0 + - wheel=0.41.2=py311hca03da5_0 + - xz=5.4.5=h80987f9_0 + - zlib=1.2.13=h5a0b063_0 + - pip: + - aiohttp==3.9.3 + - aiosignal==1.3.1 + - appdirs==1.4.4 + - appnope==0.1.4 + - arrow==1.3.0 + - asciitree==0.3.3 + - asttokens==2.4.1 + - attrs==23.2.0 + - bidsschematools==0.7.2 + - blessed==1.20.0 + - boto3==1.34.44 + - botocore==1.34.44 + - brotli==1.1.0 + - cachetools==5.3.2 + - caveclient==5.15.2 + - certifi==2024.2.2 + - chardet==5.2.0 + - charset-normalizer==3.3.2 + - ci-info==0.3.0 + - click==8.1.7 + - click-didyoumean==0.3.0 + - cloud-files==4.21.1 + - cloud-volume==8.29.1 + - comm==0.2.1 + - compressed-segmentation==2.2.2 + - compresso==3.2.2 + - contourpy==1.2.0 + - crackle-codec==0.7.1 + - crc32c==2.3.post0 + - cycler==0.12.1 + - cython==3.0.8 + - dandi==0.59.1 + - dandischema==0.8.4 + - debugpy==1.8.1 + - decorator==5.1.1 + - deflate==0.5.0 + - dill==0.3.8 + - dnspython==2.6.1 + - dracopy==1.4.0 + - email-validator==2.1.0.post1 + - etelemetry==0.3.1 + - executing==2.0.1 + - fasteners==0.19 + - fastremap==1.14.1 + - fonttools==4.49.0 + - fpzip==1.2.3 + - fqdn==1.5.1 + - frozenlist==1.4.1 + - fscacher==0.4.0 + - fsspec==2024.2.0 + - gevent==24.2.1 + - google-api-core==2.17.1 + - google-auth==2.28.0 + - google-cloud-core==2.4.1 + - google-cloud-storage==2.14.0 + - google-crc32c==1.5.0 + - google-resumable-media==2.7.0 + - googleapis-common-protos==1.62.0 + - greenlet==3.0.3 + - h5py==3.10.0 + - hdmf==3.12.2 + - humanize==4.9.0 + - idna==3.6 + - importlib-metadata==7.0.1 + - inflection==0.5.1 + - iniconfig==2.0.0 + - interleave==0.2.1 + - ipykernel==6.29.2 + - ipython==8.21.0 + - isodate==0.6.1 + - isoduration==20.11.0 + - jaraco-classes==3.3.1 + - jedi==0.19.1 + - jmespath==1.0.1 + - joblib==1.3.2 + - json5==0.9.14 + - jsonpointer==2.4 + - jsonschema==4.21.1 + - jsonschema-specifications==2023.12.1 + - jupyter-client==8.6.0 + - jupyter-core==5.7.1 + - keyring==24.3.0 + - keyrings-alt==5.0.0 + - kiwisolver==1.4.5 + - markdown==3.5.2 + - matplotlib==3.8.3 + - matplotlib-inline==0.1.6 + - more-itertools==10.2.0 + - multidict==6.0.5 + - multiprocess==0.70.16 + - natsort==8.4.0 + - nest-asyncio==1.6.0 + - networkx==3.2.1 + - numcodecs==0.12.1 + - numpy==1.23.2 + - nwbinspector==0.4.33 + - oldest-supported-numpy==2023.12.21 + - orjson==3.9.14 + - packaging==23.2 + - pandas==2.2.0 + - parso==0.8.3 + - pathos==0.3.2 + - pexpect==4.9.0 + - pillow==10.2.0 + - platformdirs==4.2.0 + - pluggy==1.4.0 + - posix-ipc==1.1.1 + - pox==0.3.4 + - ppft==1.7.6.8 + - prompt-toolkit==3.0.43 + - protobuf==4.25.3 + - psutil==5.9.8 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - pyarrow==11.0.0 + - pyasn1==0.5.1 + - pyasn1-modules==0.3.0 + - pybind11==2.11.1 + - pycryptodomex==3.20.0 + - pydantic==1.10.14 + - pygments==2.17.2 + - pynwb==2.5.0 + - pyout==0.7.3 + - pyparsing==3.1.1 + - pysimdjson==6.0.2 + - pyspng-seunglab==1.1.1 + - pytest==8.0.1 + - python-dateutil==2.8.2 + - python-jsonschema-objects==0.5.2 + - pytz==2024.1 + - pyyaml==6.0.1 + - pyzmq==25.1.2 + - referencing==0.33.0 + - requests==2.31.0 + - rfc3339-validator==0.1.4 + - rfc3987==1.3.8 + - rpds-py==0.18.0 + - rsa==4.9 + - ruamel-yaml==0.18.6 + - ruamel-yaml-clib==0.2.8 + - s3fs==0.4.2 + - s3transfer==0.10.0 + - scipy==1.12.0 + - semantic-version==2.10.0 + - simplejpeg==1.7.2 + - six==1.16.0 + - stack-data==0.6.3 + - tenacity==8.2.3 + - tornado==6.4 + - tqdm==4.66.2 + - traitlets==5.14.1 + - types-python-dateutil==2.8.19.20240106 + - typing-extensions==4.9.0 + - tzdata==2024.1 + - uri-template==1.3.0 + - urllib3==2.0.7 + - wcwidth==0.2.13 + - webcolors==1.13 + - yarl==1.9.4 + - zarr==2.17.0 + - zarr-checksum==0.4.0 + - zfpc==0.1.2 + - zfpy==1.0.0 + - zipp==3.17.0 + - zope-event==5.0 + - zope-interface==6.2 + - zstandard==0.22.0 + diff --git a/000402/MICrONS/coregistration/microns_nwb_coreg.py b/000402/MICrONS/coregistration/microns_nwb_coreg.py new file mode 100644 index 0000000..1ca7fe2 --- /dev/null +++ b/000402/MICrONS/coregistration/microns_nwb_coreg.py @@ -0,0 +1,145 @@ +############################################################################################## +# Copyright 2023 The Johns Hopkins University Applied Physics Laboratory LLC +# All rights reserved. +# Permission is hereby granted, free of charge, to any person obtaining a copy of this +# software and associated documentation files (the "Software"), to deal in the Software +# without restriction, including without limitation the rights to use, copy, modify, +# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, +# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR +# PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE +# OR OTHER DEALINGS IN THE SOFTWARE. +############################################################################################## + +""" +This script contains all functions necessary to update a MICrONS NWB file with the latest automated coregistration from CAVE. + +Check the DANDISET