in cites as compared to years since publication



In [1]:

    
import sqlite3
import datetime
conng = sqlite3.connect('pmcv1-graph.db') 
cg = conng.cursor()
connf = sqlite3.connect('pmcv3-full.db') 
cf = connf.cursor()



In [2]:

    
cf.execute('''SELECT DISTINCT pmid FROM meta WHERE pubdate IS NOT NULL''')
pmids = cf.fetchall()
for i in range(len(pmids)): pmids[i] = pmids[i][0]
daysoutl = []
numincitesl = []
for pmid in pmids: #pmid = 24113773
    cf.execute('''SELECT pubdate FROM meta WHERE pmid = ? ''', [pmid])
    date = cf.fetchone()[0]
    date = datetime.date(int(date[0:4]), int(date[5:7]), int(date[8:10]))
    daysout = abs((date - datetime.date(2016,6,1)).days) # days between pub and data being fetched
    if daysout > 0:
        daysoutl.append(daysout)
        cg.execute('''SELECT incites FROM cites WHERE pmid = ? ''', [pmid])
        incites = len(cg.fetchone()[0].split(','))
        numincitesl.append(incites)



In [3]:

    
#plots
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd



In [4]:

    
citedaysratio = pd.DataFrame(np.array(map(float,numincitesl)) / np.array(daysoutl))



In [5]:

    
citedaysratio.describe()









    Out[5]:






  
    
      
      0
    
  
  
    
      count
      347948.000000
    
    
      mean
      0.005236
    
    
      std
      0.013767
    
    
      min
      0.000194
    
    
      25%
      0.001587
    
    
      50%
      0.002841
    
    
      75%
      0.005291
    
    
      max
      2.675676



In [6]:

    
plt.hist(citedaysratio.clip_upper(.8), log = True, bins = 50)









    Out[6]:





(array([  3.30488000e+05,   1.16440000e+04,   3.48900000e+03,
          1.07900000e+03,   4.91000000e+02,   2.97000000e+02,
          1.90000000e+02,   7.90000000e+01,   6.60000000e+01,
          1.10000000e+01,   2.00000000e+01,   1.20000000e+01,
          8.00000000e+00,   8.00000000e+00,   1.00000000e+01,
          4.00000000e+00,   1.00000000e+00,   1.00000000e+00,
          3.00000000e+00,   3.00000000e+00,   2.00000000e+00,
          4.00000000e+00,   0.00000000e+00,   1.00000000e+00,
          2.00000000e+00,   0.00000000e+00,   0.00000000e+00,
          1.00000000e+00,   2.00000000e+00,   3.00000000e+00,
          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
          0.00000000e+00,   2.00000000e+00,   1.00000000e+00,
          1.00000000e+00,   2.00000000e+00,   1.00000000e+00,
          0.00000000e+00,   0.00000000e+00,   1.00000000e+00,
          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
          0.00000000e+00,   2.10000000e+01]),
 array([  1.94439043e-04,   1.61905503e-02,   3.21866615e-02,
          4.81827727e-02,   6.41788839e-02,   8.01749951e-02,
          9.61711064e-02,   1.12167218e-01,   1.28163329e-01,
          1.44159440e-01,   1.60155551e-01,   1.76151662e-01,
          1.92147774e-01,   2.08143885e-01,   2.24139996e-01,
          2.40136107e-01,   2.56132219e-01,   2.72128330e-01,
          2.88124441e-01,   3.04120552e-01,   3.20116663e-01,
          3.36112775e-01,   3.52108886e-01,   3.68104997e-01,
          3.84101108e-01,   4.00097220e-01,   4.16093331e-01,
          4.32089442e-01,   4.48085553e-01,   4.64081664e-01,
          4.80077776e-01,   4.96073887e-01,   5.12069998e-01,
          5.28066109e-01,   5.44062220e-01,   5.60058332e-01,
          5.76054443e-01,   5.92050554e-01,   6.08046665e-01,
          6.24042777e-01,   6.40038888e-01,   6.56034999e-01,
          6.72031110e-01,   6.88027221e-01,   7.04023333e-01,
          7.20019444e-01,   7.36015555e-01,   7.52011666e-01,
          7.68007778e-01,   7.84003889e-01,   8.00000000e-01]),
 <a list of 50 Patch objects>)



