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Caitlin Lewis
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add neuro example for line collection cmapping
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‎lines.ipynb‎

Lines changed: 299 additions & 4 deletions
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@@ -20,7 +20,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "177e0cc3-870c-4570-bf59-af3528ba94bf",
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"metadata": {},
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"outputs": [],
@@ -563,7 +563,7 @@
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"plot = fpl.Plot()\n",
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"\n",
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"# highest values, lowest values, mid-high values, mid values\n",
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"cmap_values = cmap_values = [\n",
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"cmap_values = [\n",
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" 0, 1, 1, 2,\n",
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" 0, 0, 1, 1,\n",
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" 2, 2, 3, 3,\n",
@@ -648,6 +648,289 @@
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"sc.close()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "54374981-9a79-4887-a297-8a5012ebb4b7",
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"metadata": {},
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"source": [
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"## neuro data example"
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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": 48,
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"id": "6901f4b7-d1f8-4747-b8b0-de078ae4c825",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# load in movie \n",
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"movie = np.load('/home/clewis7/repos/fastplotlib-scipy2023/data/rcm.npy')\n",
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"\n",
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"# load in identified nuerons\n",
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"contours = np.load('/home/clewis7/repos/fastplotlib-scipy2023/data/contours.npy', allow_pickle=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "606c769a-9c63-4c3f-ace7-a5f4721b5ee5",
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"metadata": {},
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"source": [
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"### labeling good and bad components"
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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": 50,
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"id": "6a9005e6-2c6b-4561-8890-0a576273e609",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# indices of good components\n",
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"good_ixs = np.load('/home/clewis7/repos/fastplotlib-scipy2023/data/good_ixs.npy')"
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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": 51,
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"id": "7c8eaeac-3f4a-45aa-8a85-ad319c25143d",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# initialize colors and set good components as green\n",
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"colors = np.tile([255, 0, 0, 1], 155).reshape(155, 4)\n",
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"colors[good_ixs] = [0, 255, 0, 1]"
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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": 52,
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"id": "162a1602-2201-45be-bcc2-6fd7550cf0e4",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8990b15d33c440c786e26fbd356002d2",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"RFBOutputContext()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
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" warn(f\"converting {array.dtype} array to float32\")\n",
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"/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:31: UserWarning: converting int64 array to int32\n",
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" warn(f\"converting {array.dtype} array to int32\")\n"
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]
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}
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],
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"source": [
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"# for the image data and contours\n",
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"cnmf_iw = fpl.ImageWidget(movie, vmin_vmax_sliders=True, cmap=\"gnuplot2\")\n",
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"\n",
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"# add contours to the plot within the widget\n",
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"contours_graphic = cnmf_iw.gridplot[0,0].add_line_collection(\n",
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" contours, \n",
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" colors=colors, \n",
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" thickness=1.1, \n",
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" name=\"contours\"\n",
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")\n",
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"\n",
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"# stack the plots and show them in sidecar\n",
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"sc = Sidecar(title=\"good/bad comps\")\n",
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"\n",
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"with sc:\n",
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" display(cnmf_iw.show())"
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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": 53,
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"id": "2e34929f-55dd-43d5-8736-8253ad11d96e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"cnmf_iw.vmin_vmax_sliders[0].value = (-5, 15)"
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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": 54,
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"id": "60ec0022-857e-46e7-abd3-17bf852bd6e6",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"cnmf_iw.gridplot.close()\n",
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"sc.close()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a254ed4-370c-4b5a-8c55-8b9603af7816",
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"metadata": {},
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"source": [
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"### mapping eval metrics to cmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2516bb91-04f3-4c65-ba17-7f1cc1c7188f",
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"metadata": {},
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"source": [
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"#### cnn thresholds"
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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": 55,
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"id": "b1319e04-e1b6-4c11-a887-314dba7d60fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# load in CNN predictions\n",
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"cnn_preds = np.load('/home/clewis7/repos/fastplotlib-scipy2023/data/cnn_preds.npy')"
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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": 56,
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"id": "8f532d3f-aed5-4dd5-b433-0a76f429022a",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "952f12e9654a4e968ab42a80e0e32e9f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"RFBOutputContext()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# for the image data and contours\n",
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"cnmf_iw = fpl.ImageWidget(movie, vmin_vmax_sliders=True, cmap=\"gnuplot2\")\n",
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"\n",
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"# add contours to the plot within the widget\n",
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"contours_graphic = cnmf_iw.gridplot[0,0].add_line_collection(\n",
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" contours, \n",
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" cmap_values=cnn_preds,\n",
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" cmap=\"Wistia\",\n",
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" thickness=1.1, \n",
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" name=\"contours\"\n",
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")\n",
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"\n",
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"# stack the plots and show them in sidecar\n",
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"sc = Sidecar(title=\"eval metrics\")\n",
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"\n",
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"with sc:\n",
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" display(cnmf_iw.show())"
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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": 57,
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"id": "bc7672ba-237f-46b7-9374-538babe1fdbd",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"cnmf_iw.vmin_vmax_sliders[0].value = (-5, 15)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f60c6fb2-1d73-4d8b-ac5d-3d37a216d071",
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"metadata": {},
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"source": [
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"#### r_values"
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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": 58,
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"id": "be53bd85-9d4a-415c-b70b-c38160472aa0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# load in r_vals\n",
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"r_vals = np.load('/home/clewis7/repos/fastplotlib-scipy2023/data/r_vals.npy')\n",
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"\n",
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"# remap cmap values and cmap\n",
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"contours_graphic.cmap_values = r_vals\n",
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"contours_graphic.cmap = \"cool\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "90e7e79a-2879-454a-9293-e8abda55808e",
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"metadata": {},
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"source": [
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"#### SNR comps"
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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": 59,
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"id": "3dbd7a7e-da52-4c95-a66e-020feb447080",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# load in SNR comps\n",
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"snr_comps = np.load('/home/clewis7/repos/fastplotlib-scipy2023/data/SNR_comps.npy')\n",
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"\n",
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"# remap cmap values and cmap\n",
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"contours_graphic.cmap_values = snr_comps\n",
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"contours_graphic.cmap = \"spring\""
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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": 60,
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"id": "32ed59ef-d956-4e83-b12f-8c496e1579e4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"cnmf_iw.gridplot.close()\n",
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"sc.close()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1a34cfd-3746-489e-aeff-7373d538ab8b",
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 33,
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"id": "7a3b8014-a89e-4138-98ac-ed3cbf1f0d51",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'plot' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m 2\u001b[0m sc\u001b[38;5;241m.\u001b[39mclose()\n",
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"\u001b[0;31mNameError\u001b[0m: name 'plot' is not defined"
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]
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}
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],
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"source": [
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"plot.close()\n",
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"sc.close()"

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