CaltechDATA_Usage_Graphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show usage of CaltechDATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (3 of 3) |##########################| Elapsed Time: 0:01:09 Time:  0:01:09\n",
      "100% (2334 of 2334) |####################| Elapsed Time: 0:20:11 Time:  0:20:11\n"
     ]
    }
   ],
   "source": [
    "#Get metadata from CaltechDATA\n",
    "\n",
    "import os\n",
    "from ames.harvesters import get_caltechdata\n",
    "\n",
    "if os.path.isdir('data') == False:\n",
    "    os.mkdir('data')\n",
    "os.chdir('data')\n",
    "\n",
    "production = True\n",
    "collection = 'caltechdata.ds'\n",
    "\n",
    "get_caltechdata(collection,production)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Collect submissions over time\n",
    "#%matplotlib inline\n",
    "from ames.harvesters import get_records\n",
    "from py_dataset import dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotnine as p9\n",
    "import warnings\n",
    "#Plotnine has warning - ignore\n",
    "#These will hopefully be fixed in future (v0.6?) plotnine release \n",
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "dot_paths = ['._Key','.dates','.resourceType','.subjects','.publicationYear','.contributors']\n",
    "keys = dataset.keys(collection)\n",
    "all_metadata = get_records(dot_paths,'dois',collection,keys)\n",
    "dates = []\n",
    "categories = []\n",
    "keys = []\n",
    "for record in all_metadata:\n",
    "    #Submitted date type takes precedence, then Available date (old geo theses), then Issued\n",
    "    rdate = None\n",
    "    available = None\n",
    "    issued = None\n",
    "    if 'dates' in record:\n",
    "        for date in record['dates']:\n",
    "            if date['dateType']=='Submitted':\n",
    "                rdate = date['date']\n",
    "            if date['dateType']=='Available':\n",
    "                available = '2018-02-12' #Hard coding geology theses\n",
    "            if date['dateType']=='Issued':\n",
    "                issued = date['date']\n",
    "        if rdate == None:\n",
    "            if available != None:\n",
    "                rdate = available\n",
    "            else:\n",
    "                rdate = issued\n",
    "    else:\n",
    "        #Junk records\n",
    "        rdate = '1900-01-01'\n",
    "    dates.append(rdate)\n",
    "    #We categorize records based on submission type\n",
    "    category = 'Deposit Form'\n",
    "    thesis = False\n",
    "    if 'subjects' in record:\n",
    "        for s in record['subjects']:\n",
    "            if 'TCCON' in s['subject']:\n",
    "                category = 'API'\n",
    "            if 'Bitbucket' in s['subject']:\n",
    "                category = 'API'\n",
    "            if 'Atlas of Bacterial and Archaeal Cell Structure' in s['subject']:\n",
    "                category = 'API'\n",
    "            if 'Github' in s['subject']:\n",
    "                category = 'GitHub'\n",
    "            if 'GitHub' in s['subject']:\n",
    "                category = 'GitHub'\n",
    "            if 'thesis' in s['subject']:\n",
    "                thesis=True\n",
    "    #Check if Tony handled the thesis record\n",
    "    if thesis == True:\n",
    "        if 'contributors' in record:\n",
    "            for c in record['contributors']:\n",
    "                if c['name'] == 'Diaz, Tony':\n",
    "                    category = 'API'\n",
    "    categories.append(category)\n",
    "    keys.append(record['_Key'])\n",
    "data = {'dates':dates,'categories':categories,'keys':keys}\n",
    "df = pd.DataFrame(data=data)\n",
    "pd.options.display.max_rows = 2000\n",
    "df = df.astype({'dates':'datetime64'})\n",
    "#display(df.sort_values('dates'))\n",
    "#Group by year, month, and category\n",
    "df = df.groupby([df[\"dates\"].dt.year, df[\"dates\"].dt.month,df['categories']])['dates'].count()\n",
    "#Fill in categories with 0 if no values that month\n",
    "df = df.reindex(pd.MultiIndex.from_product([df.index.levels[0],df.index.levels[1],['API','Deposit Form','GitHub']]),fill_value=0)\n",