and CAVE_COREG_TABLE variables before running to make sure they are the correct dataset and tables +you want to pull. + +DEV NOTE: Unable to update NWB file for session 5 scan 7. + +Example Usage: +``` +from microns_nwb_coreg import get_microns_nwb_file, update_microns_nwb_file + +microns_nwb = get_microns_nwb_file(session_no=4, scan_no=4) +microns_nwb = update_microns_nwb_file(microns_nwb) +""" + +from dandi.dandiapi import DandiAPIClient +from caveclient import CAVEclient + +from fsspec.implementations.cached import CachingFileSystem +from fsspec import filesystem +from h5py import File +from pynwb import NWBHDF5IO +from pynwb.file import NWBFile + +from tqdm import tqdm +import pandas as pd + +from pynwb.ophys import PlaneSegmentation + + +DANDISET_ID = "000402" +CAVE_COREG_TABLE = "apl_functional_coreg_forward_v5" + + +def get_microns_nwb_file(session_no:int, scan_no:int): + file_path = f"sub-17797/sub-17797_ses-{session_no}-scan-{scan_no}_behavior+image+ophys.nwb" + with DandiAPIClient() as client: + asset = client.get_dandiset(DANDISET_ID, 'draft').get_asset_by_path(file_path) + s3_url = asset.get_content_url(follow_redirects=1, strip_query=True) + + # First, create a virtual filesystem based on the http protocol + fs = filesystem("http") + + # Create a cache to save downloaded data to disk (optional) + fs = CachingFileSystem( + fs=fs, + cache_storage="nwb-cache", # Local folder for the cache + ) + + # Next, open the file with NWBHDF5IO + file_system = fs.open(s3_url, "rb") + file = File(file_system, mode="r") + io = NWBHDF5IO(file=file, load_namespaces=True) + + microns_data = io.read() + return microns_data + +def create_new_plane_segmentation(old, df, descriptions): + ps = PlaneSegmentation( + name=old.name, + description=old.description, + imaging_plane=old.imaging_plane, + id=df.index.tolist() + ) + + for col in df.columns: + if col in old.colnames: + old_col = find_column_by_name(old, col) + ps.add_column(name=old_col.name, description=old_col.description, data=df[col].tolist()) + else: + ps.add_column(name=col, description=descriptions[col], data=df[col].tolist()) + return ps + + +def find_column_by_name(table,col_name): + for c in table.columns: + if c.name == col_name: + return c + +def update_microns_nwb_file( + nwb: NWBFile +): + + # Pre-requisite data loading + cave = CAVEclient("minnie65_phase3_v1") + coreg = cave.materialize.query_table(CAVE_COREG_TABLE) + scan_units = pd.read_pickle("./ScanUnit.pkl") + + session, scan_idx = int(nwb.session_id.split('-')[0]), int(nwb.session_id.split('-')[2]) + if session == 5 and scan_idx == 7: + print("Error: This file does not contain a unit_id column") + return + scan_units_modified = scan_units[(scan_units['session']==session) & (scan_units['scan_idx']==scan_idx)] + + image_segmentation = nwb.processing["ophys"].data_interfaces["ImageSegmentation"] + + all_ps = list(image_segmentation.plane_segmentations) + for ps_name in tqdm(all_ps): + + ps = image_segmentation.plane_segmentations.pop(ps_name) + field = int(ps_name[-1]) + field_scan_units = scan_units_modified[scan_units_modified['field'] == field] + ps_df = ps[:] + ps_df['mask_id'] = ps_df.index + ps_df_with_units = ps_df.merge(field_scan_units, on='mask_id', how='left').drop(columns=[ + 'mask_id', 'session', 'scan_idx', 'field' + ]) + + coreg_units = coreg[ + (coreg['session']==session) & + (coreg['scan_idx']==scan_idx) & + (coreg['field'] == field) + ][['target_id', 'unit_id']] + + if len(coreg_units): + ps_df_with_units = ps_df_with_units.merge(coreg_units, on='unit_id').rename( + columns={ + 'target_id': 'auto_match_cave_nuclei_id', + 'cave_ids': 'manual_match_cave_nuclei_id' + } + ) + + description = {x: "Placeholder" for x in ps_df_with_units.columns} + new_ps = create_new_plane_segmentation(ps, ps_df_with_units, description) + image_segmentation.plane_segmentations.add(new_ps) + + return nwb \ No newline at end of file diff --git a/000402/MICrONS/coregistration/microns_nwb_coreg_notebook.ipynb b/000402/MICrONS/coregistration/microns_nwb_coreg_notebook.ipynb new file mode 100644 index 0000000..7ccd931 --- /dev/null +++ b/000402/MICrONS/coregistration/microns_nwb_coreg_notebook.ipynb @@ -0,0 +1,1023 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "ca96fadf", + "metadata": {}, + "source": [ + "> Copyright 2023 The Johns Hopkins University Applied Physics Laboratory LLC \n", + ">\n", + "> All rights reserved. \n", + ">\n", + "> Permission is hereby granted, free of charge, to any person obtaining a copy of this \n", + "software and associated documentation files (the \"Software\"), to deal in the Software \n", + "without restriction, including without limitation the rights to use, copy, modify, \n", + "merge, publish, distribute, sublicense, and/or sell copies of the Software, and to \n", + "permit persons to whom the Software is furnished to do so.