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mods to install mathjax from an explicitly downloaded tar or zip file
mods to install mathjax from an explicitly downloaded tar or zip file

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test_rmagic.py
62 lines | 2.1 KiB | text/x-python | PythonLexer
import numpy as np
from IPython.core.interactiveshell import InteractiveShell
from IPython.extensions import rmagic
import nose.tools as nt
ip = get_ipython()
ip.magic('load_ext rmagic')
def test_push():
rm = rmagic.RMagics(ip)
ip.push({'X':np.arange(5), 'Y':np.array([3,5,4,6,7])})
ip.run_line_magic('Rpush', 'X Y')
np.testing.assert_almost_equal(np.asarray(rm.r('X')), ip.user_ns['X'])
np.testing.assert_almost_equal(np.asarray(rm.r('Y')), ip.user_ns['Y'])
def test_pull():
rm = rmagic.RMagics(ip)
rm.r('Z=c(11:20)')
ip.run_line_magic('Rpull', 'Z')
np.testing.assert_almost_equal(np.asarray(rm.r('Z')), ip.user_ns['Z'])
np.testing.assert_almost_equal(ip.user_ns['Z'], np.arange(11,21))
def test_Rconverter():
datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])
ip.user_ns['datapy'] = datapy
ip.run_line_magic('Rpush', 'datapy')
# test to see if a copy is being made
v = ip.run_line_magic('Rget', '-d datapy')
w = ip.run_line_magic('Rget', '-d datapy')
np.testing.assert_almost_equal(w['x'], v['x'])
np.testing.assert_almost_equal(w['y'], v['y'])
nt.assert_true(np.all(w['z'] == v['z']))
np.testing.assert_equal(id(w.data), id(v.data))
nt.assert_equal(w.dtype, v.dtype)
ip.run_cell_magic('R', ' -d datar datar=datapy', '')
u = ip.run_line_magic('Rget', ' -d datar')
np.testing.assert_almost_equal(u['x'], v['x'])
np.testing.assert_almost_equal(u['y'], v['y'])
nt.assert_true(np.all(u['z'] == v['z']))
np.testing.assert_equal(id(u.data), id(v.data))
nt.assert_equal(u.dtype, v.dtype)
def test_cell_magic():
ip.push({'x':np.arange(5), 'y':np.array([3,5,4,6,7])})
snippet = '''
print(summary(a))
plot(x, y, pch=23, bg='orange', cex=2)
plot(x, x)
print(summary(x))
r = resid(a)
xc = coef(a)
'''
ip.run_cell_magic('R', '-i x,y -o r,xc a=lm(y~x)', snippet)
np.testing.assert_almost_equal(ip.user_ns['xc'], [3.2, 0.9])
np.testing.assert_almost_equal(ip.user_ns['r'], np.array([-0.2, 0.9, -1. , 0.1, 0.2]))