    "#Calculate cumulative sum of categories\n",
    "df = df.groupby(level=2).cumsum()\n",
    "#Pull out categories\n",
    "df = df.reset_index(level=2)\n",
    "#display(df)\n",
    "#Cut out future dates with no changes\n",
    "df = df.loc[(2017,2):(2023,1)]\n",
    "df.index = df.index.to_numpy()\n",
    "labels = list(df.index)[0::10]\n",
    "\n",
    "theme = p9.theme(axis_text_x = p9.element_text(color=\"black\", size=12,\n",
    "                                                         angle=90),\n",
    "                           axis_text_y = p9.element_text(color=\"black\", size=12))\n",
    "\n",
    "plot = p9.ggplot(df, p9.aes(x='dates.index',y='dates',group='level_2',fill='level_2')) \n",
    "plot = plot + p9.geom_area() + theme \n",
    "plot = plot + p9.labs(x= 'Month',y= 'Record Count',fill='Deposit Method',title=\"Deposits to CaltechDATA\")\n",
    "plot = plot + p9.scale_x_discrete(breaks=labels)\n",
    "plot = plot.draw()\n",
    "plot.savefig('data_plot.png',bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Look at quality of user-submitted records\n",
    "%matplotlib inline\n",
    "from ames.harvesters import get_records\n",
    "from py_dataset import dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "dot_paths = ['._Key','.dates','.resourceType','.subjects','.publicationYear','.contributors','.descriptions','.electronic_location_and_access']\n",
    "keys = dataset.keys(collection)\n",
    "all_metadata = get_records(dot_paths,'dois',collection,keys)\n",
    "deposit_records = 0\n",
    "text_files = 0\n",
    "description_length = 0\n",
    "text_lengths = []\n",
    "file_sizes = []\n",
    "record_sizes = []\n",
    "for record in all_metadata:\n",
    "    #We categorize records based on submission type\n",
    "    category = 'Deposit Form'\n",
    "    thesis = False\n",
    "    if 'subjects' in record:\n",
    "        for s in record['subjects']:\n",
    "            if 'TCCON' in s['subject']:\n",
    "                category = 'API'\n",
    "            if 'Bitbucket' in s['subject']:\n",
    "                category = 'API'\n",
    "            if 'Atlas of Bacterial and Archaeal Cell Structure' in s['subject']:\n",
    "                category = 'API'\n",
    "            if 'Github' in s['subject']:\n",
    "                category = 'GitHub'\n",
    "            if 'GitHub' in s['subject']:\n",
    "                category = 'GitHub'\n",
    "            if 'thesis' in s['subject']:\n",
    "                thesis=True\n",
    "    #Check if Tony handled the thesis record\n",
    "    if thesis == True:\n",
    "        if 'contributors' in record:\n",
    "            for c in record['contributors']:\n",
    "                if c['contributorName'] == 'Diaz, Tony':\n",
    "                    category = 'API'\n",
    "    if category == 'Deposit Form':\n",
    "        deposit_records += 1\n",
    "        record_text_length = 0\n",
    "        t_file = False\n",
    "        for description in record['descriptions']:\n",
    "            #Filter out automated descriptions\n",
    "            text = description['description']\n",
    "            if 'Download Citation' not in text:\n",
    "                if 'Unique Views' not in text:\n",
    "                    description_length += len(text.split())\n",
    "                    record_text_length += len(text.split())\n",
    "        if 'electronic_location_and_access' in record:\n",
    "            record_size = 0\n",
    "            for record in record['electronic_location_and_access']:\n",
    "                name = record['electronic_name'][0]\n",
    "                if 'txt' in name:\n",
    "                    t_file = True\n",
    "                if 'pdf' in name:\n",
    "                    t_file = True\n",
    "                size = int(record['file_size'])\n",
    "                file_sizes.append(size)\n",
    "                record_size += size\n",
    "        record_sizes.append(record_size)\n",
    "        if t_file:\n",
    "            text_files += 1\n",