\n", + ">\n", + "> THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, \n", + "INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR \n", + "PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE \n", + "LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, \n", + "TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE \n", + "OR OTHER DEALINGS IN THE SOFTWARE." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "01715644", + "metadata": {}, + "source": [ + "# MICrONS Co-registration and Visualization\n", + "\n", + "This notebook uses the `dandi`-hosted [MICrONS functional data](https://dandiarchive.org/dandiset/000402/draft) and `bossdb`-hosted [MICrONS structural data](https://bossdb.org/microns/minnie) to examine and visualize co-registered cells.\n", + "\n", + "More specifically, we update the PlaneSegmentation tables from the MICrONS DandiSet with the automated coregistration IDs in the `apl_functional_coreg_forward_v5` CAVE table. This notebook demonstrates updating these IDs for a random session and scan, plotting two functional traces for connected neurons, and visualizing those two neurons in Neuroglancer. To analyze a specific session and scan, switch out the call to `random()` with the session and scan numbers of interest.\n", + "\n", + "This notebook requires the file 'ScanUnit.pkl' to properly execute. This file contains a dataframe mapping the mask IDs (which are session/scan/field specific) to Unit IDs (which are session/sca specific.)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6ba8ed0b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import urllib.parse\n", + "import json\n", + "\n", + "from dandi.dandiapi import DandiAPIClient\n", + "from caveclient import CAVEclient\n", + "\n", + "from fsspec.implementations.cached import CachingFileSystem\n", + "from fsspec import filesystem\n", + "from h5py import File\n", + "from pynwb import NWBHDF5IO\n", + "from pynwb.file import NWBFile\n", + "from pynwb.ophys import PlaneSegmentation\n", + "\n", + "\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "30230088", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(24601)" + ] + }, + { + "cell_type": "markdown", + "id": "8484bd8a-41bb-4f65-854e-794241a9674e", + "metadata": {}, + "source": [ + "### Open the CAVE table with the structural / functional coregistration metadata\n", + "\n", + "The table below lists metadata for each neuron nucleus id which has been coregistered with a functional unit id. The `target_id` field is unique to a specific neuron. The `session`, `scan_idx`, and `unit_id` fields can be used to match a nucleus id to a functional mask ID." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0b5ed887-912f-43a1-bd84-090d83c04d6c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " session scan_idx unit_id field mask_id um_x um_y um_z px_x px_y \\\n", + "0 4 7 1 1 1 -778 -771 80 7 5 \n", + "1 4 7 2 1 2 -741 -766 80 22 7 \n", + "2 4 7 3 1 3 -691 -776 80 41 3 \n", + "3 4 7 4 1 4 -691 -766 80 42 8 \n", + "4 4 7 5 1 5 -702 -767 80 37 7 \n", + "\n", + " ms_delay \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scan_units = pd.read_pickle(\"./ScanUnit.pkl\")\n", + "scan_units.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c5b8c504-18ec-4efd-8b1f-e397df594077", + "metadata": {}, + "source": [ + "### Open the dandiset of interest\n", + "\n", + "The Microns two-photon calcium scans are found in dandiset 000402. All sessions and scans can be browsed at https://dandiarchive.org/dandiset/000402/0.230307.2132/files?location=. We will choose an entry at random from the coregistration table and open the NWB file which holds its functional data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fb4a1966", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(7, 4, 9291)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "coreg_row = coreg.sample(1).iloc[0] # randomly select a row from the coregisration table\n", + "\n", + "session_no, scan_no, unit_id = coreg_row[\"session\"], coreg_row[\"scan_idx\"], coreg_row[\"unit_id\"] \n", + "\n", + "# access scan unit row associated with the coregistration one\n", + "scan_unit_row = scan_units[(scan_units['session']==session_no)&(scan_units['scan_idx']==scan_no)&(scan_units['unit_id']==unit_id)]\n", + "\n", + "dandiset_id = \"000402\"\n", + "\n", + "display((session_no, scan_no, unit_id))" + ] + }, + { + "cell_type": "markdown", + "id": "ba138bf1-2ca2-42de-b124-5ae342eed3e0", + "metadata": {}, + "source": [ + "Since each file is tens of GB large, use the NWB streaming capability to open the file without reading all of it into memory at once, as explained at https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cdd47621", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/xenesd1/miniconda3/envs/neurodatarehack2023/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.8.0 is already loaded.\n", + " return func(args[0], **pargs)\n", + "/Users/xenesd1/miniconda3/envs/neurodatarehack2023/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'core' version 2.5.0 because version 2.6.0-alpha is already loaded.