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July 8, 2019 20:21
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Delegates registrations and roll price
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| "data": { | |
| "text/html": [ | |
| " <script type=\"text/javascript\">\n", | |
| " window.PlotlyConfig = {MathJaxConfig: 'local'};\n", | |
| " if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n", | |
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| " 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n", | |
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| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from conseil import conseil\n", | |
| "import pandas as pd\n", | |
| "import cufflinks as cf\n", | |
| "import numpy as np\n", | |
| "import requests\n", | |
| "import dateutil" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
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| " require.undef(\"plotly\");\n", | |
| " requirejs.config({\n", | |
| " paths: {\n", | |
| " 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n", | |
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| " require(['plotly'], function(Plotly) {\n", | |
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| " " | |
| ] | |
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| "metadata": {}, | |
| "output_type": "display_data" | |
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| ], | |
| "source": [ | |
| "cf.set_config_file(offline=True, world_readable=True, theme='pearl')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "Operation = conseil.tezos.alphanet.operations\n", | |
| "query = Operation.query(Operation.timestamp) \\\n", | |
| " .filter(Operation.kind == Operation.kind.delegation,\n", | |
| " Operation.source.startswith('tz')) \\\n", | |
| " .order_by(Operation.timestamp)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.DataFrame(query.all())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.index = df['timestamp'].astype('M8[ms]')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df['total_registrations'] = 1\n", | |
| "df['total_registrations'] = df['total_registrations'].cumsum()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "market_data = requests.get('https://graphs2.coinmarketcap.com/currencies/tezos/').json()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "md = pd.DataFrame(market_data['price_btc'], columns=['ts', 'price_btc'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "md.index = md['ts'].astype('M8[ms]')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
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| "data": { | |
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| "1559177935000" | |
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| "execution_count": 10, | |
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| ], | |
| "source": [ | |
| "athens_ts = int(dateutil.parser.parse('2019-05-30T00:58:55Z').timestamp() * 1000)\n", | |
| "athens_ts" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "md['roll_price_btc'] = np.where(md['ts'] < athens_ts, 10000, 8000) * md['price_btc']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.merge_asof(df[['total_registrations']], md[['roll_price_btc']], left_index=True, right_index=True)" | |
| ] | |
| }, | |
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| " {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n", | |
| " ).then(function(){\n", | |
| " \n", | |
| "var gd = document.getElementById('e4a69ff5-9eab-42cb-8c11-d0424f05cc94');\n", | |
| "var x = new MutationObserver(function (mutations, observer) {{\n", | |
| " var display = window.getComputedStyle(gd).display;\n", | |
| " if (!display || display === 'none') {{\n", | |
| " console.log([gd, 'removed!']);\n", | |
| " Plotly.purge(gd);\n", | |
| " observer.disconnect();\n", | |
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| "// Listen for the removal of the full notebook cells\n", | |
| "var notebookContainer = gd.closest('#notebook-container');\n", | |
| "if (notebookContainer) {{\n", | |
| " x.observe(notebookContainer, {childList: true});\n", | |
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| "// Listen for the clearing of the current output cell\n", | |
| "var outputEl = gd.closest('.output');\n", | |
| "if (outputEl) {{\n", | |
| " x.observe(outputEl, {childList: true});\n", | |
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| "\n", | |
| " })\n", | |
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| " </script>\n", | |
| " </div>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df.iplot(secondary_y='roll_price_btc')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
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| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.0" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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