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273 lines (212 loc) · 8.65 KB
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import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from cStringIO import StringIO
import base64
import seaborn as sns
def getPieOne(df, conference):
fig = plt.figure()
fig = df.plot(kind = 'pie',
colormap = 'Blues',
title = '',
subplots = True,
legend = False,
labels = ['' for x in np.arange(len(df))])
plt.ylabel('')
plt.legend( list(df.index),
bbox_to_anchor=(1.1, 1),
fontsize = "xx-small")
io = StringIO()
plt.savefig(io, format='png')
img = base64.encodestring(io.getvalue())
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
script = '''<img src="data:image/png;base64,{}";/>'''
plt.close()
return script.format(data)
def getBar(df, conference, xaxis, yaxis, orientation, ylabel = 'count', xlabel = 'trada'):
plt.cla()
fig = sns.barplot(data = df,
y = yaxis,#'keyword',
x = xaxis, #'count',
palette='Blues',
orient = orientation)
fig.set_ylabel(ylabel)
fig.set_xlabel(xlabel)
io = StringIO()
plt.tight_layout()
plt.savefig(io, format='png')
img = base64.encodestring(io.getvalue())
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
script = '''<img src="data:image/png;base64,{}";/>'''
plt.close()
return script.format(data)
def getHeatMap(data_frame, indexCol = 'confName', cols = 'pubYear', vals = 'counts', annotation = False):
plt.xticks(rotation=90)
fig = sns.heatmap(data_frame.pivot_table(index=indexCol,
columns=cols,
values=vals),
annot = annotation,
cmap = 'Blues')
io = StringIO()
plt.savefig(io, format='png')
img = base64.encodestring(io.getvalue())
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
script = '''<img src="data:image/png;base64,{}";/>'''
plt.close()
return script.format(data)
def getHeatMap2(data_frame, indexCol = 'confName', cols = 'pubYear', vals = 'counts',
annotation = False, filename = 'static/Images/test.png'):
plt.cla()
plt.xticks(rotation=90)
fig = sns.heatmap(data_frame.pivot_table(index=indexCol,
columns=cols,
values=vals),
annot = annotation,
cmap = 'Blues')
plt.tight_layout()
plt.savefig((filename))
plt.close()
return fig
def getLine(data_frame, xaxis = 'confName', yaxis = 'counts'):
plt.cla()
fig = sns.swarmplot(data = data_frame,
x=xaxis,
y = yaxis,
palette = 'Blues')
io = StringIO()
plt.savefig(io, format='png')
img = base64.encodestring(io.getvalue())
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
script = '''<img src="data:image/png;base64,{}";/>'''
return script.format(data)
def getaffilbar(xaxis, yaxis, filename = 'static/Images/countryaffiliation.png'):
plt.cla()
plt.figure()
plt.xticks(rotation=90)
image = sns.barplot(x = xaxis,
y = yaxis,
palette='Blues')
for p in image.patches:
image.annotate(
s='{:.0f}'.format(p.get_height()), #label
xy=(p.get_x()+p.get_width()/2.,p.get_height()), #position
ha='center',va='center',
xytext=(0,10),
textcoords='offset points'
)
image.set_ylabel('Count')
image.set_xlabel("Country")
plt.tight_layout()
plt.savefig((filename))
return image
def createSpot(data_frame, xlabel = 'Country', ylabel = 'Counts', filename = 'static/Images/testCtsGroup.png'):
plt.cla()
#set the labels
plt.xticks( np.arange(len(data_frame)), data_frame.index, rotation = 90)
plt.xlim(-.5,len(data_frame))
plt.ylim(0, max(data_frame.max(axis = 1)) + 5)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(False)
colors = sns.color_palette('GnBu_d')
#plot!
for i,column in enumerate(data_frame.columns):
image = plt.scatter(x = data_frame.reset_index().index,
y = data_frame[column],
s = data_frame[column],
c = colors[i])
#format the legend
plt.legend(list (map(str,data_frame.columns)),
loc = 'upper left')
plt.tight_layout()
plt.savefig((filename))
return image
def areaPlot(data_frame, xlabel, ylabel, filename = 'static/Images/countryAP.png'):
plt.cla()
fig = plt.figure()
fig = data_frame.plot(kind = 'area', colormap = 'Blues', stacked = False)
fig.patch.set_facecolor('lightgray')
plt.xticks( np.arange(len(data_frame)), data_frame.index, rotation = 90)
plt.xlim(0,len(data_frame))
plt.ylim(0, max(data_frame.max(axis = 1)) + 5)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(False)
plt.tight_layout()
plt.savefig((filename))
##DRAFTS##
def getPie2():
import matplotlib.pyplot as plt
with sqlite3.connect(mydb) as con:
sqlcmd = "SELECT Conf, Year FROM ABSTRACTSTOTAL"
df = pd.read_sql_query(sqlcmd, con)
keys = list(df['Conf'].unique())
fig, axes = plt.subplots(nrows=len(keys) + 1, ncols=1,
sharex=False
)
fig = df.groupby(['Conf'])["Conf"].count().plot(kind = 'pie', colormap = 'ocean',
subplots = True, ax = axes[0] )
for i, conference in enumerate(keys):
fig = df.query('Conf == "%s"' % conference).groupby('year').count().plot(kind = 'pie',
ax = axes[i+1],
colormap = 'ocean',
subplots = True)
html = '''
<html>
<head>
<h1> Conferences by year %s</h1>
</head>
<body>
<img src="data:image/png;base64,{}" />
</body>
</html>
'''
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
return html.format(data)
def getPie(start):
df = pd.DataFrame(start[["Conf", "year"]])
fig = plt.figure()
df.groupby(['Conf'])["Conf"].count().plot(kind = 'pie',
colormap = 'ocean',
subplots = True,
title = 'Conferences',
)
for conference in df['Conf'].unique():
df.query('Conf == "%s"' % conference).groupby('year').count().plot(kind = 'pie',
subplots = True,
colormap = 'ocean',
title = conference,
)
plt.legend(bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
return html.format(data)
def getBardd(df, conference):
plt.figure()
fig = df.plot(kind = 'bar',
colormap = 'ocean',
title = conference,
subplots = True,
legend = False)
io = StringIO()
plt.savefig(io, format='png')
img = base64.encodestring(io.getvalue())
io = StringIO()
plt.savefig(io, format='png')
data = base64.encodestring(io.getvalue())
script = '''<img src="data:image/png;base64,{}";/>'''
plt.close()
return script.format(data)