\n", + " return func(args[0], **pargs)\n", + "/Users/xenesd1/miniconda3/envs/neurodatarehack2023/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.5.0 is already loaded.\n", + " return func(args[0], **pargs)\n" + ] + } + ], + "source": [ + "# Get the location of the file on DANDI\n", + "def get_NWBFile(session_no, scan_no):\n", + " file_path = f\"sub-17797/sub-17797_ses-{session_no}-scan-{scan_no}_behavior+image+ophys.nwb\"\n", + " with DandiAPIClient() as client:\n", + " asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(file_path)\n", + " s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)\n", + "\n", + " # First, create a virtual filesystem based on the http protocol\n", + " fs = filesystem(\"http\")\n", + "\n", + " # Create a cache to save downloaded data to disk (optional)\n", + " fs = CachingFileSystem(\n", + " fs=fs,\n", + " cache_storage=\"nwb-cache\", # Local folder for the cache\n", + " )\n", + "\n", + " # Next, open the file with NWBHDF5IO\n", + " file_system = fs.open(s3_url, \"rb\")\n", + " file = File(file_system, mode=\"r\")\n", + " io = NWBHDF5IO(file=file, load_namespaces=True)\n", + "\n", + " microns_data = io.read()\n", + " return microns_data\n", + "\n", + "microns_data = get_NWBFile(session_no, scan_no)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9f3b7662-27fc-41bf-b710-a57acc13abb7", + "metadata": { + "tags": [] + }, + "source": [ + "### Update the NWB File\n", + "\n", + "The `NWBFile` object contains all the metadata associated with the dandiset. The attribute `processing -> ophys -> data_interfaces -> ImageSegmentation -> plane_segmentations` contains the structural ids associated with the image masks. The first step this notebook will take is to update each `PlaneSegmentation` object using the new structural ids which were published in 2023. These are found in the CAVE table `apl_functional_coreg_forward_v5`, column `target_id`. While we're in there, also add the unit IDs and other metadata from the CAVE table as columns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "12698eb3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# The following cell depends on these functions\n", + "def create_new_plane_segmentation(old, df, descriptions):\n", + " ps = PlaneSegmentation(\n", + " name=old.name, \n", + " description=old.description, \n", + " imaging_plane=old.imaging_plane,\n", + " id=df.index.tolist()\n", + " )\n", + " \n", + " for col in df.columns:\n", + " if col in old.colnames:\n", + " old_col = find_column_by_name(old, col)\n", + " ps.add_column(name=old_col.name, description=old_col.description, data=df[col].tolist())\n", + " else:\n", + " ps.add_column(name=col, description=descriptions[col], data=df[col].tolist())\n", + " return ps\n", + " \n", + "\n", + "def find_column_by_name(table,col_name):\n", + " for c in table.columns:\n", + " if c.name == col_name:\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8ceaad08", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Update the PlaneSegmentation objects in the NWB file\n", + "def update_microns_nwb_file(\n", + " nwb: NWBFile,\n", + " coreg,\n", + " scan_units,\n", + "):\n", + " \n", + " session, scan_idx = int(nwb.session_id.split('-')[0]), int(nwb.session_id.split('-')[2])\n", + " if session == 5 and scan_idx == 7:\n", + " print(\"Error: This file does not contain a unit_id column\")\n", + " return \n", + " scan_units_modified = scan_units[(scan_units['session']==session) & (scan_units['scan_idx']==scan_idx)]\n", + " \n", + " image_segmentation = nwb.processing[\"ophys\"].data_interfaces[\"ImageSegmentation\"]\n", + " \n", + " all_ps = list(image_segmentation.plane_segmentations)\n", + " for ps_name in tqdm(all_ps):\n", + " \n", + " ps = image_segmentation.plane_segmentations.pop(ps_name)\n", + " field = int(ps_name[-1])\n", + " field_scan_units = scan_units_modified[scan_units_modified['field'] == field]\n", + " ps_df = ps[:]\n", + " ps_df['mask_id'] = ps_df.index\n", + " ps_df_with_units = ps_df.merge(field_scan_units, on='mask_id', how='left').drop(columns=[\n", + " 'mask_id', 'session', 'scan_idx'\n", + " # 'mask_id', 'session', 'scan_idx', 'field'\n", + " ])\n", + " \n", + " coreg_units = coreg[\n", + " (coreg['session']==session) & \n", + " (coreg['scan_idx']==scan_idx) & \n", + " (coreg['field'] == field)\n", + " ][['target_id', 'unit_id']]\n", + " \n", + " if len(coreg_units):\n", + " ps_df_with_units = ps_df_with_units.merge(coreg_units, on='unit_id').rename(\n", + " columns={\n", + " 'target_id': 'auto_match_cave_nuclei_id', \n", + " 'cave_ids': 'manual_match_cave_nuclei_id'\n", + " }\n", + " )\n", + " \n", + " description = {x: \"Placeholder\" for x in ps_df_with_units.columns}\n", + " new_ps = create_new_plane_segmentation(ps, ps_df_with_units, description)\n", + " image_segmentation.plane_segmentations.add(new_ps)\n", + " \n", + " return nwb" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d0bc04e0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:23<00:00, 2.89s/it]\n" + ] + } + ], + "source": [ + "# If this fails with \"This file does not contain a unit_id column\", then session 5 scan 7 was chosen.