In [132]:

    
import matplotlib 
matplotlib.rc('xtick', labelsize=14) 
matplotlib.rc('ytick', labelsize=14)

plt.figure(figsize=(6,4))
plt.hist(citedaysratio.clip_upper(.8), log = True, bins = 50)
plt.xlabel("in-cites / day since publication", fontsize=16)
plt.ylabel("Counts", fontsize=16)
plt.tight_layout()
plt.savefig('citesperdayhist.svg')



In [7]:

    
counts, div = np.histogram(citedaysratio, bins = 200, range = (0,0.1))



In [8]:

    
totcounts = counts.sum()

in citations lifecycle snapshot

Looking at all papers at once - plot proportion of new in-cites vs time since publication



In [ ]:

    
cf.execute('''SELECT DISTINCT pmid FROM meta WHERE pubdate IS NOT NULL''')
pmids = cf.fetchall()
for i in range(len(pmids)): pmids[i] = pmids[i][0]
daysoutl = []
numincitesl = []
for pmid in pmids: #pmid = 24113773
    cf.execute('''SELECT pubdate FROM meta WHERE pmid = ? ''', [pmid])
    date = cf.fetchone()[0]
    date = datetime.date(int(date[0:4]), int(date[5:7]), int(date[8:10]))
    daysout = abs((date - datetime.date(2016,6,1)).days) # days between pub and data being fetched
    if daysout > 0:
        daysoutl.append(daysout)
        cg.execute('''SELECT incites FROM cites WHERE pmid = ? ''', [pmid])
        incites = len(cg.fetchone()[0].split(','))
        numincitesl.append(incites)



In [102]:

    
import math
#group in increments of 100 days
groups = 28
dayspergroup = 182
x = [i*dayspergroup for i in range(groups)]
xcounts = [0]*groups
xvalues = [0]*groups
for i, entry in enumerate(daysoutl):
    try:
        ix = x.index(math.floor(entry/float(dayspergroup))*dayspergroup)
        xcounts[ix] += 1
        xvalues[ix] += numincitesl[i]
    except ValueError: #out of range
        pass
avgcitesin100dayperiod = []
for i in range(groups):
    if xcounts[i] > 50: #fewer than 50 count -> skip
        avgcitesin100dayperiod.append(float(xvalues[i]) / float(xcounts[i]))
    else:
        avgcitesin100dayperiod.append(None)



In [103]:

    
avgcitesin100dayperiod









    Out[103]:





[1.0141030844155845,
 1.0863427946085968,
 1.3647463191915046,
 1.7494060381294547,
 2.6064204045734387,
 2.7528102031993082,
 3.167644356491625,
 3.8347941567065074,
 4.4959389372737055,
 5.4911111111111115,
 5.693342676944639,
 5.860563168197561,
 7.2760973810402065,
 7.89718482252142,
 8.389941012108041,
 8.445446348061317,
 4.656296296296296,
 5.4262890341321715,
 5.099683544303797,
 8.309875141884222,
 15.975172413793103,
 13.061302681992338,
 14.439929328621908,
 5.329411764705882,
 6.053215077605321,
 5.5964912280701755,
 5.296178343949045,
 5.3686868686868685]



In [105]:

    
import numpy as np
z = np.polyfit(x, avgcitesin100dayperiod, 3)
f = np.poly1d(z)
print f
x_new = np.linspace(x[0], x[-1], 50)
y_new = f(x_new)









    



            3             2
-3.777e-10 x + 2.047e-06 x - 0.0002426 x + 1.358



In [109]:

    
plt.plot(x, avgcitesin100dayperiod, 'bo', x_new, y_new, alpha=0.75)









    Out[109]:





[<matplotlib.lines.Line2D at 0x108ac1f50>,
 <matplotlib.lines.Line2D at 0x108bdd890>]



In [131]:

    
import matplotlib 
matplotlib.rc('xtick', labelsize=14) 
matplotlib.rc('ytick', labelsize=14)

plt.figure(figsize=(6,4))
plt.plot(x, avgcitesin100dayperiod, 'bo', alpha=0.75)
plt.xlabel("Days since publication", fontsize=16)
plt.ylabel("Average number of in-citations", fontsize=16)
plt.tight_layout()
plt.savefig('incitesvstime.svg')



