Topology in neuroscience
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import scipy\n",
"import pickle as pkl\n",
"import ipywidgets as widgets\n",
"from mpl_toolkits import mplot3d\n",
"from scipy import sparse\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6845348.zip\n",
"allimgs.mat\n",
"dbstims.mat\n",
"default_stim_order.mat\n",
"images_natimg2800_4D_M170714_MP032_2017-09-22.mat\n",
"images_natimg2800_4D_M170717_MP033_2017-09-19.mat\n",
"images_natimg2800_4D_M170717_MP033_2017-09-22.mat\n",
"images_natimg2800_4D_M170717_MP034_2017-09-20.mat\n",
"images_natimg2800_8D_M161025_MP030_2017-06-07.mat\n",
"images_natimg2800_8D_M170604_MP031_2017-07-02.mat\n",
"images_natimg2800_8D_M170714_MP032_2017-08-10.mat\n",
"images_natimg2800_8D_M170714_MP032_2017-09-15.mat\n",
"images_natimg2800_8D_M170717_MP033_2017-08-22.mat\n",
"images_natimg2800_8D_M170717_MP034_2017-09-15.mat\n",
"images_natimg2800_all.mat\n",
"images_natimg2800_small_M170714_MP032_2017-09-18.mat\n",
"images_natimg2800_small_M170717_MP033_2017-08-23.mat\n",
"images_natimg2800_small_M170717_MP034_2017-09-17.mat\n",
"images_natimg2800_white_all.mat\n",
"images_ori_all.mat\n",
"images_sparse_all.mat\n",
"instructions.pdf\n",
"natimg2800_4D_M170714_MP032_2017-09-22.mat\n",
"natimg2800_4D_M170717_MP033_2017-09-19.mat\n",
"natimg2800_4D_M170717_MP033_2017-09-22.mat\n",
"natimg2800_4D_M170717_MP034_2017-09-20.mat\n",
"natimg2800_8D_M161025_MP030_2017-06-07.mat\n",
"natimg2800_8D_M170604_MP031_2017-07-02.mat\n",
"natimg2800_8D_M170714_MP032_2017-08-10.mat\n",
"natimg2800_8D_M170714_MP032_2017-09-15.mat\n",
"natimg2800_8D_M170717_MP033_2017-08-22.mat\n",
"natimg2800_8D_M170717_MP034_2017-09-15.mat\n",
"natimg2800_M160825_MP027_2016-12-14.mat\n",
"natimg2800_M161025_MP030_2017-05-29.mat\n",
"natimg2800_M170604_MP031_2017-06-28.mat\n",
"natimg2800_M170714_MP032_2017-08-07.mat\n",
"natimg2800_M170714_MP032_2017-09-14.mat\n",
"natimg2800_M170717_MP033_2017-08-20.mat\n",
"natimg2800_M170717_MP034_2017-09-11.mat\n",
"natimg2800_small_M170714_MP032_2017-09-18.mat\n",
"natimg2800_small_M170717_MP033_2017-08-23.mat\n",
"natimg2800_small_M170717_MP034_2017-09-17.mat\n",
"natimg2800_white_M170714_MP032_2017-09-11.mat\n",
"natimg2800_white_M170714_MP032_2017-09-12.mat\n",
"natimg2800_white_M170717_MP033_2017-09-21.mat\n",
"natimg2800_white_M170717_MP034_2017-09-14.mat\n",
"natimg32_M150824_MP019_2016-03-23.mat\n",
"natimg32_M170604_MP031_2017-06-27.mat\n",
"natimg32_M170714_MP032_2017-08-01.mat\n",
"natimg32_M170717_MP033_2017-08-25.mat\n",
"ori32_M160825_MP027_2016-12-15.mat\n",
"ori32_M170604_MP031_2017-06-26.mat\n",
"ori32_M170714_MP032_2017-08-02.mat\n",
"ori32_M170717_MP033_2017-08-17.mat\n",
"sparseSTATS.mat\n"
]
}
],
"source": [
"!ls data/stringer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f150dc9f6a0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import scipy.io as sio\n",
"mt = sio.loadmat('data/stringer/natimg2800_M170717_MP034_2017-09-11.mat')\n",
"\n",
"### stimulus responses\n",
"resp = mt['stim'][0]['resp'][0] # stimuli by neurons\n",
"istim = mt['stim'][0]['istim'][0] # identities of stimuli in resp\n",
"spont = mt['stim'][0]['spont'][0] # timepoints by neurons\n",
"\n",
"### cell information\n",
"med = mt['med'] # cell centers (X Y Z)\n",
"mt['stat'][0] # first cell’s stats\n",
"mt['stat'][0]['npix'] # one example field, tells you how pixels make up the cell\n",
"\n",
"### loading images\n",
"mt2 = sio.loadmat('data/stringer/images_natimg2800_all.mat')\n",
"imgs = mt2['imgs'] # 68 by 270 by number of images\n",
"# check out first image using matplotlib.pyplot\n",
"plt.imshow(imgs[:,:,0], cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10103, 1)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mt['stat'].shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((5880, 10103), (5880, 1))"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resp.shape, istim.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"mt_small = sio.loadmat('data/stringer/natimg2800_small_M170717_MP034_2017-09-17.mat')\n",
"images_small = sio.loadmat('data/stringer/images_natimg2800_small_M170717_MP034_2017-09-17.mat')['imgs']\n",
"istim_small = mt_small['stim'][0]['istim'][0]\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f14f5593460>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(images_small[:, :, 0], cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(68, 270, 5880)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"images_in_order = images_small[:, :, (istim_small[:, 0] - 1) % 2800]\n",
"images_in_order.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"istim_small.min()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(68, 270, 10103)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matrices = np.dot(images_in_order[:, :, ::11], resp[::11, :])\n",
"matrices.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f14d77c8f70>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(matrices.sum(axis=2), cmap='gray')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.5 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}