def histogram(data, nbins, range = None):
"""
Create a histogram.
Comes from Konrad Hinsen: Scientific Python
:param data: data list or array
:type data: [any]
:param nbins: number of bins
:type nbins: int
:param range: data range to create histogram from (min val, max val)
:type range: (float, float) OR None
:return: array (2 x len(data) ) with start of bin and witdh of bin.
:rtype: array
"""
data = N0.array(data, N0.Float)
if range is None:
min = N0.minimum.reduce(data)
max = N0.maximum.reduce(data)
else:
min, max = range
data = N0.repeat(data,
N0.logical_and(N0.less_equal(data, max),
N0.greater_equal(data, min)))
bin_width = (max-min)/nbins
data = N0.floor((data - min)/bin_width).astype(N0.Int)
histo = N0.add.reduce(N0.equal(
N0.arange(nbins)[:,N0.NewAxis], data), -1)
histo[-1] = histo[-1] + N0.add.reduce(N0.equal(nbins, data))
bins = min + bin_width*(N0.arange(nbins)+0.5)
return N0.transpose(N0.array([bins, histo]))
def group( self, a_indices, maxPerCenter ):
"""
Group a bunch of integers (atom indices in PDBModel) so that each
group has at most maxPerCenter items.
@param a_indices: atom indices
@type a_indices: [int]
@param maxPerCenter: max entries per group
@type maxPerCenter: int
@return: list of lists of int
@rtype: [[int],[int]..]
"""
## how many groups are necessary?
n_centers = len( a_indices ) // maxPerCenter ## floor division
if len( a_indices ) % maxPerCenter:
n_centers += 1
## how many items/atoms go into each group?
nAtoms = N0.ones(n_centers, N0.Int) * int(len( a_indices ) / n_centers)
i=0
while N0.sum(nAtoms) != len( a_indices ):
nAtoms[i] += 1
i += 1
## distribute atom indices into groups
result = []
pos = 0
for n in nAtoms:
result += [ N0.take( a_indices, N0.arange(n) + pos) ]
pos += n
return result
def removeFrames( self, indices ):
"""
Remove given frames from this trajectory object.
:param indices: frame numbers
:type indices: [int]
"""
i = N0.arange( self.lenFrames())
i = N0.delete( i, indices )
self.keepFrames( i )
def test_plot(self):
"""gnuplot.plot test"""
# List of (x, y) pairs
# plot([(0.,1),(1.,5),(2.,3),(3.,4)])
# plot( zip( range(10), range(10) ) )
# Two plots; each given by a 2d array
import biskit.core.oldnumeric as N0
x = N0.arange(10)
y1 = x**2
y2 = (10 - x)**2
plot(N0.transpose(N0.array([x, y1])), N0.transpose(N0.array([x, y2])))
def test_plot( self ):
"""gnuplot.plot test"""
# List of (x, y) pairs
# plot([(0.,1),(1.,5),(2.,3),(3.,4)])
# plot( zip( range(10), range(10) ) )
# Two plots; each given by a 2d array
import biskit.core.oldnumeric as N0
x = N0.arange(10)
y1 = x**2
y2 = (10-x)**2
plot( N0.transpose(N0.array([x, y1])), N0.transpose(N0.array([x, y2])))
def colorRange( nColors, palette='plasma2' ):
"""Quick access to a range of colors.
:param nColors: number of colors needed
:type nColors: int
:param palette: type of color spectrum
:type palette: str
:return: a range of color values
:rtype: [ int ]
"""
c = ColorSpectrum( palette=palette, vmin=0, vmax=1. )
r = 1. * N0.arange( 0, nColors ) / nColors
return c.colors( r )
def test_area(self):
"""mathUtils.area test"""
self.c = list(zip( N0.arange(0,1.01,0.1), N0.arange(0,1.01,0.1) ))
self.area = area( self.c )
self.assertAlmostEqual( self.area, 0.5, 7 )
def test_area(self):
"""mathUtils.area test"""
self.c = list(zip(N0.arange(0, 1.01, 0.1), N0.arange(0, 1.01, 0.1)))
self.area = area(self.c)
self.assertAlmostEqual(self.area, 0.5, 7)
def test_hist( self ):
"""hist test"""
self.x = N0.arange( 4, 12, 1.2 )
self.data = density( self.x, 3, hist=1 )
self.assertTrue( N.all( self.data == self.EXPECT) )