blog_num_journals.ipynb

Unpaywall Journals Exploration.ipynb

Unpaywall Journals QA.ipynb

blog_num_journals.ipynb

{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-10T00:52:50.337382Z",
          "end_time": "2020-02-10T00:53:16.043143Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import requests\nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport os\nimport random\n\n# r = requests.get(\"https://unpaywall-jump-api.herokuapp.com/scenario/demo-123/details?secret=\" + os.getenv(\"JWT_SECRET_KEY\"))\nr = requests.get(\"http://localhost:5004/scenario/demo-123/details?secret=\" + os.getenv(\"JWT_SECRET_KEY\"))\ndetails_dict = r.json()",
      "execution_count": 149,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-10T00:53:16.048153Z",
          "end_time": "2020-02-10T00:53:16.053344Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "details_dict.keys()\nr\nmy_journals = details_dict[\"journals\"]\n# my_journals[0]",
      "execution_count": 150,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-10T00:53:16.058089Z",
          "end_time": "2020-02-10T00:53:16.135694Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sorted_by_slope = sorted(my_journals, key=lambda j: j[\"debug\"][\"num_papers_slope\"])\nsorted_by_slope = [j for j in sorted_by_slope if j[\"debug\"][\"num_papers_slope\"] != None]\n\nprint \"num positive\", len([j for j in sorted_by_slope if j[\"debug\"][\"num_papers_slope\"] > 0])\nprint \"num negative\", len([j for j in sorted_by_slope if j[\"debug\"][\"num_papers_slope\"] < 0])\nprint \"num flat\", len([j for j in sorted_by_slope if j[\"debug\"][\"num_papers_slope\"] == 0])\n\nprint \"**volume shrinking\"\nprint u\"\\n\".join([u\"{} ({})\".format(j[\"top\"][\"title\"], j[\"top\"][\"issn_l\"])  for j in sorted_by_slope[0:20]])\nprint\nprint \"**volume growing\"\nprint u\"\\n\".join([u\"{} ({})\".format(j[\"top\"][\"title\"], j[\"top\"][\"issn_l\"]) for j in sorted_by_slope[-20:-1]])\n# , 2) for j in my_journals])[0:10]\n# print sorted([round(j[\"debug\"][\"num_papers_slope\"], 2) for j in my_journals], reverse=True)[0:10]",
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "num positive 993\nnum negative 647\nnum flat 196\n**volume shrinking\nLe Pharmacien Hospitalier et Clinicien (2211-1042)\nSeminars in Vascular Surgery (0895-7967)\nPsychologie Française (0033-2984)\nRevue du Rhumatisme (1169-8330)\nMaturitas (0378-5122)\nSeminars in Spine Surgery (1040-7383)\nTransfusion Medicine Reviews (0887-7963)\nLeukemia Research (0145-2126)\nRussian Literature (0304-3479)\nEndeavour (0160-9327)\nMathematical Social Sciences (0165-4896)\nRenewable and Sustainable Energy Reviews (1364-0321)\nInternational Journal of Intercultural Relations (0147-1767)\nFungal Genetics and Biology (1087-1845)\nResearch in Economics (1090-9443)\nComputer Vision and Image Understanding (1077-3142)\nJournal of Statistical Planning and Inference (0378-3758)\nMolecular and Cellular Endocrinology (0303-7207)\nThe American Journal of the Medical Sciences (0002-9629)\nJournal of Diabetes and its Complications (1056-8727)\n\n**volume growing\nRevista Española de Anestesiología y Reanimación (English Edition) (2341-1929)\nFood Bioscience (2212-4292)\nEcological Genetics and Genomics (2405-9854)\nGenomics (0888-7543)\nEuropean Journal of Vascular and Endovascular Surgery (1078-5884)\nThe North American Journal of Economics and Finance (1062-9408)\nEcotoxicology and Environmental Safety (0147-6513)\nJournal of Petroleum Science and Engineering (0920-4105)\nClinical Epidemiology and Global Health (2213-3984)\nSwarm and Evolutionary Computation (2210-6502)\nFood Packaging and Shelf Life (2214-2894)\nThe Science of The Total Environment (0048-9697)\nJournal of Manufacturing Processes (1526-6125)\nPhysical Communication (1874-4907)\nGene Reports (2452-0144)\nMultiple Sclerosis and Related Disorders (2211-0348)\nMicrochemical Journal (0026-265X)\nJournal of Energy Storage (2352-152X)\nStructural Change and Economic Dynamics (0954-349X)\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-10T01:19:15.502230Z",
          "end_time": "2020-02-10T01:19:16.964745Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# %matplotlib nbagg\n%matplotlib inline\n\nfrom textwrap import wrap\n\nfig, axes = plt.subplots(4, 5, figsize=(12, 9), sharex=True, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\ni = 0\nmy_journals = details_dict[\"journals\"]\nmy_journals = [j for j in my_journals if not j[\"debug\"][\"use_default_num_papers_curve\"]]\nrandom.shuffle(my_journals)\n# my_journals = sorted(my_journals, key=lambda j: j[\"debug\"][\"num_papers_slope\"], reverse=True)\nmy_journals = my_journals[0:20]\nfor j in my_journals:\n    ax = axes_flatten[i]\n    column_label = round(j[\"debug\"][\"num_papers_slope\"], 2)\n#     ax.text(.3, .2, column_label,\n#         horizontalalignment='right',\n#         verticalalignment='bottom',\n#         fontsize=12,\n#         transform=ax.transAxes)\n    ax.set_ylim(0, 50+max(j[\"debug\"][\"curve_fit_for_num_papers\"][\"input_y\"] + j[\"debug\"][\"num_papers_by_year\"]))\n    ax.plot(j[\"debug\"][\"curve_fit_for_num_papers\"][\"y_fit\"] + j[\"debug\"][\"num_papers_by_year\"], '.', color=\"grey\")\n    ax.plot(j[\"debug\"][\"curve_fit_for_num_papers\"][\"y_fit\"] + j[\"debug\"][\"num_papers_by_year\"], color=\"grey\")\n    ax.plot(j[\"debug\"][\"curve_fit_for_num_papers\"][\"input_y\"], 'x', color=\"black\")\n    ax.set_xlabel(u\"\\n\".join(wrap(u\"{}\\n({})\".format(j[\"top\"][\"title\"], column_label), 25)))\n#     ax.text(0, 0, u\"{}\\n{}\".format(j[\"top\"][\"title\"], column_label), ha='left', va=\"bottom\", wrap=True)\n    ax.set_xticks([]) \n\n    i += 1",
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x648 with 20 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-10T00:49:31.463220Z",
          "end_time": "2020-02-10T00:49:31.503958Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "my_journals = details_dict[\"journals\"]\nj = [j for j in my_journals if j[\"top\"][\"title\"].startswith(\"Hand Surgery\")][0]\nj\n# 2468-1210",
      "execution_count": 140,
      "outputs": [
        {
          "execution_count": 140,
          "output_type": "execute_result",
          "data": {
