2384 lines
96 KiB
Plaintext
2384 lines
96 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# ohlcv"
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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": 1,
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"metadata": {},
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||
"outputs": [],
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"source": [
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||
"import vectorbt as vbt"
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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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"metadata": {
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||
"Collapsed": "false"
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},
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||
"outputs": [],
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||
"source": [
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"import numpy as np\n",
|
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"import pandas as pd\n",
|
||
"from datetime import datetime, timedelta\n",
|
||
"from numba import njit"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
"Config({\n",
|
||
" \"open\": \"Open\",\n",
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" \"high\": \"High\",\n",
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||
" \"low\": \"Low\",\n",
|
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" \"close\": \"Close\",\n",
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" \"volume\": \"Volume\"\n",
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"})\n"
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||
]
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||
}
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],
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"source": [
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"print(vbt.settings.ohlcv['column_names'])"
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
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||
"metadata": {},
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||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
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"output_type": "stream",
|
||
"text": [
|
||
"Can only use .str accessor with string values!\n"
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||
]
|
||
}
|
||
],
|
||
"source": [
|
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"try:\n",
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" pd.DataFrame([1, 2, 3]).vbt.ohlcv.plot()\n",
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"except Exception as e:\n",
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" print(e) # couldn't find default column names"
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
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||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"ohlcv_ts = pd.DataFrame({\n",
|
||
" 'open': [1, 2, 3], \n",
|
||
" 'high': [1, 2, 3], \n",
|
||
" 'low': [1, 2, 3], \n",
|
||
" 'close': [1, 2, 3], \n",
|
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" 'volume': [1, 2, 3]\n",
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"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
" open high low close volume\n",
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"2018-01-01 -0.385236 -0.339812 -0.941729 -1.058755 -0.881574\n",
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"2018-01-02 -0.243403 0.210847 -0.408890 0.158420 -1.149656\n",
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"2018-01-03 0.226744 1.207481 0.198341 1.168544 -2.071525\n",
|
||
"2018-01-04 0.407461 1.294365 -0.223333 0.946101 -2.218826\n",
|
||
"2018-01-05 1.221150 1.988841 0.810106 0.834179 -2.940705\n",
|
||
"2018-01-06 2.022596 2.046477 1.293526 2.013281 -2.689034\n",
|
||
"2018-01-07 2.724580 3.303932 1.976807 3.468771 -1.720546\n",
|
||
"2018-01-08 2.057622 2.410773 1.251777 2.269435 -0.980795\n",
|
||
"2018-01-09 1.607800 1.720066 1.169075 2.456895 -1.621470\n",
|
||
"2018-01-10 2.069398 2.516643 1.838688 1.776437 -1.282516\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"ohlcv_ts = pd.DataFrame(\n",
|
||
" columns=['open', 'high', 'low', 'close', 'volume'],\n",
|
||
" index=[datetime(2018, 1, 1) + timedelta(days=i) for i in range(10)]\n",
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||
")\n",
|
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"ohlcv_ts['open'] = np.cumsum(np.random.uniform(-0.8, 1, size=(10,)))\n",
|
||
"ohlcv_ts['close'] = ohlcv_ts['open'] + np.random.uniform(-1, 1, size=(10,))\n",
|
||
"ohlcv_ts['high'] = ohlcv_ts['open'] + np.random.uniform(0, 1, size=(10,))\n",
|
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"ohlcv_ts['low'] = ohlcv_ts['open'] - np.random.uniform(0, 1, size=(10,))\n",
|
||
"ohlcv_ts['volume'] = np.cumsum(np.random.uniform(-1, 1, size=(10,)))\n",
|
||
"\n",
|
||
"print(ohlcv_ts)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"try:\n",
|
||
" ohlcv_ts.vbt.ohlcv.plot()\n",
|
||
"except Exception as e:\n",
|
||
" print(e) # still couldn't find default column names"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Specify them manually\n",
