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#################################################################################
# Copyright (c) 2011-2015, Pacific Biosciences of California, Inc.
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of Pacific Biosciences nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY PACIFIC BIOSCIENCES AND ITS
# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL PACIFIC BIOSCIENCES OR
# ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
# BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
# IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#################################################################################
# Author: David Alexander
import numpy as np
class UnavailableFeature(Exception): pass
class Unimplemented(Exception): pass
class ReferenceMismatch(Exception): pass
class IncompatibleFile(Exception): pass
BASE_FEATURE_TAGS = { "InsertionQV" : ("iq", "qv", np.uint8),
"DeletionQV" : ("dq", "qv", np.uint8),
"DeletionTag" : ("dt", "base", np.int8 ),
"SubstitutionQV" : ("sq", "qv", np.uint8),
"MergeQV" : ("mq", "qv", np.uint8),
"Ipd:Frames" : ("ip", "frames", np.uint16),
"Ipd:CodecV1" : ("ip", "codecV1", np.uint8),
"PulseWidth:Frames" : ("pw", "frames", np.uint16),
"PulseWidth:CodecV1" : ("pw", "codecV1", np.uint8) }
PULSE_FEATURE_TAGS = { "PulseCall" : ("pc", "pulse", np.uint8),
"StartFrame" : ("sf", "frames32", np.uint32),
"PkMid" : ("pm", "photons", np.uint16),
"PkMean" : ("pa", "photons", np.uint16) }
ASCII_COMPLEMENT_MAP = { ord("A") : ord("T"),
ord("T") : ord("A"),
ord("C") : ord("G"),
ord("G") : ord("C"),
ord("N") : ord("N"),
ord("-") : ord("-") }
complementAscii = np.vectorize(ASCII_COMPLEMENT_MAP.get, otypes=[np.int8])
def reverseComplementAscii(a):
return complementAscii(a)[::-1]
BAM_CMATCH = 0
BAM_CINS = 1
BAM_CDEL = 2
BAM_CREF_SKIP = 3
BAM_CSOFT_CLIP = 4
BAM_CHARD_CLIP = 5
BAM_CPAD = 6
BAM_CEQUAL = 7
BAM_CDIFF = 8
#
# qId calculation from RG ID string
#
def rgAsInt(rgIdString):
return np.int32(int(rgIdString, 16))
#
# Kinetics: decode the scheme we are using to encode approximate frame
# counts in 8-bits.
#
def _makeFramepoints():
B = 2
t = 6
T = 2**t
framepoints = []
next = 0
for i in range(256/T):
grain = B**i
nextOnes = next + grain * np.arange(0, T)
next = nextOnes[-1] + grain
framepoints = framepoints + list(nextOnes)
return np.array(framepoints, dtype=np.uint16)
def _makeLookup(framepoints):
# (frame -> code) involves some kind of rounding
# basic round-to-nearest
frameToCode = np.empty(shape=max(framepoints)+1, dtype=int)
for i, (fl, fu) in enumerate(zip(framepoints, framepoints[1:])):
if (fu > fl + 1):
m = (fl + fu)/2
for f in xrange(fl, m):
frameToCode[f] = i
for f in xrange(m, fu):
frameToCode[f] = i + 1
else:
frameToCode[fl] = i
# Extra entry for last:
frameToCode[fu] = i + 1
return frameToCode, fu
_framepoints = _makeFramepoints()
_frameToCode, _maxFramepoint = _makeLookup(_framepoints)
def framesToCode(nframes):
nframes = np.minimum(_maxFramepoint, nframes)
return _frameToCode[nframes]
def codeToFrames(code):
return _framepoints[code]
def downsampleFrames(nframes):
return codeToFrames(framesToCode(nframes))
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