    "        text_lengths.append(record_text_length)\n",
    "print(f'Deposit records: {deposit_records}')\n",
    "print(f'Average description length: {description_length/deposit_records}')\n",
    "print(f'Records with text or pdf: {text_files}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tdict = {}\n",
    "text = ''\n",
    "for record in all_metadata:\n",
    "    if record['resourceType']['resourceTypeGeneral']=='Dataset':\n",
    "        keep=True\n",
    "        for s in record['subjects']:\n",
    "            if 'TCCON' in s['subject']:\n",
    "                keep=False\n",
    "        if keep==True:\n",
    "            for s in record['subjects']:\n",
    "                subject = s['subject']\n",
    "                text = text + ' ' + subject\n",
    "                if subject in tdict:\n",
    "                    tdict[subject] +=1\n",
    "                else:\n",
    "                    tdict[subject] = 1\n",
    "\n",
    "#print(sorted(tdict.items(), key=lambda kv: kv[1]))\n",
    "wordcloud = WordCloud(min_font_size=10, max_words=100, background_color=\"white\").generate(text)\n",
    "\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={'text_length':text_lengths})\n",
    "print(df.describe())\n",
    "theme = p9.theme(axis_text_x = p9.element_text(color=\"black\", size=12,\n",
    "                                                         angle=90),\n",
    "                           axis_text_y = p9.element_text(color=\"black\", size=12))\n",
    "\n",
    "p9.ggplot(df, p9.aes(x='text_length')) + p9.geom_histogram() + theme + p9.labs(x='Number of Words',y= 'Record Count',title=\"Description Length\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "dot_paths = ['._Key','.publicationYear','.electronic_location_and_access']\n",
    "keys = dataset.keys(collection)\n",
    "all_metadata = get_records(dot_paths,'dois',collection,keys)\n",
    "\n",
    "file_sizes = []\n",
    "record_sizes = []\n",
    "total_storage = 0\n",
    "storage_2018 = 0\n",
    "storage_2019 = 0\n",
    "storage_2020 = 0\n",
    "storage_2021 = 0\n",
    "large_records = []\n",
    "\n",
    "for record in all_metadata:\n",
    "        if 'electronic_location_and_access' in record:\n",
    "            pubyear = record['publicationYear']\n",
    "            record_size = 0\n",
    "            for frecord in record['electronic_location_and_access']:\n",
    "                name = frecord['electronic_name'][0]\n",
    "                size = int(frecord['file_size'])\n",
    "                file_sizes.append(size)\n",
    "                record_size += size\n",
    "                total_storage += size\n",
    "            record_sizes.append(record_size)\n",
    "            if record_size > 25*(10**9):\n",
    "                large_records.append(record['_Key'])\n",
    "            if pubyear == '2018':\n",
    "                storage_2018 += record_size\n",
    "            if pubyear == '2019':\n",
    "                storage_2019 += record_size\n",
    "            if pubyear == '2020':\n",
    "                storage_2020 += record_size\n",
    "            if pubyear == '2021':\n",
    "                storage_2021 += record_size\n",
    "\n",
    "print(len(large_records))\n",
    "print(len(all_metadata))\n",
    "with open('large_records.json','w') as f:\n",
    "    json.dump(large_records,f)\n",
    "    \n",
    "print(f'Total Storage (TB): {total_storage/(10**12)}')\n",
    "print(f'Average storage per record (GB): {total_storage/len(all_metadata)//(10**9)}')\n",
    "print(f'2018 Storage (TB): {storage_2018/(10**12)}')\n",
    "print(f'2019 Storage (TB): {storage_2019/(10**12)}')\n",
    "print(f'2020 Storage (TB): {storage_2020/(10**12)}')\n",
    "print(f'2021 Storage (TB): {storage_2021/(10**12)}')\n",
    "print(f'2019 Growth: {storage_2019/storage_2018}')\n",
    "print(f'2020 Growth: {storage_2020/(storage_2018+storage_2019)}')\n",
    "print(f'2021 Growth: {storage_2021/(storage_2018+storage_2019+storage_2020)}')\n",
    "\n",
    "df2 = pd.DataFrame(data={'file_size':file_sizes})\n",
    "df2 = df2['file_size']/1000000000 #Convert to GB\n",