|
||
"ohlcv_ts.vbt.ohlcv(column_names={\n",
|
||
" 'open': 'open', \n",
|
||
" 'high': 'high', \n",
|
||
" 'low': 'low', \n",
|
||
" 'close': 'close', \n",
|
||
" 'volume': 'volume'\n",
|
||
"}).plot().show_svg()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Or by changing the defaults\n",
|
||
"vbt.settings.ohlcv['column_names'] = {\n",
|
||
" 'open': 'open', \n",
|
||
" 'high': 'high', \n",
|
||
" 'low': 'low', \n",
|
||
" 'close': 'close', \n",
|
||
" 'volume': 'volume'\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
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opacity: 1; stroke-width: 0px; fill: rgb(27, 158, 118); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M448,0V24.81H512V0Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(27, 158, 118); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M512,0V41.02H576V0Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(27, 158, 118); fill-opacity: 1;\"/></g><g class=\"point\"><path d=\"M576,0V32.44H640V0Z\" style=\"vector-effect: non-scaling-stroke; opacity: 1; stroke-width: 0px; fill: rgb(217, 95, 2); fill-opacity: 1;\"/></g></g></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"x2tick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" transform=\"translate(62,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"320\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"320\">2018</tspan></text></g><g class=\"x2tick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(190,0)\">Jan 3</text></g><g class=\"x2tick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(318,0)\">Jan 5</text></g><g class=\"x2tick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(446,0)\">Jan 7</text></g><g class=\"x2tick\"><text text-anchor=\"middle\" x=\"0\" y=\"320\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(574,0)\">Jan 9</text></g></g><g class=\"yaxislayer-above\"><g class=\"y2tick\"><text text-anchor=\"end\" x=\"29\" y=\"4.199999999999999\" transform=\"translate(0,304.58)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">−3</text></g><g class=\"y2tick\"><text text-anchor=\"end\" x=\"29\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,279.28999999999996)\">−2</text></g><g class=\"y2tick\"><text text-anchor=\"end\" x=\"29\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,253.98999999999998)\">−1</text></g><g class=\"y2tick\"><text text-anchor=\"end\" x=\"29\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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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": [
|
||
"ohlcv_ts.vbt.ohlcv.plot().show_svg()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
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"outputs": [
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{
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||
"data": {
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"text/plain": [
|
||
"Start 2018-01-01 00:00:00\n",
|
||
"End 2018-01-10 00:00:00\n",
|
||
"Period 10 days 00:00:00\n",
|
||
"First Price -0.385236\n",
|
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"Lowest Price -1.058755\n",
|
||
"Highest Price 3.468771\n",
|
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"Last Price 1.776437\n",
|
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"First Volume -0.881574\n",
|
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"Lowest Volume -2.940705\n",
|
||
"Highest Volume -0.881574\n",
|
||
"Last Volume -1.282516\n",
|
||
"Name: agg_func_mean, dtype: object"
|
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]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
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"output_type": "execute_result"
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}
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],
|
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"source": [
|
||
"ohlcv_ts.vbt.ohlcv.stats()"
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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": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
||
"7.62 ms ± 11.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"big_ohlcv_ts = pd.DataFrame(np.random.uniform(1, 2, (10000, 5)), columns=ohlcv_ts.columns)\n",
|
||
"\n",
|
||
"%timeit big_ohlcv_ts.vbt.ohlcv.stats()"
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||
]
|
||
},
|
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{
|
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"linecolor": "white",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "white",
|
||
"zerolinewidth": 2
|
||
},
|
||
"yaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "white",
|
||
"zerolinewidth": 2
|
||
}
|
||
}
|
||
},
|
||
"width": 700,
|
||
"xaxis": {
|
||
"rangeslider": {
|
||
"visible": false
|
||
},
|
||
"showgrid": true
|
||
},
|
||
"yaxis": {
|
||
"domain": [
|
||
0,
|
||
0.33
|
||
],
|
||
"showgrid": true
|
||
},
|
||
"yaxis2": {
|
||
"domain": [
|
||
0.33,
|
||
1
|
||
]
|
||
}
|
||
},
|
||
"_py2js_animate": {},
|
||
"_py2js_deleteTraces": {},
|
||
"_py2js_moveTraces": {},
|
||
"_py2js_removeLayoutProps": {},
|
||
"_py2js_removeTraceProps": {},
|
||
"_py2js_restyle": {},
|
||
"_view_count": 0
|
||
}
|
||
}
|
||
},
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|