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Add pretrained model download step to example notebooks (#20)
* Initial plan * Add pretrained model download cells to example notebooks 01-07 Co-authored-by: lappalainenj <34949352+lappalainenj@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: lappalainenj <34949352+lappalainenj@users.noreply.github.com>
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‎examples/01_flyvision_connectome.ipynb‎

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" !pip install -e .[examples]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94376496",
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"metadata": {
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"tags": [
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"hide"
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]
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},
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"source": [
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"**Download pretrained models**\n",
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"\n",
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"Running the code block below will download the pretrained models needed for this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b5337bfa",
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"metadata": {
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"cellView": "form",
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"tags": [
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"hide"
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]
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},
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"outputs": [],
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"source": [
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"# @markdown **Download pretrained models**\n",
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"!flyvis download-pretrained"
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]
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},
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{
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"cell_type": "markdown",
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"id": "899b7f6e",

‎examples/02_flyvision_optic_flow_task.ipynb‎

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" !pip install -e .[examples]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2d098165",
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"metadata": {
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"tags": [
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"hide"
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]
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},
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"source": [
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"**Download pretrained models**\n",
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"\n",
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"Running the code block below will download the pretrained models needed for this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f2260977",
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"metadata": {
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"cellView": "form",
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"tags": [
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"hide"
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]
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},
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"outputs": [],
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"source": [
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"# @markdown **Download pretrained models**\n",
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"!flyvis download-pretrained"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d6e57181",
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_9c5b27045cae43fdaeaab6818be0c798",
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"placeholder": "​",
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"placeholder": "\u200b",
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"style": "IPY_MODEL_14936f9c260b4802b0756c15436e6b92",
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"tabbable": null,
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"tooltip": null,
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_8cd53e9b306b443491663dbfe5b2a1a9",
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"placeholder": "​",
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"placeholder": "\u200b",
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"style": "IPY_MODEL_159aa73c20d145f2ba32f713f3a3c14d",
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_7f0cea8f331e4c2c984aabe74c1c4f83",
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"placeholder": "​",
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"placeholder": "\u200b",
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"style": "IPY_MODEL_5491c0996f9f49bb810a2e2bb79d9560",
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_3773b17f16164007b1396a5ab6bd5097",
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"placeholder": "\u200b",
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"style": "IPY_MODEL_5c3d72509ffa44c3a65551607705c0de",
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"tabbable": null,
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"tooltip": null,

‎examples/03_flyvision_flash_responses.ipynb‎

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" !pip install -e .[examples]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "069f9de4",
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"metadata": {
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"tags": [
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"hide"
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]
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},
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"source": [
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"**Download pretrained models**\n",
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"\n",
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"Running the code block below will download the pretrained models needed for this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "102a35f3",
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"metadata": {
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"cellView": "form",
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"tags": [
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"hide"
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]
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},
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"outputs": [],
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"source": [
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"# @markdown **Download pretrained models**\n",
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"!flyvis download-pretrained"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_3f5f9c5b2d874431ad45cfadafb685c8",
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"placeholder": "\u200b",
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"style": "IPY_MODEL_a78b5b586dc8471aa441463b84d1d807",
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"tabbable": null,
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_2c46b6ae58704facb18ed897dd80d056",
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"style": "IPY_MODEL_10d252043c444e7b8cd88111941f16f4",
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"tabbable": null,
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‎examples/04_flyvision_moving_edge_responses.ipynb‎

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" !pip install -e .[examples]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f1778a02",
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"metadata": {
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"tags": [
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"hide"
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]
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},
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"source": [
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"**Download pretrained models**\n",
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"\n",
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"Running the code block below will download the pretrained models needed for this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb65fc39",
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"metadata": {
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"cellView": "form",
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"tags": [
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"hide"
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]
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},
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"outputs": [],
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"source": [
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"# @markdown **Download pretrained models**\n",
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"!flyvis download-pretrained"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"description": "",
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"description_allow_html": false,
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"layout": "IPY_MODEL_e205e4e08ab64cf6801cc4a0d68224c8",
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"placeholder": "\u200b",
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"style": "IPY_MODEL_6ae6eec453d247b4b5991670378c8f2d",
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"description": "",
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"layout": "IPY_MODEL_e99a82e9ad0d41cb9c660b6634a2b785",
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"description": "",
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"description_allow_html": false,
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"description": "",
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"layout": "IPY_MODEL_9170dcf8c84e4271aefb78bb06162329",
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‎examples/05_flyvision_umap_and_clustering_models.ipynb‎

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" !pip install -e .[examples]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "23e17381",
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"metadata": {
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"tags": [
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"hide"
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]
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},
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"source": [
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"**Download pretrained models**\n",
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"\n",
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"Running the code block below will download the pretrained models needed for this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bb122ba1",
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"metadata": {
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"cellView": "form",
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"tags": [
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"hide"
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]
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},
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"outputs": [],
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"source": [
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"# @markdown **Download pretrained models**\n",
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"!flyvis download-pretrained"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"description": "",
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"layout": "IPY_MODEL_86ed2280559c40738790ec88dd6f829e",
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"description": "",
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‎examples/06_flyvision_maximally_excitatory_stimuli.ipynb‎

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" !pip install -e .[examples]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fd7f30a8",
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"metadata": {
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"tags": [
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"hide"
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]
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},
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"source": [
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"**Download pretrained models**\n",
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"\n",
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"Running the code block below will download the pretrained models needed for this notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "03b2b984",
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"metadata": {
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"cellView": "form",
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"tags": [
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"hide"
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]
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},
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"outputs": [],
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"source": [
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"# @markdown **Download pretrained models**\n",
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"!flyvis download-pretrained"
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]
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},
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{
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"cell_type": "markdown",
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"id": "abb48f9d",

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