by Scott Cole
This notebook describes the data collected on poster viewing at the 2016 Society for Neuroscience annual meeting. Specifically, we look at the distribution of viewers at each poster and how that varies with the neuroscience theme. This shows that the Development theme is substantially less popular than other themes, such as Cognition.
For more details, see the associated blog post at srcole.github.io.
In [1]:
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from tools.plt import scatt_corr, unpair_2cond, scatt_2cond
import seaborn as sns
sns.set_style('white')
import pandas as pd
In [2]:
df = pd.DataFrame.from_csv('./posterviewers_theme.csv')
In [3]:
key_S = 'Session'
key_P = 'Poster number'
key_N = 'Number of people'
key_T = 'Minutes since 1pm'
In [5]:
# Count missing and withdrawn posters
N_posters = len(df)
print('# of total posters:', N_posters)
N_withdrawn = sum(df['Number of people']=='W')
print('# of withdrawn posters:', N_withdrawn, ',', np.round(100*N_withdrawn/float(N_posters),1),'%')
N_missing = sum(df['Number of people']=='99')
print('# of missing posters:', N_missing, ',', np.round(100*N_missing/float(N_posters),1),'%')
N_empty = sum(df['Number of people']=='0')
print('# of no viewers:', N_empty, ',', np.round(100*N_empty/float(N_posters),1),'%')
# Remove missing and withdrawn posters
df = df[df[key_N]!='W']
df = df[df[key_N]!='99']
# Convert observations to ints
df[key_N] = df[key_N].astype(int)
In [8]:
plt.figure(figsize=(5,3))
bins = range(12)
df.hist(key_N,bins=bins,ax=plt.gca(),normed=True,color='.5')
plt.xlabel('Number of people at a poster')
plt.ylabel('Probability')
plt.title('')
plt.xticks(np.arange(0.5,11.5,2),np.arange(0,11,2))
plt.tight_layout()
# plt.savefig('/gh/fig/sfn/viewerdist_all')
In [23]:
# Separate df into groups by Theme
dfs_grouped = {}
obj_grouped = df.groupby('Theme')
ks_Theme = obj_grouped.groups.keys()
for k in ks_Theme:
dfs_grouped[k] = obj_grouped.get_group(k)
# Remove 'Techniques' because only 2 posters were counted. Rest have at least 156
dfs_grouped.pop('Techniques')
ks_Theme = list(ks_Theme)[:-1]
In [24]:
# Count of number of posters in each theme for each day
for k in ks_Theme:
print(k,':',len(dfs_grouped[k]))
In [25]:
# Calculate probability distribution for each theme
N_themes = len(ks_Theme)
bins = np.arange(-.5,11,1)
N_bins = len(bins)-1
normviewerdist_bytheme = np.zeros((N_themes,N_bins))
for i in range(N_themes):
normviewerdist_bytheme[i], bin_edges = np.histogram(dfs_grouped[ks_Theme[i]][key_N],bins=bins,density=True)
In [26]:
# Sort themes by average number of people
# Calculate weighted average
weightedavg_bytheme = np.zeros(N_themes)
for i in range(N_themes):
weightedavg_bytheme[i] = np.average(range(11),weights=normviewerdist_bytheme[i])
# Get ranking of each theme
theme_ranks = sp.stats.rankdata(weightedavg_bytheme).astype(int) - 1
# Sort themes by weighted average
ks_Theme_sort = ['']*N_themes
weightedavg_sort = np.zeros(N_themes)
normviewerdist_sort = np.zeros((N_themes,N_bins))
for i, irank in enumerate(theme_ranks):
ks_Theme_sort[irank] = ks_Theme[i]
weightedavg_sort[irank] = weightedavg_bytheme[i]
normviewerdist_sort[irank] = normviewerdist_bytheme[i]
In [29]:
np.shape(normviewerdist_sort)
Out[29]:
In [28]:
# Make a 2d colorplot
from tools.plt import color2d #from the 'srcole/tools' repo
from matplotlib import cm
plt.figure(figsize=(10,6))
color2d(normviewerdist_sort, cmap=cm.viridis,
clim=[0,.3], cticks = np.arange(0,.31,.05), color_label='Probability',
plot_title='Poster popularity\nacross SfN 2016 Themes',
plot_xlabel='Number of viewers', plot_ylabel='Neuroscience Theme',
plot_xticks_locs=range(N_bins), plot_xticks_labels=range(N_bins),
plot_yticks_locs=range(N_themes), plot_yticks_labels=ks_Theme_sort,
interpolation='none', fontsize_minor=14, fontsize_major=19)
# On top, plot the mean
plt.plot(weightedavg_sort,range(8),'k.',ms=15)
Out[28]:
In [ ]:
# Compare development and cognition across each session
k1 = 'Development'
k2 = 'Cognition'
sessions_devcog = df['Session'].unique()
print k1,' vs. ',k2
for s in sessions_devcog:
print 'Session:', s
df_t1 = dfs_grouped[k1]
df_t2 = dfs_grouped[k2]
N1 = df_t1[df_t1[key_S]==s][key_N]
N2 = df_t2[df_t2[key_S]==s][key_N]
print 'Number of posters in',k1,':',len(N1)
print 'Number of posters in',k2,':',len(N2)
if len(N1) > 5 and len(N2) > 5:
print sp.stats.ttest_ind(N1, N2)
else:
print 'Not enough data for this session'
In [ ]:
# Compare development and sensory systems across each session
k1 = 'Development'
k2 = 'Sensory systems'
sessions_devcog = df['Session'].unique()
print k1,' vs. ',k2
for s in sessions_devcog:
print 'Session:', s
df_t1 = dfs_grouped[k1]
df_t2 = dfs_grouped[k2]
N1 = df_t1[df_t1[key_S]==s][key_N]
N2 = df_t2[df_t2[key_S]==s][key_N]
print 'Number of posters in',k1,':',len(N1)
print 'Number of posters in',k2,':',len(N2)
if len(N1) > 5 and len(N2) > 5:
print sp.stats.ttest_ind(N1, N2)
else:
print 'Not enough data for this session'
In [ ]:
# Compare development and motor systems across each session
k1 = 'Development'
k2 = 'Motor systems'
sessions_devcog = df['Session'].unique()
print k1,' vs. ',k2
for s in sessions_devcog:
print 'Session:', s
df_t1 = dfs_grouped[k1]
df_t2 = dfs_grouped[k2]
N1 = df_t1[df_t1[key_S]==s][key_N]
N2 = df_t2[df_t2[key_S]==s][key_N]
print 'Number of posters in',k1,':',len(N1)
print 'Number of posters in',k2,':',len(N2)
if len(N1) > 5 and len(N2) > 5:
print sp.stats.ttest_ind(N1, N2)
else:
print 'Not enough data for this session'