            "text/plain": "{u'apc': {u'annual_projected_cost': 0,\n  u'annual_projected_fractional_authorship': 0,\n  u'annual_projected_num_papers': 0,\n  u'apc_price': u'unknown'},\n u'cost': {u'data': [{u'cost_avg': u'$29',\n    u'cost_per_use': u'$0.80',\n    u'cost_type': u'*Your scenario cost*',\n    u'year_2020': u'$20',\n    u'year_2021': u'$29',\n    u'year_2022': u'$30',\n    u'year_2023': u'$31',\n    u'year_2024': u'$34'},\n   {u'cost_avg': u'$875',\n    u'cost_per_use': u'$24.31',\n    u'cost_type': u'Subscription By Year',\n    u'year_2020': u'$746',\n    u'year_2021': u'$806',\n    u'year_2022': u'$870',\n    u'year_2023': u'$940',\n    u'year_2024': u'$1,015'},\n   {u'cost_avg': u'$29',\n    u'cost_per_use': u'$0.80',\n    u'cost_type': u'ILL By Year',\n    u'year_2020': u'$20',\n    u'year_2021': u'$29',\n    u'year_2022': u'$30',\n    u'year_2023': u'$31',\n    u'year_2024': u'$34'},\n   {u'cost_avg': u'$847',\n    u'cost_per_use': u'$23.51',\n    u'cost_type': u'Subscription Minus ILL By Year',\n    u'year_2020': u'$726',\n    u'year_2021': u'$777',\n    u'year_2022': u'$840',\n    u'year_2023': u'$909',\n    u'year_2024': u'$981'}],\n  u'headers': [{u'text': u'Cost Type', u'value': u'cost_type'},\n   {u'text': u'Cost (projected annual)', u'value': u'cost_avg'},\n   {u'text': u'Cost-Type per paid use', u'value': u'cost_per_use'},\n   {u'text': u'Cost projected 2020', u'value': u'year_2020'},\n   {u'text': u'2021', u'value': u'year_2021'},\n   {u'text': u'2022', u'value': u'year_2022'},\n   {u'text': u'2023', u'value': u'year_2023'},\n   {u'text': u'2024', u'value': u'year_2024'}],\n  u'ncppu': u'$23.51',\n  u'subscribed': False},\n u'debug': {u'backfile_obs_pub_matrix': [[2,\n    2,\n    2,\n    2,\n    2,\n    1,\n    2,\n    3,\n    6,\n    9,\n    0,\n    0,\n    0,\n    0],\n   [0, 2, 2, 2, 2, 2, 1, 2, 3, 2, 0, 0, 0, 0],\n   [0, 0, 2, 2, 2, 2, 2, 1, 2, 1, 0, 0, 0, 0],\n   [0, 0, 0, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0],\n   [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]],\n  u'curve_fit_for_downloads': {u'input_y': [138, 105, 63, 10, 0],\n   u'params': [445.49369464621293, -80.37174028836141, -2.5020713390158154],\n   u'r_squared': 0.9799967897430987,\n   u'y_fit': [142.37510703474504,\n    98.44582131365082,\n    57.805793716765194,\n    22.827759483294784,\n    -5.454481549667747]},\n  u'curve_fit_for_num_papers': {u'input_y': [324, 326, 320, 288],\n   u'params': [342.9999974568206, -11.39999898272937],\n   u'r_squared': 0.6804187769194942,\n   u'y_extrap': [286.00000254317376,\n    274.6000035604444,\n    263.20000457771505,\n    251.80000559498563,\n    240.40000661225628],\n   u'y_fit': [331.59999847409125,\n    320.1999994913619,\n    308.8000005086325,\n    297.4000015259031]},\n  u'display_perpetual_access_years': u'<2010-2019',\n  u'downloads_backfile_by_year': [31, 18, 14, 11, 5],\n  u'downloads_by_age': [17.078374,\n   6.3359939999999995,\n   4.411216,\n   3.35271,\n   2.7070540000000003],\n  u'downloads_counter_multiplier': 0.14556962025316456,\n  u'downloads_counter_multiplier_normalized': 0.429,\n  u'downloads_oa_bronze_by_age': [0.0, 0.0, 0.0, 0.0, 0.0],\n  u'downloads_oa_bronze_by_year': [0, 0, 0, 0, 0],\n  u'downloads_oa_bronze_older': 0.0,\n  u'downloads_oa_by_age': [0.07693710081825096,\n   0.054807552661596946,\n   0.036574515625855515,\n   0.026594766547528514,\n   0.0],\n  u'downloads_oa_by_year': [0, 0, 0, 0, 0],\n  u'downloads_oa_green_by_age': [0.06493678326996198,\n   0.024091231939163495,\n   0.033545368821292776,\n   0.025495893536121673,\n   0.0],\n  