\n", + "# This file does not contain unit_ids, so a new random neuron must be chosen.\n", + "# Rerun the cells in Open the Dandiset of Interest to choose a new random neuron, then try this cell again.\n", + "microns_data = update_microns_nwb_file(microns_data, coreg, scan_units)" + ] + }, + { + "cell_type": "markdown", + "id": "a1ded545-8fd3-4c8b-b932-3f30ad559a94", + "metadata": {}, + "source": [ + "### Visualization\n", + "\n", + "The functional data contains multiple types of data for a single mask ID. Plot the image of the mask and its fluorescence trace together." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "244ae432", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Return the mask image, fluorescence trace values, and timestamps for a given unit id\n", + "def get_time_series_data(unit_id, microns_data):\n", + " mask_id = 0\n", + " for plane_seg in range(1, 9):\n", + " image_seg = microns_data.processing[\"ophys\"].data_interfaces[\"ImageSegmentation\"].plane_segmentations[f\"PlaneSegmentation{plane_seg}\"]\n", + " try:\n", + " mask_id = image_seg.get(\"unit_id\").data.index(unit_id)\n", + " except:\n", + " continue\n", + " else:\n", + " break\n", + "\n", + " if mask_id == 0:\n", + " return Exception(f\"Unit Id {unit_id} not found\")\n", + "\n", + " # Get fluorescence of the table number\n", + " fluor = microns_data.processing[\"ophys\"].data_interfaces[\"Fluorescence\"].roi_response_series[f\"RoiResponseSeries{plane_seg}\"]\n", + "\n", + " # Pull the data we are interested in\n", + " mask_image = image_seg.image_mask[mask_id]\n", + " fluor_data = fluor.data[:, mask_id]\n", + " timestamps = fluor.timestamps[:]\n", + "\n", + " return mask_image, fluor_data, timestamps" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c8708986", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'response magnitude')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the segmentation and fluorescence trace for the unit id\n", + "mask_image, fluor, timestamps = get_time_series_data(unit_id, microns_data)\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 8))\n", + "fig.suptitle(f'session: {session_no}, scan: {scan_no}, unit_id: {unit_id}')\n", + "ax1.imshow(mask_image)\n", + "ax2.plot(timestamps, fluor)\n", + "ax2.set_xlabel(\"time (s)\")\n", + "ax2.set_ylabel(\"response magnitude\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d74cd342", + "metadata": {}, + "source": [ + "## Neuroglancer Visualization \n", + "Generate JSON states associated with the functional and structural information for a unit. Unit info is formatted in the tuple `(session_id, scan_id, field_id, unit_field_id)`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fdc76363", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Table Owner Notice on nucleus_neuron_svm: Please cite https://doi.org/10.1101/2022.07.20.499976 when using this table.\n" + ] + } + ], + "source": [ + "from ng_utils import generate_functional_ng_state, generate_structural_ng_state, save_json" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0b1395f5", + "metadata": {}, + "outputs": [], + "source": [ + "# The field ID is a new identifier not mentioned yet in this notebook. It is important for pulling the correct Neuroglancer layers.\n", + "# It was added as a column when we modified the PlaneSegmentation objects. Index into this column to grab the correct field ID.\n", + "def get_field_id(unit_id, microns_data):\n", + " mask_id = 0\n", + " field_id = 0\n", + " for plane_seg in range(1, 9):\n", + " image_seg = microns_data.processing[\"ophys\"].data_interfaces[\"ImageSegmentation\"].plane_segmentations[f\"PlaneSegmentation{plane_seg}\"]\n", + " try:\n", + " mask_id = image_seg.get(\"unit_id\").data.index(unit_id)\n", + " except:\n", + " continue\n", + " else:\n", + " field_id = image_seg.get(\"field\").data[mask_id]\n", + " break\n", + "\n", + " if mask_id == 0:\n", + " return Exception(f\"Unit Id {unit_id} not found\")\n", + "\n", + " return field_id" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d85608e8-597a-4d6d-a170-78f58f960f30", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate Neuroglancer JSON states\n", + "# States will be saved to a file that can be copied into Neuroglancer's state editor\n", + "\n", + "unit_info = (session_no, scan_no, get_field_id(unit_id, microns_data), unit_id)\n", + "\n", + "\"\"\"\n", + "Functional Test\n", + "\"\"\"\n", + "funct_ng_state, target_id = generate_functional_ng_state(unit_info)\n", + "save_json(f'functional_state_{\"_\".join([str(x) for x in unit_info])}.json', funct_ng_state)\n", + "\n", + "\"\"\"\n", + "Structural