In [ ]:

    
plt.ticklabel_format(fontsize=14)

compute "citation quality" as citations / day given a PMID

used to compute "influence factor" on webapp

reset kernel before beginning so code can easily be reused



In [28]:

    
import numpy as np
counts = [13027, 34108, 33621, 34843, 37047, 30559, 22854, 18040, 16354, 13309, 11530, 8719, 6640, 5484, 5449, 4666, 5021, 3330, 3152, 2689, 3257, 2887, 1743, 1852, 1591, 1778, 1336, 773, 1851, 491, 396, 1063, 1589, 338, 407, 922, 623, 406, 128, 457, 999, 677, 410, 190, 73, 492, 535, 423, 601, 47, 449, 175, 53, 43, 363, 572, 38, 497, 507, 39, 406, 27, 150, 22, 139, 30, 182, 24, 410, 24, 22, 553, 19, 18, 499, 12, 397, 20, 14, 19, 25, 15, 19, 18, 9, 14, 250, 9, 7, 10, 381, 9, 10, 7, 5, 327, 13, 7, 9, 7, 389, 9, 7, 6, 12, 277, 8, 6, 4, 6, 7, 72, 1, 5, 6, 5, 5, 38, 5, 4, 4, 6, 7, 5, 1, 144, 4, 6, 0, 6, 1, 4, 1, 179, 3, 3, 7, 1, 2, 2, 2, 1, 135, 1, 4, 4, 3, 2, 5, 5, 1, 5, 1, 101, 4, 3, 2, 2, 0, 1, 3, 3, 2, 3, 4, 1, 133, 3, 1, 3, 0, 0, 2, 2, 3, 3, 2, 1, 3, 3, 3, 99, 3, 1, 2, 1, 2, 5, 1, 0, 2, 3, 2, 0, 2, 1, 3, 2, 0, 1]
sumcounts = 347499.
div = [ 0. , 0.0005, 0.001 , 0.0015, 0.002 , 0.0025, 0.003 , 0.0035, 0.004 , 0.0045, 0.005 , 0.0055, 0.006 , 0.0065, 0.007 , 0.0075, 0.008 , 0.0085, 0.009 , 0.0095, 0.01 , 0.0105, 0.011 , 0.0115, 0.012 , 0.0125, 0.013 , 0.0135, 0.014 , 0.0145, 0.015 , 0.0155, 0.016 , 0.0165, 0.017 , 0.0175, 0.018 , 0.0185, 0.019 , 0.0195, 0.02 , 0.0205, 0.021 , 0.0215, 0.022 , 0.0225, 0.023 , 0.0235, 0.024 , 0.0245, 0.025 , 0.0255, 0.026 , 0.0265, 0.027 , 0.0275, 0.028 , 0.0285, 0.029 , 0.0295, 0.03 , 0.0305, 0.031 , 0.0315, 0.032 , 0.0325, 0.033 , 0.0335, 0.034 , 0.0345, 0.035 , 0.0355, 0.036 , 0.0365, 0.037 , 0.0375, 0.038 , 0.0385, 0.039 , 0.0395, 0.04 , 0.0405, 0.041 , 0.0415, 0.042 , 0.0425, 0.043 , 0.0435, 0.044 , 0.0445, 0.045 , 0.0455, 0.046 , 0.0465, 0.047 , 0.0475, 0.048 , 0.0485, 0.049 , 0.0495, 0.05 , 0.0505, 0.051 , 0.0515, 0.052 , 0.0525, 0.053 , 0.0535, 0.054 , 0.0545, 0.055 , 0.0555, 0.056 , 0.0565, 0.057 , 0.0575, 0.058 , 0.0585, 0.059 , 0.0595, 0.06 , 0.0605, 0.061 , 0.0615, 0.062 , 0.0625, 0.063 , 0.0635, 0.064 , 0.0645, 0.065 , 0.0655, 0.066 , 0.0665, 0.067 , 0.0675, 0.068 , 0.0685, 0.069 , 0.0695, 0.07 , 0.0705, 0.071 , 0.0715, 0.072 , 0.0725, 0.073 , 0.0735, 0.074 , 0.0745, 0.075 , 0.0755, 0.076 , 0.0765, 0.077 , 0.0775, 0.078 , 0.0785, 0.079 , 0.0795, 0.08 , 0.0805, 0.081 , 0.0815, 0.082 , 0.0825, 0.083 , 0.0835, 0.084 , 0.0845, 0.085 , 0.0855, 0.086 , 0.0865, 0.087 , 0.0875, 0.088 , 0.0885, 0.089 , 0.0895, 0.09 , 0.0905, 0.091 , 0.0915, 0.092 , 0.0925, 0.093 , 0.0935, 0.094 , 0.0945, 0.095 , 0.0955, 0.096 , 0.0965, 0.097 , 0.0975, 0.098 , 0.0985, 0.099 , 0.0995, 0.1 ]