u'downloads_oa_green_by_year': [0, 0, 0, 0, 0],\n  u'downloads_oa_green_older': 0.0,\n  u'downloads_oa_hybrid_by_age': [0.0, 0.024091231939163495, 0.0, 0.0, 0.0],\n  u'downloads_oa_hybrid_by_year': [0, 0, 0, 0, 0],\n  u'downloads_oa_hybrid_older': 0.0,\n  u'downloads_oa_peer_reviewed_by_year': [0, 0, 0, 0, 0],\n  u'downloads_obs_pub_matrix': [[3, 3, 3, 3, 3, 2, 3, 4, 7, 20, 0, 0, 0, 0],\n   [0, 3, 3, 3, 3, 3, 2, 3, 4, 6, 19, 0, 0, 0],\n   [0, 0, 3, 3, 3, 3, 3, 2, 3, 4, 6, 18, 0, 0],\n   [0, 0, 0, 3, 3, 3, 3, 3, 2, 3, 4, 6, 17, 0],\n   [0, 0, 0, 0, 2, 2, 2, 2, 2, 1, 2, 3, 5, 16]],\n  u'downloads_total_by_year': [54.500800000000005,\n   52.324999999999996,\n   50.153800000000004,\n   47.9826,\n   45.8114],\n  u'downloads_total_older_than_five_years': 16.269351999999998,\n  u'ncppu': 23.509489,\n  u'ncppu_rank': 946.0,\n  u'num_bronze_by_year': [0, 0, 0, 0, 0],\n  u'num_green_by_year': [1.0, 1.0, 2.0, 2.0, 0],\n  u'num_hybrid_by_year': [0, 1.0, 0, 0, 0],\n  u'num_hybrid_historical_by_year': [0, 0, 0, 1.0, 0],\n  u'num_oa_by_year': [1.1848, 2.275, 2.1806, 2.0862, 0.0],\n  u'num_oa_historical_by_year': [0, 2.0, 2.0, 2.0, 1.0],\n  u'num_papers': 263.0,\n  u'num_papers_by_year': [286.00000254317376,\n   274.6000035604444,\n   263.20000457771505,\n   251.80000559498563,\n   240.40000661225628],\n  u'num_papers_growth_from_2018_by_year': [1.1848,\n   1.1375,\n   1.0903,\n   1.0431,\n   0.9959],\n  u'num_papers_slope': -0.03,\n  u'oa_data': {u'green': {u'2016.0': 2.0,\n    u'2017.0': 2.0,\n    u'2018.0': 1.0,\n    u'2019': 1.0},\n   u'hybrid': {u'2018.0': 1.0}},\n  u'oa_embargo_months': None,\n  u'oa_obs_pub_matrix': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n  u'old_school_cpu': 17.453997,\n  u'old_school_cpu_rank': 1029.0,\n  u'perpetual_access_years': [2010,\n   2011,\n   2012,\n   2013,\n   2014,\n   2015,\n   2016,\n   2017,\n   2018,\n   2019],\n  u'raw_downloads_by_age': [20.088607594936708,\n   15.284810126582279,\n   9.170886075949367,\n   1.4556962025316456,\n   0.0],\n  u'raw_num_papers_historical_by_year': [0, 324, 326, 320, 288],\n  u'scenario_settings': {u'backfile_contribution': 100,\n   u'cost_alacart_increase': 8,\n   u'cost_bigdeal': 2100000,\n   u'cost_bigdeal_increase': 5,\n   u'cost_content_fee_percent': 5.7,\n   u'cost_ill': 17,\n   u'ill_request_percent_of_delayed': 5,\n   u'include_backfile': True,\n   u'include_bronze': True,\n   u'include_social_networks': True,\n   u'include_submitted_version': True,\n   u'package': u'658349d9',\n   u'weight_authorship': 100,\n   u'weight_citation': 10},\n  u'use_actual': {u'backfile': 15.8,\n   u'ill': 1.8006023200000003,\n   u'oa': 0.0,\n   u'other_delayed': 34.21144408,\n   u'social_networks': 0.5233536,\n   u'subscription': 0,\n   u'total': 52.3354},\n  u'use_actual_by_year': {u'backfile': [32.3479,\n    18.7826,\n    14.6087,\n    11.4783,\n    5.2174],\n   u'ill': [1.1976898000000002,\n    1.76357,\n    1.8601178000000003,\n    1.9044906,\n    2.1053884000000003],\n   u'oa': [0, 0, 0, 0, 0],\n   u'other_delayed': [22.756106200000005,\n    33.50783,\n    35.342238200000004,\n    36.18532139999999,\n    40.0023796],\n   u'social_networks': [0.5687040000000001,\n    0.5459999999999999,\n    0.523344,\n    0.500688,\n    0.47803199999999996],\n   u'subscription': [0, 0, 0, 