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Updated and new notebooks
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‎CaltechDATA_Usage_Graphs.ipynb‎

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‎Downloads_over_time.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"id": "e5170d8d-efd6-4bda-8568-818ebb2abaab",
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"text": [
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" 0% (34 of 8729) | | Elapsed Time: 0:00:00 ETA: 0:00:25IOPub data rate exceeded.\n",
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"The Jupyter server will temporarily stop sending output\n",
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"to the client in order to avoid crashing it.\n",
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"To change this limit, set the config variable\n",
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"`--ServerApp.iopub_data_rate_limit`.\n",
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"\n",
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"Current values:\n",
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"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
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"ServerApp.rate_limit_window=3.0 (secs)\n",
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"\n",
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"100% (8729 of 8729) |####################| Elapsed Time: 0:02:05 Time: 0:02:05\n"
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"100% (3 of 3) |##########################| Elapsed Time: 0:00:11 Time: 0:00:11\n",
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"100% (2030 of 2030) |####################| Elapsed Time: 0:00:22 Time: 0:00:22\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import requests\n",
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"\n",
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"downloads = {}\n",
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"\n",
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"days = 1\n",
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"while days < 31:\n",
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" print(days)\n",
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" response = requests.get(f'https://stats.tind.io/index.php?date=2021-12-{days:02}&expanded=1&filter_limit=-1&format=JSON&idSite=1161&method=Actions.getDownloads&module=API&period=day&segment=&token_auth=TOKEN')\n",
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" for dl in response.json()[0]['subtable']:\n",
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" unique = dl['nb_uniq_visitors']\n",
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" downloads[label] = unique\n",
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" days = days + 1\n",
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" \n",
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"for d in downloads:\n",
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" print(d)\n",
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" print(downloads[d])"
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"#for d in downloads:\n",
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"# print(d)\n",
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"# print(downloads[d])"
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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": 33,
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"execution_count": 7,
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"id": "c92239e9-8618-4f8f-a4de-d796f89551b7",
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"metadata": {},
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"outputs": [
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"0.11409395973154363\n",
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"0.08571428571428572\n",
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"0.10626702997275204\n"
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]
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}

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