In [31]:

    
import sqlite3
import datetime
conng = sqlite3.connect('pmcv1-graph.db')  # CHANGE TO v2 and rerun!
cg = conng.cursor()
connf = sqlite3.connect('pmcv2-full.db') 
cf = connf.cursor()



In [61]:

    
cf.execute('SELECT pmid FROM meta ORDER BY RANDOM() LIMIT 1')
pmid = cf.fetchone()[0]
print pmid
#pmid = 26938165
cf.execute('''SELECT pubdate FROM meta WHERE pmid = ? ''', [pmid])
date = cf.fetchone()[0]
date = datetime.date(int(date[0:4]), int(date[5:7]), int(date[8:10]))
daysout = abs((date - datetime.date(2016,6,1)).days) # days between pub and data being fetched
if daysout <= 0:
    pass #return median value?
else:
    cg.execute('''SELECT incites FROM cites WHERE pmid = ? ''', [pmid])
    incites = len(cg.fetchone()[0].split(','))
    citeratio = incites / float(daysout)
# lookup in histogram
for i, division in enumerate(div):
    if citeratio < division:
        print "percentile: {}".format(np.array(counts[0:i+1]).sum() / sumcounts * 100)
        break









    



20191640
percentile: 13.5640678103



In [63]:

    
# as function
import numpy as np
import datetime

def incitepercentile(pmid, fullcursor, graphcursor): 
    counts = [13027, 34108, 33621, 34843, 37047, 30559, 22854, 18040, 16354, 13309, 11530, 8719, 6640, 5484, 5449, 4666, 5021, 3330, 3152, 2689, 3257, 2887, 1743, 1852, 1591, 1778, 1336, 773, 1851, 491, 396, 1063, 1589, 338, 407, 922, 623, 406, 128, 457, 999, 677, 410, 190, 73, 492, 535, 423, 601, 47, 449, 175, 53, 43, 363, 572, 38, 497, 507, 39, 406, 27, 150, 22, 139, 30, 182, 24, 410, 24, 22, 553, 19, 18, 499, 12, 397, 20, 14, 19, 25, 15, 19, 18, 9, 14, 250, 9, 7, 10, 381, 9, 10, 7, 5, 327, 13, 7, 9, 7, 389, 9, 7, 6, 12, 277, 8, 6, 4, 6, 7, 72, 1, 5, 6, 5, 5, 38, 5, 4, 4, 6, 7, 5, 1, 144, 4, 6, 0, 6, 1, 4, 1, 179, 3, 3, 7, 1, 2, 2, 2, 1, 135, 1, 4, 4, 3, 2, 5, 5, 1, 5, 1, 101, 4, 3, 2, 2, 0, 1, 3, 3, 2, 3, 4, 1, 133, 3, 1, 3, 0, 0, 2, 2, 3, 3, 2, 1, 3, 3, 3, 99, 3, 1, 2, 1, 2, 5, 1, 0, 2, 3, 2, 0, 2, 1, 3, 2, 0, 1]
    sumcounts = 347499.
    div = [ 0. , 0.0005, 0.001 , 0.0015, 0.002 , 0.0025, 0.003 , 0.0035, 0.004 , 0.0045, 0.005 , 0.0055, 0.006 , 0.0065, 0.007 , 0.0075, 0.008 , 0.0085, 0.009 , 0.0095, 0.01 , 0.0105, 0.011 , 0.0115, 0.012 , 0.0125, 0.013 , 0.0135, 0.014 , 0.0145, 0.015 , 0.0155, 0.016 , 0.0165, 0.017 , 0.0175, 0.018 , 0.0185, 0.019 , 0.0195, 0.02 , 0.0205, 0.021 , 0.0215, 0.022 , 0.0225, 0.023 , 0.0235, 0.024 , 0.0245, 0.025 , 0.0255, 0.026 , 0.0265, 0.027 , 0.0275, 0.028 , 0.0285, 0.029 , 0.0295, 0.03 , 0.0305, 0.031 , 0.0315, 0.032 , 0.0325, 0.033 , 0.0335, 0.034 , 0.0345, 0.035 , 0.0355, 0.036 , 0.0365, 0.037 , 