    "print(df2.describe())\n",
    "df3 = pd.DataFrame(data={'record_size':record_sizes})\n",
    "#df3 = df3['record_size']/1000000000 #Convert to GB\n",
    "print(df3.describe())\n",
    "#p9.ggplot(df3, p9.aes(x='record_size'))  + p9.geom_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine numbers of unique users who have submitted records and what percentage of Caltech campus has used CaltechDATA\n",
    "dot_paths = ['._Key','.dates','.owners']\n",
    "keys = dataset.keys(collection)\n",
    "metadata = get_records(dot_paths,'dois',collection,keys)\n",
    "dates = []\n",
    "owners = []\n",
    "keys = []\n",
    "for record in metadata:\n",
    "    #Submitted date type takes precedence, then Updated date (old geo theses), then Issued\n",
    "    rdate = None\n",
    "    submitted = None\n",
    "    issued = None\n",
    "    if 'dates' in record:\n",
    "        for date in record['dates']:\n",
    "            if date['dateType']=='Submitted':\n",
    "                rdate = date['date']\n",
    "            if date['dateType']=='Updated':\n",
    "                submitted = date['date']\n",
    "            if date['dateType']=='Issued':\n",
    "                issued = date['date']\n",
    "    if rdate == None:\n",
    "        if submitted != None:\n",
    "            rdate = submitted\n",
    "        else:\n",
    "            rdate = issued\n",
    "    dates.append(rdate)\n",
    "    keys.append(record['_Key'])\n",
    "    if 'owners' in record:\n",
    "        owners.append(record['owners'][0])\n",
    "    else:\n",
    "        owners.append(0)\n",
    "        #print(record['_Key'])\n",
    "data = {'dates':dates,'owners':owners,'keys':keys}    \n",
    "df = pd.DataFrame(data=data)\n",
    "pd.options.display.max_rows = 2000\n",
    "df = df.astype({'dates':'datetime64'})\n",
    "#df = df.query('20180801 < dates < 20190701')\n",
    "#display(df.sort_values('dates'))\n",
    "print('Unique users: ',df['owners'].nunique())\n",
    "#Caltech FTE from https://www.scelc.org/institutions/california-institute-technology\n",
    "print('Percentage of campus: ',100*df['owners'].nunique()/4200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine number of authors per record\n",
    "dot_paths = ['._Key','.relatedIdentifiers','.creators','.subjects']\n",
    "keys = dataset.keys(collection)\n",
    "metadata = get_records(dot_paths,'dois',collection,keys)\n",
    "num_records = len(metadata)\n",
    "total_authors = 0\n",
    "has_paper = 0\n",
    "automated_software = 0\n",
    "tccon = 0\n",
    "thesis = 0\n",
    "cell_atlas = 0\n",
    "for record in metadata:\n",
    "    total_authors += len(record['creators'])\n",
    "    if 'relatedIdentifiers' in record:\n",
    "        rel = False\n",
    "        for identifier in record['relatedIdentifiers']:\n",
    "            if identifier['relationType'] == 'IsSupplementTo':\n",
    "                rel = True \n",
    "        if rel:\n",
    "            has_paper += 1\n",
    "    if 'subjects' in record:\n",
    "        for s in record['subjects']:\n",
    "            if 'TCCON' in s['subject']:\n",
    "                tccon += 1\n",
    "            if 'Bitbucket' in s['subject']:\n",
    "                automated_software +=1\n",
    "            if 'Atlas of Bacterial and Archaeal Cell Structure' in s['subject']:\n",
    "                cell_atlas += 1\n",
    "            if 'Github' in s['subject']:\n",
    "                automated_software +=1\n",
    "            if 'GitHub' in s['subject']:\n",
    "                automated_software +=1\n",
    "            if 'thesis' in s['subject']:\n",
    "                thesis+=1\n",
    "print(f'Large record categories {thesis} Theses Records {automated_software} Software Integrations {tccon} TCCON {cell_atlas} Cell Atlas')\n",
    "print(f'Total number of authors (incl duplicates): {total_authors}')\n",
    "print(f'Number of records with publication in metadata: {has_paper}')\n",
    "print(f'Average authors per record: {total_authors/num_records}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}