def test_write_splinelib(self):
"""Test the write_splinelib function"""
mlib = self.get_mdt_library()
restype = mdt.features.ResidueType(mlib)
ang = mdt.features.Chi1Dihedral(mlib,
bins=mdt.uniform_bins(180, -180.0, 4.0))
m = self.get_test_mdt(mlib, [restype, ang])
mdt.write_splinelib(open('test.out', 'w'), m, 'chi1')
# Make sure that valid Python code was produced
code = compile(open('test.out').read(), 'test.out', 'exec')
os.unlink('test.out')
def test_write_splinelib(self):
"""Test the write_splinelib function"""
mlib = self.get_mdt_library()
restype = mdt.features.ResidueType(mlib)
ang = mdt.features.Chi1Dihedral(
mlib, bins=mdt.uniform_bins(180, -180.0, 4.0))
m = self.get_test_mdt(mlib, [restype, ang])
with open('test.out', 'w') as fh:
mdt.write_splinelib(fh, m, 'chi1')
# Make sure that valid Python code was produced
with open('test.out') as fh:
_ = compile(fh.read(), 'test.out', 'exec')
os.unlink('test.out')
# (the scaling actually does not matter, because I am eventually taking the
# log and subtracting the smallest element of the final pdf, so this command
# could be omitted without impact):
m = m.normalize(to_pdf=True, dimensions=1, dx_dy=2.5, to_zero=True)
# Take the logarithm of the smoothed frequencies
# (this is safe: none of bins is 0 because of mdt.smooth()):
m = m.log_transform(offset=0., multiplier=1.)
# Reverse the sign:
m = m.linear_transform(offset=0., multiplier=-1.)
# Offset the final distribution so that the lowest value is at 0:
m = m.offset_min(dimensions=1)
mdt.write_splinelib(file("phi.py", "w"), m, "phi", density_cutoff=0.1)
text = """
SET TICK_FONT = 5, CAPTION_FONT = 5
SET Y_TICK = -999 -999 -999
SET WORLD_WINDOW = -999 -999 -999 -999
"""
m.write_asgl(asglroot='modlib-a',
plot_type='PLOT2D',
every_x_numbered=20,
text=text,
dimensions=1,
plot_position=1,
plots_per_page=8)
os.system('asgl modlib-a')
# Start by smoothing with a uniform prior (equal weight when 10 points per bin),
# producing a normalized distribution that sums to 1 (not a pdf when dx != 1):
m = m.smooth(dimensions=1, weight=10)
# Normalize it to get the true pdf (Integral p(x) dx = 1):
# (the scaling actually does not matter, because I am eventually taking the
# log and subtracting the smallest element of the final pdf, so this command
# could be omitted without impact):
m = m.normalize(to_pdf=True, dimensions=1, dx_dy=2.5, to_zero=True)
# Take the logarithm of the smoothed frequencies
# (this is safe: none of bins is 0 because of mdt.smooth()):
m = m.log_transform(offset=0., multiplier=1.)
# Reverse the sign:
m = m.linear_transform(offset=0., multiplier=-1.)
# Offset the final distribution so that the lowest value is at 0:
m = m.offset_min(dimensions=1)
mdt.write_splinelib(file("chi2.py", "w"), m, "chi2", density_cutoff=0.1)
text = """
SET TICK_FONT = 5, CAPTION_FONT = 5
SET Y_TICK = -999 -999 -999
SET WORLD_WINDOW = -999 -999 -999 -999
"""
m.write_asgl(asglroot='modlib-a', plot_type='PLOT2D', every_x_numbered=20,
text=text, dimensions=1, plot_position=1, plots_per_page=8)
os.system('asgl modlib-a')
# (the scaling actually does not matter, because I am eventually taking the
# log and subtracting the smallest element of the final pdf, so this command
# could be omitted without impact):
m = m.normalize(to_pdf=True, dimensions=1, dx_dy=2.5, to_zero=True)
# Take the logarithm of the smoothed frequencies
# (this is safe: none of bins is 0 because of mdt.smooth()):
m = m.log_transform(offset=0., multiplier=1.)
# Reverse the sign:
m = m.linear_transform(offset=0., multiplier=-1.)
# Offset the final distribution so that the lowest value is at 0:
m = m.offset_min(dimensions=1)
mdt.write_splinelib(file("chi4.py", "w"), m, "chi4", density_cutoff=0.1)
text = """
SET TICK_FONT = 5, CAPTION_FONT = 5
SET Y_TICK = -999 -999 -999
SET WORLD_WINDOW = -999 -999 -999 -999
"""
m.write_asgl(asglroot='modlib-a',
plot_type='PLOT2D',
every_x_numbered=20,
text=text,
dimensions=1,
plot_position=1,
plots_per_page=8)
os.system('asgl modlib-a')
# Start by smoothing with a uniform prior (equal weight when 10 points per bin),
# producing a normalized distribution that sums to 1 (not a pdf when dx != 1):
m = m.smooth(dimensions=1, weight=10)
# Normalize it to get the true pdf (Integral p(x) dx = 1):
# (the scaling actually does not matter, because I am eventually taking the
# log and subtracting the smallest element of the final pdf, so this command
# could be omitted without impact):
m = m.normalize(to_pdf=True, dimensions=1, dx_dy=2.5, to_zero=True)
# Take the logarithm of the smoothed frequencies
# (this is safe: none of bins is 0 because of mdt.smooth()):
m = m.log_transform(offset=0., multiplier=1.)
# Reverse the sign:
m = m.linear_transform(offset=0., multiplier=-1.)
# Offset the final distribution so that the lowest value is at 0:
m = m.offset_min(dimensions=1)
mdt.write_splinelib(file("psi.py", "w"), m, "psi", density_cutoff=0.1)
text = """
SET TICK_FONT = 5, CAPTION_FONT = 5
SET Y_TICK = -999 -999 -999
SET WORLD_WINDOW = -999 -999 -999 -999
"""
m.write_asgl(asglroot='modlib-a', plot_type='PLOT2D', every_x_numbered=20,
text=text, dimensions=1, plot_position=1, plots_per_page=8)
os.system('asgl modlib-a')