0.0375, 0.038 , 0.0385, 0.039 , 0.0395, 0.04 , 0.0405, 0.041 , 0.0415, 0.042 , 0.0425, 0.043 , 0.0435, 0.044 , 0.0445, 0.045 , 0.0455, 0.046 , 0.0465, 0.047 , 0.0475, 0.048 , 0.0485, 0.049 , 0.0495, 0.05 , 0.0505, 0.051 , 0.0515, 0.052 , 0.0525, 0.053 , 0.0535, 0.054 , 0.0545, 0.055 , 0.0555, 0.056 , 0.0565, 0.057 , 0.0575, 0.058 , 0.0585, 0.059 , 0.0595, 0.06 , 0.0605, 0.061 , 0.0615, 0.062 , 0.0625, 0.063 , 0.0635, 0.064 , 0.0645, 0.065 , 0.0655, 0.066 , 0.0665, 0.067 , 0.0675, 0.068 , 0.0685, 0.069 , 0.0695, 0.07 , 0.0705, 0.071 , 0.0715, 0.072 , 0.0725, 0.073 , 0.0735, 0.074 , 0.0745, 0.075 , 0.0755, 0.076 , 0.0765, 0.077 , 0.0775, 0.078 , 0.0785, 0.079 , 0.0795, 0.08 , 0.0805, 0.081 , 0.0815, 0.082 , 0.0825, 0.083 , 0.0835, 0.084 , 0.0845, 0.085 , 0.0855, 0.086 , 0.0865, 0.087 , 0.0875, 0.088 , 0.0885, 0.089 , 0.0895, 0.09 , 0.0905, 0.091 , 0.0915, 0.092 , 0.0925, 0.093 , 0.0935, 0.094 , 0.0945, 0.095 , 0.0955, 0.096 , 0.0965, 0.097 , 0.0975, 0.098 , 0.0985, 0.099 , 0.0995, 0.1 ]
    fullcursor.execute('''SELECT pubdate FROM meta WHERE pmid = ? ''', [pmid])
    date = fullcursor.fetchone()[0]
    date = datetime.date(int(date[0:4]), int(date[5:7]), int(date[8:10]))
    daysout = abs((date - datetime.date(2016,6,1)).days) # days between pub and data being fetched
    if daysout <= 0:
        return 0
    else:
        graphcursor.execute('''SELECT incites FROM cites WHERE pmid = ? ''', [pmid])
        incites = len(cg.fetchone()[0].split(','))
        citeratio = incites / float(daysout)
    # lookup in histogram
    for i, division in enumerate(div):
        if citeratio < division:
            return np.array(counts[0:i+1]).sum() / sumcounts * 100



In [64]:

    
incitepercentile(20191640, cf, cg)









    Out[64]:





13.56406781026708

"quality" of in-cites. how highly cited are the in cites?

could also examine this in terms of how highly cited the in-citer's authors are, but will leave this as future work

To consider: how to address that very highly cited in-cites should count for a lot? 3 20%tile incites for one paper shouldn't look as good as 1 60%tile incite for another paper. Need to weight highly cited papers more

For now, will simply return mean quality



In [ ]:

    
# go through each in cite - how high quality are the incites
# use baysian smoothing to address papers with low citation counts
# -> add 5 average quality cites to every cite before computing metric



In [1]:

    
import sqlite3
conng = sqlite3.connect('pmcv1-graph.db')  # CHANGE TO v2 and rerun!
cg = conng.cursor()
connf = sqlite3.connect('pmcv2-full.db') 
cf = connf.cursor()



