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358 lines
121 KiB
358 lines
121 KiB
2 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Topological analysis of a noisy circle\n",
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"_Test application for the topological tools_"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"**Imports**"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 13,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"from model import persistence, decoding\n",
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"import pandas as pd\n",
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"%matplotlib inline"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"**Parameters**"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 2,
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"outputs": [],
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"source": [
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"N = 20\n",
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"R = 1\n",
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"SIGMA = 0.1"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Generating the circle"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 3,
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"outputs": [
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{
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"data": {
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"text/plain": "(20, 2)"
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"phase = np.linspace(0, 2 * np.pi, N)\n",
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"points = np.array([np.cos(phase), np.sin(phase)]).swapaxes(0, 1) * R\n",
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"points.shape"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 4,
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"outputs": [],
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"source": [
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"noise = np.random.normal(scale=SIGMA, size=points.shape)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 5,
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"outputs": [
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{
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"data": {
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"text/plain": "<matplotlib.collections.PathCollection at 0x7fc83452b0a0>"
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},
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|
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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"image/png": "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},
|
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"metadata": {
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"needs_background": "light"
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},
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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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"points_no_noise = points\n",
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"points += noise"
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],
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"metadata": {
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"collapsed": false,
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|
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"pycharm": {
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|
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"name": "#%%\n"
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}
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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": 9,
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"outputs": [
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{
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"data": {
|
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"text/plain": "<matplotlib.collections.PathCollection at 0x7fc82d76da50>"
|
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},
|
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|
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"execution_count": 9,
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|
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"metadata": {},
|
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|
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"output_type": "execute_result"
|
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|
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},
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|
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{
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"data": {
|
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"text/plain": "<Figure size 640x480 with 1 Axes>",
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|
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.scatter(points[:, 0], points[:, 1])"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"persistence.persistence(points)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dataset = pd.DataFrame(points, columns=['x', 'y'])"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"persistence.persistence(points_no_noise)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 432x288 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Decoding... done\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "(20, 1)"
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"param = decoding.cohomological_parameterization(dataset)\n",
|
||
|
|
"param.shape"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "Text(0, 0.5, 'Cohomological parameter')"
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(np.roll(np.arctan2(points[:, 0], points[:, 1]), 5), np.roll(param, 5))\n",
|
||
|
|
"plt.xlabel('Phase')\n",
|
||
|
|
"plt.ylabel('Cohomological parameter')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"pycharm": {
|
||
|
|
"name": "#%%\n"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 2
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython2",
|
||
|
|
"version": "2.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|