def test_lognormal(self):
"""Statistics.lognormal test"""
import random
import Biskit.gnuplot as gnuplot
import Biskit.hist as H
cr = []
for i in range( 10000 ):
## Some random values drawn from the same lognormal distribution
alpha = 1.5
beta = .7
x = 10.
R = [ random.lognormvariate( alpha, beta ) for j in range( 10 ) ]
cr += [ logConfidence( x, R )[0] ]
ca = logArea( x, alpha, beta )
if self.local:
gnuplot.plot( H.density( N.array(cr) - ca, 100 ) )
globals().update( locals() )
self.assertAlmostEqual( ca, 0.86877651432955771, 7)
def plot( delta_scores_1, delta_rms_1, delta_scores_2, delta_rms_2,
feps ):
"""
"""
p = B.FramedPlot()
p.xlabel = r"Interface rmsd to bound relative to free"
p.ylabel = r"Docking performance relative to free"
points_1 = B.Points( delta_rms_1, delta_scores_1, type='circle',
symbolsize=1)
points_1.label = 'MD'
points_2 = B.Points( delta_rms_2, delta_scores_2, type='diamond',
symbolsize=1)
points_2.label = 'PCR-MD'
a = concatenate( (delta_rms_1, delta_rms_2) )
h = density( a, 20 )
scale = max( h[:,1] ) / max( a )
histogram = B.Curve( h[:,0], h[:,1] * scale )
histogram.label = "distribution"
p.add( points_1 )
p.add( points_2 )
p.add( histogram )
p.add( B.PlotKey( 0.73, 0.95, [ points_1, points_2, histogram ] ) )
p.show()
p.write_eps( feps )
def test_lognormal(self):
"""Statistics.lognormal test"""
import random
import Biskit.gnuplot as gnuplot
import Biskit.hist as H
cr = []
for i in range(10000):
## Some random values drawn from the same lognormal distribution
alpha = 1.5
beta = .7
x = 10.
R = [random.lognormvariate(alpha, beta) for j in range(10)]
cr += [logConfidence(x, R)[0]]
ca = logArea(x, alpha, beta)
if self.local:
gnuplot.plot(H.density(N.array(cr) - ca, 100))
globals().update(locals())
self.assertAlmostEqual(ca, 0.86877651432955771, 7)
def plot( delta_scores_1, delta_rms_1, delta_scores_2, delta_rms_2,
feps ):
"""
"""
p = B.FramedPlot()
p.xlabel = r"Interface rmsd to bound relative to free"
p.ylabel = r"Docking performance relative to free"
points_1 = B.Points( delta_rms_1, delta_scores_1, type='circle',
symbolsize=1)
points_1.label = 'MD'
points_2 = B.Points( delta_rms_2, delta_scores_2, type='diamond',
symbolsize=1)
points_2.label = 'PCR-MD'
a = concatenate( (delta_rms_1, delta_rms_2) )
h = density( a, 20 )
scale = max( h[:,1] ) / max( a )
histogram = B.Curve( h[:,0], h[:,1] * scale )
histogram.label = "distribution"
p.add( points_1 )
p.add( points_2 )
p.add( histogram )
p.add( B.PlotKey( 0.73, 0.95, [ points_1, points_2, histogram ] ) )
p.show()
p.write_eps( feps )
def plotHistogram( self, *name, **arg ):
"""
@param bins: number of bins (10)
@type bins: int
@param ynormalize: normalize histograms to area 1.0 (False)
@type ynormalize: bool
@param xnormalize: adapt bin range to min and max of all profiles (True)
@type xnormalize: bool
@param xrange: min and max of bin range (None)
@type xrange: (float, float)
@param steps: draw histogram steps (True)
@type steps: bool
"""
if not biggles:
raise ImportError, 'module biggles could not be imported'
plot = biggles.FramedArray( len(name),1 )
if not 'size' in arg:
arg['size'] = 1
bins = arg.get('bins', 10)
hist = arg.get('ynormalize', False)
steps= arg.get('steps', 1)
histrange= arg.get('xrange', None)
autorange= arg.get('xnormalize', histrange is None)
xkey = arg.get('xkey', None)
if xkey:
plot.xlabel = xkey
if autorange:
v = []
for keys in name:
if not type(keys) is tuple:
keys = ( keys, )
for key in keys:
v += list(self[key])
histrange = ( min( v ), max( v ) )
for i, keys in enumerate(name):
if not type(keys) is tuple:
keys = ( keys, )
for j, key in enumerate(keys):
h = density( self[key], nBins=bins, steps=steps, hist=hist,
range=histrange )
x = h[:,0]
y = h[:,1]
colors = [0] + T.colorSpectrum( len(keys) , '00FF00', 'FF00FF')
plot[i,0].add( biggles.Curve( x, y, color=colors[j], **arg ) )
plot[i,0].add( biggles.PlotLabel( 0.7, 0.95-j*0.1, key,
color=colors[j] ) )
return plot
def plotContactDensity(self, step=1, cutoff=4.5):
"""
Example. plot histogramm of contact density. Somehing wrong??
@raise ComplexTrajError: if gnuplot program is not installed
"""
if not gnuplot.installed:
raise ComplexTrajError, 'gnuplot program is not installed'
r = self.averageContacts(step, cutoff)
r = N0.ravel(r)
r = N0.compress(r, r)
gnuplot.plot(hist.density(r, 10))
def __init__(self, p=None, values=None, bins=20):
"""
@param p: discrete distribution, array (2 x len(data) ) with
start of bin and witdh of bin (default: None)
@type p: array
@param values: list of samples (default: None)
@type values: [float]
@param bins: number of bins (default: 20)
@type bins: int
"""
if p is None and values is None:
raise ValueError, 'Either a discrete distribution or ' + \
'a list of samples must be specified'
if values is not None:
p = H.density(values, bins, steps=0)
self.store(p)
self.verbose = 0
def __init__(self, p = None, values = None, bins = 20):
"""
@param p: discrete distribution, array (2 x len(data) ) with
start of bin and witdh of bin (default: None)
@type p: array
@param values: list of samples (default: None)
@type values: [float]
@param bins: number of bins (default: 20)
@type bins: int
"""
if p is None and values is None:
raise ValueError, 'Either a discrete distribution or ' + \
'a list of samples must be specified'
if values is not None:
p = H.density(values, bins, steps = 0)
self.store(p)
self.verbose = 0