In [29]:

    
import numpy as np
import datetime

def incitepercentile(pmid, fullcursor, graphcursor): 
    counts = [13027, 34108, 33621, 34843, 37047, 30559, 22854, 18040, 16354, 13309, 11530, 8719, 6640, 5484, 5449, 4666, 5021, 3330, 3152, 2689, 3257, 2887, 1743, 1852, 1591, 1778, 1336, 773, 1851, 491, 396, 1063, 1589, 338, 407, 922, 623, 406, 128, 457, 999, 677, 410, 190, 73, 492, 535, 423, 601, 47, 449, 175, 53, 43, 363, 572, 38, 497, 507, 39, 406, 27, 150, 22, 139, 30, 182, 24, 410, 24, 22, 553, 19, 18, 499, 12, 397, 20, 14, 19, 25, 15, 19, 18, 9, 14, 250, 9, 7, 10, 381, 9, 10, 7, 5, 327, 13, 7, 9, 7, 389, 9, 7, 6, 12, 277, 8, 6, 4, 6, 7, 72, 1, 5, 6, 5, 5, 38, 5, 4, 4, 6, 7, 5, 1, 144, 4, 6, 0, 6, 1, 4, 1, 179, 3, 3, 7, 1, 2, 2, 2, 1, 135, 1, 4, 4, 3, 2, 5, 5, 1, 5, 1, 101, 4, 3, 2, 2, 0, 1, 3, 3, 2, 3, 4, 1, 133, 3, 1, 3, 0, 0, 2, 2, 3, 3, 2, 1, 3, 3, 3, 99, 3, 1, 2, 1, 2, 5, 1, 0, 2, 3, 2, 0, 2, 1, 3, 2, 0, 1]
    sumcounts = 347499.
    div = [ 0. , 0.0005, 0.001 , 0.0015, 0.002 , 0.0025, 0.003 , 0.0035, 0.004 , 0.0045, 0.005 , 0.0055, 0.006 , 0.0065, 0.007 , 0.0075, 0.008 , 0.0085, 0.009 , 0.0095, 0.01 , 0.0105, 0.011 , 0.0115, 0.012 , 0.0125, 0.013 , 0.0135, 0.014 , 0.0145, 0.015 , 0.0155, 0.016 , 0.0165, 0.017 , 0.0175, 0.018 , 0.0185, 0.019 , 0.0195, 0.02 , 0.0205, 0.021 , 0.0215, 0.022 , 0.0225, 0.023 , 0.0235, 0.024 , 0.0245, 0.025 , 0.0255, 0.026 , 0.0265, 0.027 , 0.0275, 0.028 , 0.0285, 0.029 , 0.0295, 0.03 , 0.0305, 0.031 , 0.0315, 0.032 , 0.0325, 0.033 , 0.0335, 0.034 , 0.0345, 0.035 , 0.0355, 0.036 , 0.0365, 0.037 , 0.0375, 0.038 , 0.0385, 0.039 , 0.0395, 0.04 , 0.0405, 0.041 , 0.0415, 0.042 , 0.0425, 0.043 , 0.0435, 0.044 , 0.0445, 0.045 , 0.0455, 0.046 , 0.0465, 0.047 , 0.0475, 0.048 , 0.0485, 0.049 , 0.0495, 0.05 , 0.0505, 0.051 , 0.0515, 0.052 , 0.0525, 0.053 , 0.0535, 0.054 , 0.0545, 0.055 , 0.0555, 0.056 , 0.0565, 0.057 , 0.0575, 0.058 , 0.0585, 0.059 , 0.0595, 0.06 , 0.0605, 0.061 , 0.0615, 0.062 , 0.0625, 0.063 , 0.0635, 0.064 , 0.0645, 0.065 , 0.0655, 0.066 , 0.0665, 0.067 , 0.0675, 0.068 , 0.0685, 0.069 , 0.0695, 0.07 , 0.0705, 0.071 , 0.0715, 0.072 , 0.0725, 0.073 , 0.0735, 0.074 , 0.0745, 0.075 , 0.0755, 0.076 , 0.0765, 0.077 , 0.0775, 0.078 , 0.0785, 0.079 , 0.0795, 0.08 , 0.0805, 0.081 , 0.0815, 0.082 , 0.0825, 0.083 , 0.0835, 0.084 , 0.0845, 0.085 , 0.0855, 0.086 , 0.0865, 0.087 , 0.0875, 0.088 , 0.0885, 0.089 , 0.0895, 0.09 , 0.0905, 0.091 , 0.0915, 0.092 , 0.0925, 0.093 , 0.0935, 0.094 , 0.0945, 0.095 , 0.0955, 0.096 , 0.0965, 0.097 , 0.0975, 0.098 , 0.0985, 0.099 , 0.0995, 0.1 ]
    fullcursor.execute('''SELECT pubdate FROM meta WHERE pmid = ? ''', [pmid])
    date = fullcursor.fetchone()[0]
    date = datetime.date(int(date[0:4]), int(date[5:7]), int(date[8:10]))
    daysout = abs((date - datetime.date(2016,6,1)).days) # days between pub and data being fetched
    if daysout <= 0:
        return 0
    else:
        graphcursor.execute('''SELECT incites FROM cites WHERE pmid = ? ''', [pmid])
        incites = len(cg.fetchone()[0].split(','))
        citeratio = incites / float(daysout)
    # lookup in histogram
    for i, division in enumerate(div):
        if citeratio < division:
            return np.array(counts[0:i+1]).sum() / sumcounts * 100