0, 0],\n   u'total': [56.870400000000004,\n    54.599999999999994,\n    52.3344,\n    50.068799999999996,\n    47.8032]},\n  u'use_default_download_curve': True,\n  u'use_default_num_papers_curve': False,\n  u'use_instant_by_year': [32.916604,\n   19.328599999999998,\n   15.132044,\n   11.978988000000001,\n   5.695431999999999],\n  u'use_instant_percent': 31.1899,\n  u'use_instant_percent_by_year': [57.88, 35.4004, 28.9141, 23.9251, 11.9143],\n  u'use_oa': 0.0,\n  u'use_oa_percent_by_year': [0.0, 0.0, 0.0, 0.0, 0.0],\n  u'use_weight_multiplier': 1.0434794745058789,\n  u'use_weight_multiplier_normalized': 0.4878},\n u'fulfillment': {u'data': [{u'group': u'OA',\n    u'usage': u'0',\n    u'usage_percent': u'0%',\n    u'year_2020': u'0',\n    u'year_2021': u'0',\n    u'year_2022': u'0',\n    u'year_2023': u'0',\n    u'year_2024': u'0'},\n   {u'group': u'ASNs',\n    u'usage': u'1',\n    u'usage_percent': u'1%',\n    u'year_2020': u'1',\n    u'year_2021': u'1',\n    u'year_2022': u'1',\n    u'year_2023': u'1',\n    u'year_2024': u'0'},\n   {u'group': u'Backfile',\n    u'usage': u'16',\n    u'usage_percent': u'30%',\n    u'year_2020': u'32',\n    u'year_2021': u'19',\n    u'year_2022': u'15',\n    u'year_2023': u'11',\n    u'year_2024': u'5'},\n   {u'group': u'Subscription',\n    u'usage': u'0',\n    u'usage_percent': u'0%',\n    u'year_2020': u'0',\n    u'year_2021': u'0',\n    u'year_2022': u'0',\n    u'year_2023': u'0',\n    u'year_2024': u'0'},\n   {u'group': u'ILL',\n    u'usage': u'2',\n    u'usage_percent': u'3%',\n    u'year_2020': u'1',\n    u'year_2021': u'2',\n    u'year_2022': u'2',\n    u'year_2023': u'2',\n    u'year_2024': u'2'},\n   {u'group': u'Other',\n    u'usage': u'34',\n    u'usage_percent': u'65%',\n    u'year_2020': u'23',\n    u'year_2021': u'34',\n    u'year_2022': u'35',\n    u'year_2023': u'36',\n    u'year_2024': u'40'},\n   {u'group': u'*Total*',\n    u'usage': u'52',\n    u'usage_percent': u'100%',\n    u'year_2020': u'57',\n    u'year_2021': u'55',\n    u'year_2022': u'52',\n    u'year_2023': u'50',\n    u'year_2024': u'48'}],\n  u'display_perpetual_access_years': u'<2010-2019',\n  u'downloads_per_paper_by_age': [0.06493678326996198,\n   0.024091231939163495,\n   0.016772684410646388,\n   0.012747946768060837,\n   0.010292980988593157],\n  u'has_perpetual_access': True,\n  u'headers': [{u'text': u'Type', u'value': u'group'},\n   {u'text': u'Usage (projected annual)', u'value': u'usage'},\n   {u'text': u'Usage (percent)', u'value': u'usage_percent'},\n   {u'text': u'Usage projected 2020', u'value': u'year_2020'},\n   {u'text': u'2021', u'value': u'year_2021'},\n   {u'text': u'2022', u'value': u'year_2022'},\n   {u'text': u'2023', u'value': u'year_2023'},\n   {u'text': u'2024', u'value': u'year_2024'}],\n  u'perpetual_access_years_text': [2010,\n   2011,\n   2012,\n   2013,\n   2014,\n   2015,\n   2016,\n   2017,\n   2018,\n   2019],\n  u'use_actual_by_year': {u'backfile': [32.3479,\n    18.7826,\n    14.6087,\n    11.4783,\n    5.2174],\n   u'ill': [1.1976898000000002,\n    1.76357,\n    1.8601178000000003,\n    1.9044906,\n    2.1053884000000003],\n   u'oa': [0, 0, 0, 0, 0],\n   u'other_delayed': [22.756106200000005,\n    33.50783,\n    35.342238200000004,\n    36.18532139999999,\n    40.0023796],\n   u'social_networks': [0.5687040000"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<b>limit_output extension: Maximum message size of 10000 exceeded with 11876 characters</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-08T23:25:03.614621Z",