Test\n", + "\"\"\"\n", + "struct_ng_state = generate_structural_ng_state(target_id)\n", + "save_json(f'structural_state_id_{\"_\".join([str(x) for x in unit_info])}.json', struct_ng_state)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4ded4802", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Alternatively, run this cell to open in browser\n", + "import webbrowser\n", + "funct_url = 'https://neuroglancer.neuvue.io/#!' + urllib.parse.quote(json.dumps(funct_ng_state))\n", + "struct_url = 'https://neuroglancer.neuvue.io/#!' + urllib.parse.quote(json.dumps(struct_ng_state))\n", + "webbrowser.open(funct_url)\n", + "webbrowser.open(struct_url)" + ] + }, + { + "cell_type": "markdown", + "id": "5be05b9c-14b9-4cbe-a397-28df9f28d86b", + "metadata": {}, + "source": [ + "### Connectivity-informed functional analysis\n", + "\n", + "We can use the `pt_root_id` column in the coregistration table we pulled at the very beginning of the notebook to generate a list of neurons which synapse onto our neuron of interest, along with the quantity and pre/post designation of each synapse." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "71147e46-5ea3-486b-9ee8-cf6c87159387", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def generate_synapse_map(pt_root_id):\n", + " return {\n", + " 'pre': cave.materialize.query_table(\"synapses_pni_2\", \n", + " filter_equal_dict={\"post_pt_root_id\": str(pt_root_id)})['pre_pt_root_id'].value_counts().to_dict(),\n", + " 'post': cave.materialize.query_table(\"synapses_pni_2\", \n", + " filter_equal_dict={\"pre_pt_root_id\": str(pt_root_id)})['post_pt_root_id'].value_counts().to_dict()\n", + " }\n", + "\n", + "pt_root_id = coreg_row[\"pt_root_id\"]\n", + "synapse_map = generate_synapse_map(pt_root_id)\n", + "\n", + "# Remove neuron cycles first if they exist.\n", + "del synapse_map['pre'][pt_root_id]\n", + "del synapse_map['post'][pt_root_id]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fedf498d-5e9b-472d-b9cc-97a8c2dc907a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/xenesd1/miniconda3/envs/neurodatarehack2023/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.5.1 because version 1.8.0 is already loaded.\n", + " return func(args[0], **pargs)\n", + "/Users/xenesd1/miniconda3/envs/neurodatarehack2023/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'core' version 2.5.0 because version 2.6.0-alpha is already loaded.\n", + " return func(args[0], **pargs)\n", + "/Users/xenesd1/miniconda3/envs/neurodatarehack2023/lib/python3.11/site-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.2.0 because version 0.5.0 is already loaded.\n", + " return func(args[0], **pargs)\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:16<00:00, 2.10s/it]\n" + ] + } + ], + "source": [ + "# Find a functionally characterized neuron in the list of post synaptic neurons\n", + "for post_pt_root_id in synapse_map['post']:\n", + " coreg_row_2 = coreg.loc[coreg[\"pt_root_id\"] == post_pt_root_id].head(1)\n", + " if len(coreg_row_2) > 0: break\n", + " \n", + "# Repeat the steps to pull the NWB file for the neuron\n", + "scan_unit_row_2 = scan_units.loc[coreg_row_2.index[0]]\n", + "\n", + "session_no_2 = scan_unit_row_2[\"session\"]\n", + "scan_no_2 = scan_unit_row_2[\"scan_idx\"]\n", + "unit_id_2 = scan_unit_row_2[\"unit_id\"]\n", + "\n", + "microns_data_2 = get_NWBFile(session_no_2, scan_no_2)\n", + "microns_data_2 = update_microns_nwb_file(microns_data_2, coreg, scan_units)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "93db8e18", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the two functional traces on top of each other\n", + "\n", + "mask_image2, fluor2, timestamps2 = get_time_series_data(unit_id_2, microns_data_2)\n", + "\n", + "plt.plot(fluor, label=f\"Session {session_no}, Scan {scan_no}, Unit Id {unit_id}\", alpha=0.5)\n", + "plt.plot(fluor2, label=f\"Session {session_no_2}, Scan {scan_no_2}, Unit Id {unit_id_2}\", alpha=0.5)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Response Magnitude\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "microns-coreg-env", + "language": "python", + "name": "microns-coreg-env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "a1a3b2ec9f69649c0500eeac98d17fbbf901b93530ebe3fc8fe808263e60f6de" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/000402/MICrONS/coregistration/ng_utils.py b/000402/MICrONS/coregistration/ng_utils.py new file mode 100644 index 0000000..e0e86ab --- /dev/null +++ b/000402/MICrONS/coregistration/ng_utils.py @@ -0,0 +1,78 @@ +from caveclient import CAVEclient +from datetime import datetime +import numpy as np +import json + +""" +Table Queries +""" +client = CAVEclient("minnie65_phase3_v1") +apl_functional_coreg_forward_v5 = client.materialize.live_query('apl_functional_coreg_forward_v5', timestamp=datetime.utcnow()) +nucleus_neuron_svm = client.materialize.live_query('nucleus_neuron_svm', timestamp=datetime.utcnow()) + +MINNIE_RESOLUTION = np.array([4,4,40]) + +def generate_functional_ng_state(unit_info, table = apl_functional_coreg_forward_v5): + unit_info = [int(x) for x in unit_info] + ref_ng_state = json.load(open('ng_visualization/functional_base_state.json','r')) + fields = ref_ng_state['layers'][1]['source'] + base_url = 'precomputed://s3://bossdb-open-data/iarpa_microns/minnie/functional_data/field_seg_masks/' + + def gen_compact_roi_layer(field_list): + source_list = [] + for field in field_list: + field = field.copy() + field_name = field['url'].split('/')[-1] + field['url'] = base_url+field_name + source_list.extend([field]) + + return {'type':"segmentation", + "source":source_list, + "tab":"source", + "segments": [], + "segmentQuery":[], + "name":"all_annotations", + "visible": False} + + def get_roi_layer(field, unit_field_id): + field_name = field['url'].split('/')[-1] + field['url'] = base_url+field_name + return {'type':"segmentation", + "source":field, + "tab":"source", + "segments": [unit_field_id], + "segmentQuery":[], + "name":field_name + '_anno'} + + ref_ng_state['layers'].extend([gen_compact_roi_layer(fields)]) + session_id, scan_id, field_id, unit_field_id = unit_info + unit_id_row = table[(table['session']==session_id)&(table['field'] == field_id)&(table['scan_idx']==scan_id)&(table['unit_id'] == unit_field_id)].iloc[0] + field_obj = fields[unit_id_row['field']-1].copy() + ref_ng_state['position'] = list(np.array(field_obj['transform']['matrix'])[:,-1]) + ref_ng_state['layers'].extend([get_roi_layer(field_obj, unit_field_id=unit_field_id)]) + + target_id = unit_id_row.target_id + + return ref_ng_state, target_id + + +def generate_structural_ng_state(target_id, table = nucleus_neuron_svm): + """ + update structural ng state with associate seg_id and center at position + """ + target_id_row = table[table['id'] == target_id] + pt_root_id = target_id_row.pt_root_id.values[0] + pt_position = [int(x) for x in target_id_row.pt_position.values[0]] + base_structural_state = json.load(open('ng_visualization/structural_base_state.json','r')) + base_structural_state['layers'][1]['segments'].extend([str(pt_root_id)]) + base_structural_state['navigation']['pose']['position']['voxelCoordinates'].extend(pt_position) + + return base_structural_state + +def save_json(filename, json_data): + """ + utility function to generate jsons with states + """ + with open(filename, 'w', encoding='utf-8') as f: + json.dump(json_data, f, ensure_ascii=False, indent=4) + diff --git a/000402/MICrONS/coregistration/ng_visualization/functional_base_state.json b/000402/MICrONS/coregistration/ng_visualization/functional_base_state.json new file mode 100644 index 0000000..2548b3a --- /dev/null +++ b/000402/MICrONS/coregistration/ng_visualization/functional_base_state.json @@ -0,0 +1,391 @@ +{ + "dimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + }, + "position": [ + 251.5, + 733.5, + 359.5 + ], + "crossSectionScale": 1.5, + "projectionOrientation": [ + 1, + -1, + 0, + 0 + ], + "projectionScale": 3769.203504704791, + "layers": [ + { + "type": "image", + "source": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/stack_rgb/", + "tab": "rendering", + "shader": "\n#uicontrol invlerp red(channel=1)\n#uicontrol invlerp green(channel=0)\n\nvoid main() {\n emitRGB(vec3(red(), green(), 0));\n}\n", + "shaderControls": { + "red": { + "range": [ + 0, + 310 + ], + "window": [ + 0, + 3000 + ] + }, + "green": { + "range": [ + 0, + 1297 + ], + "window": [ + 0, + 3000 + ] + } + }, + "channelDimensions": { + "c^": [ + 1, + "" + ] + }, + "name": "stack" + }, + { + "type": "image", + "source": [ + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_1", + "transform": { + "matrix": [ + [ + 1.01366, + -0.00826935, + 0, + 166.40623782499995 + ], + [ + -0.000894356, + 0.98838, + 0, + 77.15399318199991 + ], + [ + 0.00191511, + -0.00646626, + 1, + 154.520483325 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_2", + "transform": { + "matrix": [ + [ + 1.01349, + -0.0118541, + 0, + 718.99737295 + ], + [ + -0.00302889, + 0.996249, + 0, + 58.78261595499998 + ], + [ + -0.0106083, + 0.00570692, + 1, + 148.77131630999997 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_3", + "transform": { + "matrix": [ + [ + 1.01323, + -0.00685865, + 0, + 159.50214317500001 + ], + [ + 0.000692124, + 0.989937, + 0, + 72.849406122 + ], + [ + 0.00580975, + -0.00289405, + 1, + 219.38116284999998 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_4", + "transform": { + "matrix": [ + [ + 1.01213, + -0.00931375, + 0, + 716.177770625 + ], + [ + -0.000600391, + 0.996653, + 0, + 56.28499751449999 + ], + [ + -0.00339025, + 0.00593962, + 1, + 215.435461185 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_5", + "transform": { + "matrix": [ + [ + 1.01226, + -0.00449916, + 0, + 146.56281841999999 + ], + [ + 0.00356799, + 0.993619, + 0, + 65.7370665950001 + ], + [ + 0.000355121, + 0.00413598, + 1, + 355.4523690405 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_6", + "transform": { + "matrix": [ + [ + 1.0096, + -0.00627319, + 0, + 711.706717905 + ], + [ + 0.00257479, + 0.998612, + 0, + 51.96980849500005 + ], + [ + -0.00491603, + 0.00682901, + 1, + 353.52097029 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_7", + "transform": { + "matrix": [ + [ + 1.01126, + -0.00358033, + 0, + 133.42042133500001 + ], + [ + 0.00397489, + 0.997661, + 0, + 60.82505204500001 + ], + [ + 0.0000287759, + 0.0000668757, + 1, + 497.9353456618 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + }, + { + "url": "n5://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/functional_data/field_summaries/session_7_scan_4_field_8", + "transform": { + "matrix": [ + [ + 1.00818, + -0.00482558, + 0, + 707.2639462099999 + ], + [ + 0.00418602, + 1.00025, + 0, + 48.71005180999998 + ], + [ + 0.0000741406, + 0.0000185663, + 1, + 494.07285130245 + ] + ], + "outputDimensions": { + "x": [ + 0.000001, + "m" + ], + "y": [ + 0.000001, + "m" + ], + "z": [ + 0.000001, + "m" + ] + } + } + } + ], + "tab": "source", + "opacity": 0.81, + "name": "ses7_scan4" + } + ], + "showAxisLines": false, + "layout": "xy", + "layerListPanel": { + "visible": true + } + } \ No newline at end of file diff --git a/000402/MICrONS/coregistration/ng_visualization/structural_base_state.json b/000402/MICrONS/coregistration/ng_visualization/structural_base_state.json new file mode 100644 index 0000000..cdf98dd --- /dev/null +++ b/000402/MICrONS/coregistration/ng_visualization/structural_base_state.json @@ -0,0 +1,80 @@ +{ + "layers": [ + { + "source": "precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/em", + "type": "image", + "blend": "default", + "shader": "#uicontrol float black slider(min=0, max=1, default=0.0)\n#uicontrol float white slider(min=0, max=1, default=1.0)\nfloat rescale(float value) {\n return (value - black) / (white - black);\n}\nvoid main() {\n float val = toNormalized(getDataValue());\n if (val < black) {\n emitRGB(vec3(0,0,0));\n } else if (val > white) {\n emitRGB(vec3(1.0, 1.0, 1.0));\n } else {\n emitGrayscale(rescale(val));\n }\n}\n", + "shaderControls": { + "black": 0.35, + "white": 0.6 + }, + "name": "img" + }, + { + "source": "graphene://https://minnie.microns-daf.com/segmentation/table/minnie3_v1", + "type": "segmentation_with_graph", + "selectedAlpha": 0.3, + "segments": [], + "skeletonRendering": { + "mode2d": "lines_and_points", + "mode3d": "lines" + }, + "graphOperationMarker": [ + { + "annotations": [], + "tags": [] + }, + { + "annotations": [], + "tags": [] + } + ], + "pathFinder": { + "color": "#ffff00", + "pathObject": { + "annotationPath": { + "annotations": [], + "tags": [] + }, + "hasPath": false + } + }, + "name": "seg" + }, + { + "type": "annotation", + "annotations": [], + "annotationTags": [], + "voxelSize": [ + 4, + 4, + 40 + ], + "bracketShortcutsShowSegmentation": true, + "annotationSelectionShowsSegmentation": true, + "name": "ann" + } + ], + "navigation": { + "pose": { + "position": { + "voxelSize": [ + 4, + 4, + 40 + ], + "voxelCoordinates": [] + } + }, + "zoomFactor": 433.3084653616374 + }, + "perspectiveZoom": 774.0839833480868, + "showSlices": false, + "jsonStateServer": "https://global.daf-apis.com/nglstate/api/v1/post", + "selectedLayer": { + "layer": "seg", + "visible": true + }, + "layout": "4panel" + } \ No newline at end of file diff --git a/000402/MICrONS/coregistration/requirements.txt b/000402/MICrONS/coregistration/requirements.txt new file mode 100644 index 0000000..c0148e0 --- /dev/null +++ b/000402/MICrONS/coregistration/requirements.txt @@ -0,0 +1,6 @@ +matplotlib>=3.8.3 +pynwb==2.5.0 +dandi==0.59.1 +cloud-volume==8.29.1 +caveclient==5.15.2 +fsspec==2024.2.0 From 7c4b3d1fee66ce52ac41a7807a7bbbb4e97199f6 Mon Sep 17 00:00:00 2001 From: Ben Dichter Date: Tue, 27 Aug 2024 14:11:15 -0400 Subject: [PATCH 2/2] Update 000402/MICrONS/coregistration/README.md --- 000402/MICrONS/coregistration/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/000402/MICrONS/coregistration/README.md b/000402/MICrONS/coregistration/README.md index 8715538..b8efbb0 100644 --- a/000402/MICrONS/coregistration/README.md +++ b/000402/MICrONS/coregistration/README.md @@ -11,7 +11,7 @@ Our goal with this notebook to provide a seamless integration of generate novel ## How to run You'll need a fresh python virtual environment (ver. 3.8 or higher) to run this notebook. -1. Install python requirements from `requirements.txt` or through conda from the `enviornment.yml` file. +1. Install python requirements from `requirements.txt` or through conda from the `environment.yml` file. 2. Run `microns_nwb_coreg_notebook` notebook for a complete end-to-end demo of how to integrate the latest co-registed cells from CAVE with the MICrONS functiondal data stored in DANDI. 3. Feel free to import/modify `microns_nwb_coreg.py` or `ng_utils.py` script functions for your own use-case!