def incitequalityindex(pmid, fullcursor, graphcursor):
    graphcursor.execute('''SELECT incites FROM cites WHERE pmid = ?''', [pmid])
    try:
        incites = map(int, graphcursor.fetchall()[0][0].split(","))
    except ValueError: # no incites in db for this pub
        return 69.5
    results = []
    for cite in incites:
        try:
            result = incitepercentile(cite, fullcursor, graphcursor)
        except TypeError: # issue with date
            result = None
        if result != None:
            results.append(result)
    if np.isnan(np.array(results).mean()):
        return 69.5
    return np.array(results).mean()



In [5]:

    
incitequalityindex(25901944, cf, cg)









    Out[5]:





75.78813176440795



In [31]:

    
# find mean incitequalityindex
cf.execute('''SELECT DISTINCT pmid FROM meta''')
pmids = map(lambda x: x[0], cf.fetchall())
globalincitequality = []
for pmid in pmids:
    result = incitequalityindex(pmid, cf, cg)
    if result != 69.5:
        globalincitequality.append(result)



In [34]:

    
import pandas as pd
pd.DataFrame(globalincitequality).describe()









    Out[34]:






  
    
      
      0
    
  
  
    
      count
      123965.000000
    
    
      mean
      69.548052
    
    
      std
      18.229639
    
    
      min
      0.000000
    
    
      25%
      59.297725
    
    
      50%
      71.473388
    
    
      75%
      83.103453
    
    
      max
      99.991942



In [ ]:

    
# so center value should be 69.5



In [ ]:

author self-citation index

unclear what the source of error is here, return to this later



In [1]:

    
import sqlite3
conng = sqlite3.connect('pmcv1-graph.db')  # CHANGE TO v2 and rerun!
cg = conng.cursor()
connf = sqlite3.connect('pmcv2-full.db') 
cf = connf.cursor()



In [40]:

    
def selfcitesperpub(author, fullcursor, graphcursor):
    fullcursor.execute('''SELECT DISTINCT pmid FROM authors WHERE abbr = ?''', [author])
    authorspapers = set(map(lambda x: x[0], fullcursor.fetchall()))
    selfcites = 0.
    pubs = len(authorspapers)
    for pmid in authorspapers:
        graphcursor.execute('''SELECT outcites FROM cites WHERE pmid = ?''', [pmid])
        outcites = set(map(lambda x: x[0], graphcursor.fetchall()))
        selfcites += len(authorspapers.intersection(outcites))
    return selfcites / float(pubs)



In [31]:

    
selfcitesperpub(u'kosukeyoshihara', cf, cg)









    Out[31]:





0.0



In [35]:

    
cf.execute('''SELECT DISTINCT abbr FROM authors''')
allauthors = map(lambda x: x[0], cf.fetchall())



In [37]:

    
selfcitesglobal = []
%time for author in allauthors: selfcitesglobal.append(selfcitesperpub(author, cf, cg))









    



CPU times: user 37 s, sys: 25.7 s, total: 1min 2s
Wall time: 1min 2s



In [38]:

    
#plots
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd



In [39]:

    
pd.DataFrame(selfcitesglobal).describe()



In [ ]:

	0
count	347948.000000
mean	0.005236
std	0.013767
min	0.000194
25%	0.001587
50%	0.002841
75%	0.005291
max	2.675676

	0
count	123965.000000
mean	69.548052
std	18.229639
min	0.000000
25%	59.297725
50%	71.473388
75%	83.103453
max	99.991942