          "end_time": "2020-02-08T23:25:03.621673Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# len([1 for j in my_journals if j[\"debug\"][\"use_default_download_curve_data\"] == True])\n# len([j[\"top\"][\"issn_l\"] for j in my_journals if \"use_default_download_curve_data\" not in j[\"debug\"].keys()])\nprint sum([1 for j in my_journals if j[\"debug\"][\"use_default_download_curve\"]])",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "189\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-08T23:25:04.741674Z",
          "end_time": "2020-02-08T23:25:04.747547Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print sum([1 for j in my_journals if j[\"debug\"][\"use_default_num_papers_curve\"]])",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "41\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-08T23:25:05.924206Z",
          "end_time": "2020-02-08T23:25:05.934580Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "my_journals = details_dict[\"journals\"]\nprint sum([1 for j in my_journals if not j[\"debug\"][\"use_default_num_papers_curve\"] \n           and j[\"debug\"][\"curve_fit_for_num_papers\"][\"params\"][1] < 0 ])\nprint sum([j[\"debug\"][\"curve_fit_for_num_papers\"][\"params\"][1] for j in my_journals])",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "529\n30370.5994208\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-02-10T00:09:24.838866Z",
          "end_time": "2020-02-10T00:09:27.822169Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# %matplotlib nbagg\n%matplotlib inline\n\nfig, axes = plt.subplots(10, 10, figsize=(12, 9), sharex=False, sharey=False)\naxes_flatten = axes.flatten()\nplt.tight_layout(pad=0, w_pad=2, h_pad=1)\nplt.subplots_adjust(hspace=1)\n\ni = 0\nmy_journals = details_dict[\"journals\"]\nmy_journals = [j for j in my_journals if not j[\"debug\"][\"use_default_num_papers_curve\"]]\nrandom.shuffle(my_journals)\nmy_journals = my_journals[0:100]\n# my_journals = sorted(my_journals, key=lambda j: j[\"debug\"][\"curve_fit_for_num_papers\"][\"r_squared\"], reverse=True)\nfor j in my_journals:\n    ax = axes_flatten[i]\n    ax.set_axis_off() \n#     column_label = \"{}\\n{}\".format(r_squared, int(j[\"debug\"][\"curve_fit_for_num_papers\"][\"params\"][1]))\n#     r_squared = round(j[\"debug\"][\"curve_fit_for_num_papers\"][\"r_squared\"], 2)\n#     column_label = r_squared\n    column_label = round(j[\"debug\"][\"num_papers_slope\"], 2)\n    ax.text(.3, .2, column_label,\n        horizontalalignment='right',\n        verticalalignment='bottom',\n        fontsize=12,\n        transform=ax.transAxes)\n    ax.set_ylim(0, max(j[\"debug\"][\"curve_fit_for_num_papers\"][\"input_y\"] + j[\"debug\"][\"curve_fit_for_num_papers\"][\"y_extrap\"]))\n    ax.plot(j[\"debug\"][\"curve_fit_for_num_papers\"][\"y_fit\"] + j[\"debug\"][\"curve_fit_for_num_papers\"][\"y_extrap\"])\n    ax.plot(j[\"debug\"][\"curve_fit_for_num_papers\"][\"input_y\"], '.')\n    i += 1",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {