[med-svn] [deepnano] 01/01: Do not commit temporary package build results
Andreas Tille
tille at debian.org
Sat Dec 17 08:50:59 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository deepnano.
commit e0c504307e4bc5c8810438718360470c656b891d
Author: Andreas Tille <tille at debian.org>
Date: Sat Dec 17 09:50:24 2016 +0100
Do not commit temporary package build results
---
debian/deepnano/DEBIAN/control | 15 -
debian/deepnano/DEBIAN/md5sums | 25 --
debian/deepnano/DEBIAN/postinst | 9 -
debian/deepnano/DEBIAN/prerm | 14 -
debian/deepnano/usr/bin/deepnano_basecall | 5 -
.../usr/bin/deepnano_basecall_no_metrichor | 1 -
debian/deepnano/usr/lib/deepnano/align_2d | Bin 43096 -> 0 bytes
debian/deepnano/usr/lib/deepnano/realign | Bin 39000 -> 0 bytes
debian/deepnano/usr/share/deepnano/basecall.py | 185 ----------
.../usr/share/deepnano/basecall_no_metrichor.py | 277 ---------------
.../share/deepnano/basecall_no_metrichor_devel.py | 371 ---------------------
debian/deepnano/usr/share/deepnano/helpers.py | 76 -----
debian/deepnano/usr/share/deepnano/rnn_fin.py | 81 -----
.../usr/share/doc/deepnano/changelog.Debian.gz | Bin 271 -> 0 bytes
debian/deepnano/usr/share/doc/deepnano/copyright | 36 --
.../doc/deepnano/examples/nets_data/map5-2d.npz.gz | Bin 5082272 -> 0 bytes
.../deepnano/examples/nets_data/map5comp.npz.gz | Bin 1592095 -> 0 bytes
.../deepnano/examples/nets_data/map5temp.npz.gz | Bin 1592084 -> 0 bytes
.../deepnano/examples/nets_data/map6-2d-big.npz.gz | Bin 14015984 -> 0 bytes
.../examples/nets_data/map6-2d-no-metr.npz.gz | Bin 14015890 -> 0 bytes
.../examples/nets_data/map6-2d-no-metr10.npz.gz | Bin 14016340 -> 0 bytes
.../examples/nets_data/map6-2d-no-metr20.npz.gz | Bin 14015359 -> 0 bytes
.../examples/nets_data/map6-2d-no-metr23.npz.gz | Bin 14016230 -> 0 bytes
.../doc/deepnano/examples/nets_data/map6-2d.npz.gz | Bin 5081800 -> 0 bytes
.../deepnano/examples/nets_data/map6comp.npz.gz | Bin 1592557 -> 0 bytes
.../deepnano/examples/nets_data/map6temp.npz.gz | Bin 1592875 -> 0 bytes
.../2016_3_4_3507_1_ch120_read521_strand.fast5.gz | Bin 861647 -> 0 bytes
.../2016_3_4_3507_1_ch13_read1130_strand.fast5.gz | Bin 1066763 -> 0 bytes
.../2016_3_4_3507_1_ch13_read1132_strand.fast5.gz | Bin 1320321 -> 0 bytes
.../usr/share/python/runtime.d/deepnano.rtupdate | 7 -
30 files changed, 1102 deletions(-)
diff --git a/debian/deepnano/DEBIAN/control b/debian/deepnano/DEBIAN/control
deleted file mode 100644
index 40bb851..0000000
--- a/debian/deepnano/DEBIAN/control
+++ /dev/null
@@ -1,15 +0,0 @@
-Package: deepnano
-Version: 0.0+20110617-1
-Architecture: amd64
-Maintainer: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.org>
-Installed-Size: 87902
-Depends: python:any (>= 2.7.5-5~), libc6 (>= 2.2.5), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python-h5py, python-numpy, python-dateutil, python-theano
-Section: science
-Priority: optional
-Homepage: https://bitbucket.org/vboza/deepnano
-Description: alternative basecaller for MinION reads of genomic sequences
- DeepNano is alternative basecaller for Oxford Nanopore MinION reads
- based on deep recurrent neural networks.
- .
- Currently it works with SQK-MAP-006 and SQK-MAP-005 chemistry and as a
- postprocessor for Metrichor.
diff --git a/debian/deepnano/DEBIAN/md5sums b/debian/deepnano/DEBIAN/md5sums
deleted file mode 100644
index 64127b6..0000000
--- a/debian/deepnano/DEBIAN/md5sums
+++ /dev/null
@@ -1,25 +0,0 @@
-cba2f62f9fc586043fc00938b0e932b6 usr/bin/deepnano_basecall
-2b88df4d884e7afa2f22870458c97757 usr/lib/deepnano/align_2d
-bdb5eb7d2d0b3d70145310b7131c8d02 usr/lib/deepnano/realign
-bce23353ab354f2528a5de9661a5230c usr/share/deepnano/basecall.py
-5e1fe3018daa7b36e249c2157411812a usr/share/deepnano/basecall_no_metrichor.py
-3a4ae91d811983676c1f6237c8fec97e usr/share/deepnano/basecall_no_metrichor_devel.py
-115ccfa267eb418b79d57a4aad9b039e usr/share/deepnano/helpers.py
-e9bb97314500d839bb0ec8315a7a4ef9 usr/share/deepnano/rnn_fin.py
-cdf6a037be6f655d9c83430fbcc6f9d4 usr/share/doc/deepnano/changelog.Debian.gz
-35b0edea4c50091a781a9385b8c7705f usr/share/doc/deepnano/copyright
-702509a2bdf2369f5ea14062d5ae7762 usr/share/doc/deepnano/examples/nets_data/map5-2d.npz.gz
-e6b1b2969b7448accf054142b846ab62 usr/share/doc/deepnano/examples/nets_data/map5comp.npz.gz
-fe10cb4e2efb306594eea797ceba70e4 usr/share/doc/deepnano/examples/nets_data/map5temp.npz.gz
-fb3755161d24834453c9d9d2f7db9353 usr/share/doc/deepnano/examples/nets_data/map6-2d-big.npz.gz
-818c6b69c501943804cf2aca1b5203c3 usr/share/doc/deepnano/examples/nets_data/map6-2d-no-metr.npz.gz
-d93a44348cc5b454b15338dccec70b0f usr/share/doc/deepnano/examples/nets_data/map6-2d-no-metr10.npz.gz
-7872e4100faa2dd13e21549174b0f171 usr/share/doc/deepnano/examples/nets_data/map6-2d-no-metr20.npz.gz
-a672d7cba84ba1f8aacb36f998dc6866 usr/share/doc/deepnano/examples/nets_data/map6-2d-no-metr23.npz.gz
-273653b4f06a1529a2448c53a8dcc94c usr/share/doc/deepnano/examples/nets_data/map6-2d.npz.gz
-af5b1570fe91051b69e013d63bc5d446 usr/share/doc/deepnano/examples/nets_data/map6comp.npz.gz
-3e5342e80bad5a6e7193db9956c6380a usr/share/doc/deepnano/examples/nets_data/map6temp.npz.gz
-c9a6911fe747ab12be4721e4f543a609 usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch120_read521_strand.fast5.gz
-2f64706324cd5e8f10666f6b19fac14c usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch13_read1130_strand.fast5.gz
-3113c8f6d453c1619ea606e7f768e10d usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch13_read1132_strand.fast5.gz
-788eec3c08bb9ed41061cccd5f6d9d05 usr/share/python/runtime.d/deepnano.rtupdate
diff --git a/debian/deepnano/DEBIAN/postinst b/debian/deepnano/DEBIAN/postinst
deleted file mode 100755
index 5aac91b..0000000
--- a/debian/deepnano/DEBIAN/postinst
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/sh
-set -e
-
-# Automatically added by dh_python2:
-if which pycompile >/dev/null 2>&1; then
- pycompile -p deepnano /usr/share/deepnano
-fi
-
-# End automatically added section
diff --git a/debian/deepnano/DEBIAN/prerm b/debian/deepnano/DEBIAN/prerm
deleted file mode 100755
index a4c1086..0000000
--- a/debian/deepnano/DEBIAN/prerm
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/sh
-set -e
-
-# Automatically added by dh_python2:
-if which pyclean >/dev/null 2>&1; then
- pyclean -p deepnano
-else
- dpkg -L deepnano | grep \.py$ | while read file
- do
- rm -f "${file}"[co] >/dev/null
- done
-fi
-
-# End automatically added section
diff --git a/debian/deepnano/usr/bin/deepnano_basecall b/debian/deepnano/usr/bin/deepnano_basecall
deleted file mode 100755
index 1d79c0a..0000000
--- a/debian/deepnano/usr/bin/deepnano_basecall
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/sh
-
-SCRIPT=`basename $0 | sed 's/^deepnano_//'`
-
-/usr/share/deepnano/${SCRIPT}.py $@
diff --git a/debian/deepnano/usr/bin/deepnano_basecall_no_metrichor b/debian/deepnano/usr/bin/deepnano_basecall_no_metrichor
deleted file mode 120000
index 2041646..0000000
--- a/debian/deepnano/usr/bin/deepnano_basecall_no_metrichor
+++ /dev/null
@@ -1 +0,0 @@
-deepnano_basecall
\ No newline at end of file
diff --git a/debian/deepnano/usr/lib/deepnano/align_2d b/debian/deepnano/usr/lib/deepnano/align_2d
deleted file mode 100755
index 6ce2cda..0000000
Binary files a/debian/deepnano/usr/lib/deepnano/align_2d and /dev/null differ
diff --git a/debian/deepnano/usr/lib/deepnano/realign b/debian/deepnano/usr/lib/deepnano/realign
deleted file mode 100755
index 47dbc8d..0000000
Binary files a/debian/deepnano/usr/lib/deepnano/realign and /dev/null differ
diff --git a/debian/deepnano/usr/share/deepnano/basecall.py b/debian/deepnano/usr/share/deepnano/basecall.py
deleted file mode 100755
index aa81f75..0000000
--- a/debian/deepnano/usr/share/deepnano/basecall.py
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/usr/bin/python
-import argparse
-from rnn_fin import RnnPredictor
-import h5py
-import sys
-import numpy as np
-import theano as th
-import os
-import re
-import dateutil.parser
-import datetime
-from helpers import *
-
-def load_read_data(read_file):
- h5 = h5py.File(read_file, "r")
- ret = {}
-
- extract_timing(h5, ret)
-
- base_loc = get_base_loc(h5)
-
- try:
- ret["called_template"] = h5[base_loc+"/BaseCalled_template/Fastq"][()].split('\n')[1]
- ret["called_complement"] = h5[base_loc+"/BaseCalled_complement/Fastq"][()].split('\n')[1]
- ret["called_2d"] = h5["Analyses/Basecall_2D_000/BaseCalled_2D/Fastq"][()].split('\n')[1]
- except Exception as e:
- pass
- try:
- events = h5[base_loc+"/BaseCalled_template/Events"]
- tscale, tscale_sd, tshift, tdrift = extract_scaling(h5, "template", base_loc)
- ret["temp_events"] = extract_1d_event_data(
- h5, "template", base_loc, tscale, tscale_sd, tshift, tdrift)
- except:
- pass
-
- try:
- cscale, cscale_sd, cshift, cdrift = extract_scaling(h5, "complement", base_loc)
- ret["comp_events"] = extract_1d_event_data(
- h5, "complement", base_loc, cscale, cscale_sd, cshift, cdrift)
- except Exception as e:
- pass
-
- try:
- al = h5["Analyses/Basecall_2D_000/BaseCalled_2D/Alignment"]
- temp_events = h5[base_loc+"/BaseCalled_template/Events"]
- comp_events = h5[base_loc+"/BaseCalled_complement/Events"]
- ret["2d_events"] = []
- for a in al:
- ev = []
- if a[0] == -1:
- ev += [0, 0, 0, 0, 0]
- else:
- e = temp_events[a[0]]
- mean = (e["mean"] - tshift) / cscale
- stdv = e["stdv"] / tscale_sd
- length = e["length"]
- ev += [1] + preproc_event(mean, stdv, length)
- if a[1] == -1:
- ev += [0, 0, 0, 0, 0]
- else:
- e = comp_events[a[1]]
- mean = (e["mean"] - cshift) / cscale
- stdv = e["stdv"] / cscale_sd
- length = e["length"]
- ev += [1] + preproc_event(mean, stdv, length)
- ret["2d_events"].append(ev)
- ret["2d_events"] = np.array(ret["2d_events"], dtype=np.float32)
- except Exception as e:
- print e
- pass
-
- h5.close()
- return ret
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--template_net', type=str, default="nets_data/map6temp.npz")
-parser.add_argument('--complement_net', type=str, default="nets_data/map6comp.npz")
-parser.add_argument('--big_net', type=str, default="nets_data/map6-2d-big.npz")
-parser.add_argument('reads', type=str, nargs='*')
-parser.add_argument('--timing', action='store_true', default=False)
-parser.add_argument('--type', type=str, default="all", help="One of: template, complement, 2d, all, use comma to separate multiple options, eg.: template,complement")
-parser.add_argument('--output', type=str, default="output.fasta")
-parser.add_argument('--output_orig', action='store_true', default=False)
-parser.add_argument('--directory', type=str, default='', help="Directory where read files are stored")
-
-args = parser.parse_args()
-types = args.type.split(',')
-do_template = False
-do_complement = False
-do_2d = False
-
-if "all" in types or "template" in types:
- do_template = True
-if "all" in types or "complement" in types:
- do_complement = True
-if "all" in types or "2d" in types:
- do_2d = True
-
-assert do_template or do_complement or do_2d, "Nothing to do"
-assert len(args.reads) != 0 or len(args.directory) != 0, "Nothing to basecall"
-
-if do_template:
- print "loading template net"
- temp_net = RnnPredictor(args.template_net)
- print "done"
-if do_complement:
- print "loading complement net"
- comp_net = RnnPredictor(args.complement_net)
- print "done"
-if do_2d:
- print "loading 2D net"
- big_net = RnnPredictor(args.big_net)
- print "done"
-
-chars = "ACGT"
-mapping = {"A": 0, "C": 1, "G": 2, "T": 3, "N": 4}
-
-fo = open(args.output, "w")
-
-total_bases = [0, 0, 0]
-
-files = args.reads
-if len(args.directory):
- files += [os.path.join(args.directory, x) for x in os.listdir(args.directory)]
-
-for i, read in enumerate(files):
- basename = os.path.basename(read)
- try:
- data = load_read_data(read)
- except Exception as e:
- print "error at file", read
- print e
- continue
- if not data:
- continue
- print "\rcalling read %d/%d %s" % (i, len(files), read),
- sys.stdout.flush()
- if args.output_orig:
- try:
- if "called_template" in data:
- print >>fo, ">%s_template" % basename
- print >>fo, data["called_template"]
- if "called_complement" in data:
- print >>fo, ">%s_complement" % basename
- print >>fo, data["called_complement"]
- if "called_2d" in data:
- print >>fo, ">%s_2d" % basename
- print >>fo, data["called_2d"]
- except:
- pass
-
- temp_start = datetime.datetime.now()
- if do_template and "temp_events" in data:
- predict_and_write(data["temp_events"], temp_net, fo, "%s_template_rnn" % basename)
- temp_time = datetime.datetime.now() - temp_start
-
- comp_start = datetime.datetime.now()
- if do_complement and "comp_events" in data:
- predict_and_write(data["comp_events"], comp_net, fo, "%s_complement_rnn" % basename)
- comp_time = datetime.datetime.now() - comp_start
-
- start_2d = datetime.datetime.now()
- if do_2d and "2d_events" in data:
- predict_and_write(data["2d_events"], big_net, fo, "%s_2d_rnn" % basename)
- time_2d = datetime.datetime.now() - start_2d
-
- if args.timing:
- try:
- print "Events: %d/%d" % (len(data["temp_events"]), len(data["comp_events"]))
- print "Our times: %f/%f/%f" % (temp_time.total_seconds(), comp_time.total_seconds(),
- time_2d.total_seconds())
- print "Our times per base: %f/%f/%f" % (
- temp_time.total_seconds() / len(data["temp_events"]),
- comp_time.total_seconds() / len(data["comp_events"]),
- time_2d.total_seconds() / (len(data["comp_events"]) + len(data["temp_events"])))
- print "Their times: %f/%f/%f" % (data["temp_time"].total_seconds(), data["comp_time"].total_seconds(), data["2d_time"].total_seconds())
- print "Their times per base: %f/%f/%f" % (
- data["temp_time"].total_seconds() / len(data["temp_events"]),
- data["comp_time"].total_seconds() / len(data["comp_events"]),
- data["2d_time"].total_seconds() / (len(data["comp_events"]) + len(data["temp_events"])))
- except:
- # Don't let timing throw us out
- pass
- fo.flush()
-fo.close()
diff --git a/debian/deepnano/usr/share/deepnano/basecall_no_metrichor.py b/debian/deepnano/usr/share/deepnano/basecall_no_metrichor.py
deleted file mode 100755
index 50b8dbc..0000000
--- a/debian/deepnano/usr/share/deepnano/basecall_no_metrichor.py
+++ /dev/null
@@ -1,277 +0,0 @@
-#!/usr/bin/python
-import argparse
-from rnn_fin import RnnPredictor
-import h5py
-import sys
-import numpy as np
-import theano as th
-import os
-import re
-import dateutil.parser
-import datetime
-from helpers import *
-import subprocess
-import time
-
-def get_scaling_template(events, has_std):
- down = 48.4631279889
- up = 65.7312554591
- our_down = np.percentile(events["mean"], 10)
- our_up = np.percentile(events["mean"], 90)
- scale = (our_up - our_down) / (up - down)
- shift = (our_up / scale - up) * scale
-
- sd = 0.807981325017
- if has_std:
- return scale, np.percentile(events["stdv"], 50) / sd, shift
- else:
- return scale, np.sqrt(np.percentile(events["variance"], 50)) / sd, shift
-
-
-def get_scaling_complement(events, has_std):
- down = 49.2638926877
- up = 69.0192568072
- our_down = np.percentile(events["mean"], 10)
- our_up = np.percentile(events["mean"], 90)
- scale = (our_up - our_down) / (up - down)
- shift = (our_up / scale - up) * scale
-
- sd = 1.04324844612
- if has_std:
- return scale, np.percentile(events["stdv"], 50) / sd, shift
- else:
- return scale, np.sqrt(np.percentile(events["variance"], 50)) / sd, shift
-
-def template_complement_loc(events):
- abasic_level = np.percentile(events["mean"], 99) + 5
- abasic_locs = (events["mean"] > abasic_level).nonzero()[0]
- last = -47
- run_len = 1
- runs = []
- for x in abasic_locs:
- if x - last == 1:
- run_len += 1
- else:
- if run_len >= 5:
- if len(runs) and last - runs[-1][0] < 50:
- run_len = last - runs[-1][0]
- run_len += runs[-1][1]
- runs[-1] = (last, run_len)
- else:
- runs.append((last, run_len))
- run_len = 1
- last = x
- to_sort = []
- mid = len(events) / 2
- low_third = len(events) / 3
- high_third = len(events) / 3 * 2
- for r in runs:
- if r[0] < low_third:
- continue
- if r[0] > high_third:
- continue
- to_sort.append((abs(r[0] - mid), r[0] - r[1], r[0]))
- to_sort.sort()
- if len(to_sort) == 0:
- return None
- trim_size = 10
- return {"temp": (trim_size, to_sort[0][1] - trim_size),
- "comp": (to_sort[0][2] + trim_size, len(events) - trim_size)}
-
-def load_read_data(read_file):
- h5 = h5py.File(read_file, "r")
- ret = {}
-
- read_key = h5["Analyses/EventDetection_000/Reads"].keys()[0]
- base_events = h5["Analyses/EventDetection_000/Reads"][read_key]["Events"]
- temp_comp_loc = template_complement_loc(base_events)
- sampling_rate = h5["UniqueGlobalKey/channel_id"].attrs["sampling_rate"]
-
- if temp_comp_loc:
- events = base_events[temp_comp_loc["temp"][0]:temp_comp_loc["temp"][1]]
- else:
- events = base_events
- has_std = True
- try:
- std = events[0]["stdv"]
- except:
- has_std = False
- tscale2, tscale_sd2, tshift2 = get_scaling_template(events, has_std)
-
- index = 0.0
- ret["temp_events2"] = []
- for e in events:
- mean = (e["mean"] - tshift2) / tscale2
- if has_std:
- stdv = e["stdv"] / tscale_sd2
- else:
- stdv = np.sqrt(e["variance"]) / tscale_sd2
- length = e["length"] / sampling_rate
- ret["temp_events2"].append(preproc_event(mean, stdv, length))
-
- ret["temp_events2"] = np.array(ret["temp_events2"], dtype=np.float32)
-
- if not temp_comp_loc:
- return ret
-
- events = base_events[temp_comp_loc["comp"][0]:temp_comp_loc["comp"][1]]
- cscale2, cscale_sd2, cshift2 = get_scaling_complement(events, has_std)
-
- index = 0.0
- ret["comp_events2"] = []
- for e in events:
- mean = (e["mean"] - cshift2) / cscale2
- if has_std:
- stdv = e["stdv"] / cscale_sd2
- else:
- stdv = np.sqrt(e["variance"]) / cscale_sd2
- length = e["length"] / sampling_rate
- ret["comp_events2"].append(preproc_event(mean, stdv, length))
-
- ret["comp_events2"] = np.array(ret["comp_events2"], dtype=np.float32)
-
- return ret
-
-def basecall(read_file_name, fo):
- basename = os.path.basename(read_file_name)
- try:
- data = load_read_data(read_file_name)
- except Exception as e:
- print e
- print "error at file", read_file_name
- return
-
- if do_template or do_2d:
- o1, o2 = predict_and_write(
- data["temp_events2"], temp_net,
- fo if do_template else None,
- "%s_template_rnn" % basename)
-
- if (do_complement or do_2d) and "comp_events2" in data:
- o1c, o2c = predict_and_write(
- data["comp_events2"], comp_net,
- fo if do_complement else None,
- "%s_complement_rnn" % basename)
-
- if do_2d and "comp_events2" in data and\
- len(data["comp_events2"]) <= args.max_2d_length and\
- len(data["temp_events2"]) <= args.max_2d_length:
- p = subprocess.Popen("/usr/lib/deepnano/align_2d", stdin=subprocess.PIPE, stdout=subprocess.PIPE)
- f2d = p.stdin
- print >>f2d, len(o1)+len(o2)
- for a, b in zip(o1, o2):
- print >>f2d, " ".join(map(str, a))
- print >>f2d, " ".join(map(str, b))
- print >>f2d, len(o1c)+len(o2c)
- for a, b in zip(o1c, o2c):
- print >>f2d, " ".join(map(str, a))
- print >>f2d, " ".join(map(str, b))
- f2do, f2de = p.communicate()
- if p.returncode != 0:
- return
- lines = f2do.strip().split('\n')
- print >>fo, ">%s_2d_rnn_simple" % basename
- print >>fo, lines[0].strip()
- events_2d = []
- for l in lines[1:]:
- temp_ind, comp_ind = map(int, l.strip().split())
- e = []
- if temp_ind == -1:
- e += [0, 0, 0, 0, 0]
- else:
- e += [1] + list(data["temp_events2"][temp_ind])
- if comp_ind == -1:
- e += [0, 0, 0, 0, 0]
- else:
- e += [1] + list(data["comp_events2"][comp_ind])
- events_2d.append(e)
- events_2d = np.array(events_2d, dtype=np.float32)
- predict_and_write(events_2d, big_net, fo, "%s_2d_rnn" % basename)
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--template_net', type=str, default="nets_data/map6temp.npz")
-parser.add_argument('--complement_net', type=str, default="nets_data/map6comp.npz")
-parser.add_argument('--big_net', type=str, default="nets_data/map6-2d-no-metr23.npz")
-parser.add_argument('--max_2d_length', type=int, default=10000, help='Max length for 2d basecall')
-parser.add_argument('reads', type=str, nargs='*')
-parser.add_argument('--type', type=str, default="all", help="One of: template, complement, 2d, all, use comma to separate multiple options, eg.: template,complement")
-parser.add_argument('--output', type=str, default="output.fasta")
-parser.add_argument('--directory', type=str, default='', help="Directory where read files are stored")
-parser.add_argument('--watch', type=str, default='', help='Watched directory')
-
-
-args = parser.parse_args()
-types = args.type.split(',')
-do_template = False
-do_complement = False
-do_2d = False
-
-if "all" in types or "template" in types:
- do_template = True
-if "all" in types or "complement" in types:
- do_complement = True
-if "all" in types or "2d" in types:
- do_2d = True
-
-assert do_template or do_complement or do_2d, "Nothing to do"
-assert len(args.reads) != 0 or len(args.directory) != 0 or len(args.watch) != 0, "Nothing to basecall"
-
-if do_template or do_2d:
- print "loading template net"
- temp_net = RnnPredictor(args.template_net)
- print "done"
-if do_complement or do_2d:
- print "loading complement net"
- comp_net = RnnPredictor(args.complement_net)
- print "done"
-if do_2d:
- print "loading 2D net"
- big_net = RnnPredictor(args.big_net)
- print "done"
-
-chars = "ACGT"
-mapping = {"A": 0, "C": 1, "G": 2, "T": 3, "N": 4}
-
-if len(args.reads) or len(args.directory) != 0:
- fo = open(args.output, "w")
-
- files = args.reads
- if len(args.directory):
- files += [os.path.join(args.directory, x) for x in os.listdir(args.directory)]
-
- for i, read in enumerate(files):
- basecall(read, fo)
-
- fo.close()
-
-if len(args.watch) != 0:
- try:
- from watchdog.observers import Observer
- from watchdog.events import PatternMatchingEventHandler
- except:
- print "Please install watchdog to watch directories"
- sys.exit()
-
- class Fast5Handler(PatternMatchingEventHandler):
- """Class for handling creation fo fast5-files"""
- patterns = ["*.fast5"]
- def on_created(self, event):
- print "Calling", event
- file_name = str(os.path.basename(event.src_path))
- fasta_file_name = os.path.splitext(event.src_path)[0] + '.fasta'
- with open(fasta_file_name, "w") as fo:
- basecall(event.src_path, fo)
- print('Watch dir: ' + args.watch)
- observer = Observer()
- print('Starting Observerer')
- # start watching directory for fast5-files
- observer.start()
- observer.schedule(Fast5Handler(), path=args.watch)
- try:
- while True:
- time.sleep(1)
- # quit script using ctrl+c
- except KeyboardInterrupt:
- observer.stop()
-
- observer.join()
diff --git a/debian/deepnano/usr/share/deepnano/basecall_no_metrichor_devel.py b/debian/deepnano/usr/share/deepnano/basecall_no_metrichor_devel.py
deleted file mode 100644
index 488fee3..0000000
--- a/debian/deepnano/usr/share/deepnano/basecall_no_metrichor_devel.py
+++ /dev/null
@@ -1,371 +0,0 @@
-import argparse
-from rnn_fin import RnnPredictor
-import h5py
-import sys
-import numpy as np
-import theano as th
-import os
-import re
-import dateutil.parser
-import datetime
-
-def preproc_event(mean, std, length):
- mean = mean / 100.0 - 0.66
- std = std - 1
- return [mean, mean*mean, std, length]
-
-def get_scaling_template(events):
- down = 48.4631279889
- up = 65.7312554591
- our_down = np.percentile(events["mean"], 10)
- our_up = np.percentile(events["mean"], 90)
- scale = (our_up - our_down) / (up - down)
- shift = (our_up / scale - up) * scale
-
- sd = 0.807981325017
- return scale, np.percentile(events["stdv"], 50) / sd, shift
-
-def get_scaling_complement(events):
- down = 49.2638926877
- up = 69.0192568072
- our_down = np.percentile(events["mean"], 10)
- our_up = np.percentile(events["mean"], 90)
- scale = (our_up - our_down) / (up - down)
- shift = (our_up / scale - up) * scale
-
- sd = 1.04324844612
- return scale, np.percentile(events["stdv"], 50) / sd, shift
-
-def template_complement_loc(events):
- abasic_level = np.percentile(events["mean"], 99) + 5
- abasic_locs = (events["mean"] > abasic_level).nonzero()[0]
- last = -47
- run_len = 1
- runs = []
- for x in abasic_locs:
- if x - last == 1:
- run_len += 1
- else:
- if run_len >= 5:
- if len(runs) and last - runs[-1][0] < 50:
- run_len = last - runs[-1][0]
- run_len += runs[-1][1]
- runs[-1] = (last, run_len)
- else:
- runs.append((last, run_len))
- run_len = 1
- last = x
- to_sort = []
- mid = len(events) / 2
- low_third = len(events) / 3
- high_third = len(events) / 3 * 2
- for r in runs:
- if r[0] < low_third:
- continue
- if r[0] > high_third:
- continue
- to_sort.append((abs(r[0] - mid), r[0] - r[1], r[0]))
- to_sort.sort()
- if len(to_sort) == 0:
- return None
- trim_size = 10
- return {"temp": (trim_size, to_sort[0][1] - trim_size),
- "comp": (to_sort[0][2] + trim_size, len(events) - trim_size)}
-
-def load_read_data(read_file):
- h5 = h5py.File(read_file, "r")
- ret = {}
-
- read_key = h5["Analyses/EventDetection_000/Reads"].keys()[0]
- base_events = h5["Analyses/EventDetection_000/Reads"][read_key]["Events"]
- temp_comp_loc = template_complement_loc(base_events)
- if not temp_comp_loc:
- return None
-
-# print "temp_comp_loc", temp_comp_loc["temp"], temp_comp_loc["comp"]
-# print h5["Analyses/Basecall_2D_000/Summary/split_hairpin"].attrs["start_index_temp"],
-# print h5["Analyses/Basecall_2D_000/Summary/split_hairpin"].attrs["end_index_temp"],
-# print h5["Analyses/Basecall_2D_000/Summary/split_hairpin"].attrs["start_index_comp"],
-# print h5["Analyses/Basecall_2D_000/Summary/split_hairpin"].attrs["end_index_comp"]
-
- sampling_rate = h5["UniqueGlobalKey/channel_id"].attrs["sampling_rate"]
-
- try:
- ret["called_template"] = h5["Analyses/Basecall_2D_000/BaseCalled_template/Fastq"][()].split('\n')[1]
- ret["called_complement"] = h5["Analyses/Basecall_2D_000/BaseCalled_complement/Fastq"][()].split('\n')[1]
- ret["called_2d"] = h5["Analyses/Basecall_2D_000/BaseCalled_2D/Fastq"][()].split('\n')[1]
- except Exception as e:
- print "wat", e
- return None
- events = base_events[temp_comp_loc["temp"][0]:temp_comp_loc["temp"][1]]
- tscale2, tscale_sd2, tshift2 = get_scaling_template(events)
-
- index = 0.0
- ret["temp_events2"] = []
- for e in events:
- mean = (e["mean"] - tshift2) / tscale2
- stdv = e["stdv"] / tscale_sd2
- length = e["length"] / sampling_rate
- ret["temp_events2"].append(preproc_event(mean, stdv, length))
- events = h5["Analyses/Basecall_2D_000/BaseCalled_template/Events"]
- tscale = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_template"].attrs["scale"]
- tscale_sd = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_template"].attrs["scale_sd"]
- tshift = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_template"].attrs["shift"]
- tdrift = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_template"].attrs["drift"]
- index = 0.0
- ret["temp_events"] = []
- for e in events:
- mean = (e["mean"] - tshift - index * tdrift) / tscale
- stdv = e["stdv"] / tscale_sd
- length = e["length"]
- ret["temp_events"].append(preproc_event(mean, stdv, length))
- index += e["length"]
-
- events = base_events[temp_comp_loc["comp"][0]:temp_comp_loc["comp"][1]]
- cscale2, cscale_sd2, cshift2 = get_scaling_complement(events)
-
- index = 0.0
- ret["comp_events2"] = []
- for e in events:
- mean = (e["mean"] - cshift2) / cscale2
- stdv = e["stdv"] / cscale_sd2
- length = e["length"] / sampling_rate
- ret["comp_events2"].append(preproc_event(mean, stdv, length))
-
- events = h5["Analyses/Basecall_2D_000/BaseCalled_complement/Events"]
- cscale = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_complement"].attrs["scale"]
- cscale_sd = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_complement"].attrs["scale_sd"]
- cshift = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_complement"].attrs["shift"]
- cdrift = h5["/Analyses/Basecall_2D_000/Summary/basecall_1d_complement"].attrs["drift"]
- index = 0.0
- ret["comp_events"] = []
- for e in events:
- mean = (e["mean"] - cshift - index * cdrift) / cscale
- stdv = e["stdv"] / cscale_sd
- length = e["length"]
- ret["comp_events"].append(preproc_event(mean, stdv, length))
- index += e["length"]
-
- ret["temp_events2"] = np.array(ret["temp_events2"], dtype=np.float32)
- ret["comp_events2"] = np.array(ret["comp_events2"], dtype=np.float32)
- ret["temp_events"] = np.array(ret["temp_events"], dtype=np.float32)
- ret["comp_events"] = np.array(ret["comp_events"], dtype=np.float32)
-
- al = h5["Analyses/Basecall_2D_000/BaseCalled_2D/Alignment"]
- ret["al"] = al
- temp_events = h5["Analyses/Basecall_2D_000/BaseCalled_template/Events"]
- comp_events = h5["Analyses/Basecall_2D_000/BaseCalled_complement/Events"]
- ret["2d_events"] = []
- for a in al:
- ev = []
- if a[0] == -1:
- ev += [0, 0, 0, 0, 0]
- else:
- e = temp_events[a[0]]
- mean = (e["mean"] - tshift - index * tdrift) / cscale
- stdv = e["stdv"] / tscale_sd
- length = e["length"]
- ev += [1] + preproc_event(mean, stdv, length)
- if a[1] == -1:
- ev += [0, 0, 0, 0, 0]
- else:
- e = comp_events[a[1]]
- mean = (e["mean"] - cshift - index * cdrift) / cscale
- stdv = e["stdv"] / cscale_sd
- length = e["length"]
- ev += [1] + preproc_event(mean, stdv, length)
- ret["2d_events"].append(ev)
- ret["2d_events"] = np.array(ret["2d_events"], dtype=np.float32)
- return ret
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--template_net', type=str, default="nets_data/map6temp.npz")
-parser.add_argument('--complement_net', type=str, default="nets_data/map6comp.npz")
-parser.add_argument('--big_net', type=str, default="nets_data/map6-2d-big.npz")
-parser.add_argument('reads', type=str, nargs='+')
-parser.add_argument('--type', type=str, default="all", help="One of: template, complement, 2d, all, use comma to separate multiple options, eg.: template,complement")
-parser.add_argument('--output', type=str, default="output.fasta")
-parser.add_argument('--output_orig', action='store_true', default=True)
-
-args = parser.parse_args()
-types = args.type.split(',')
-do_template = False
-do_complement = False
-do_2d = False
-
-if "all" in types or "template" in types:
- do_template = True
-if "all" in types or "complement" in types:
- do_complement = True
-if "all" in types or "2d" in types:
- do_2d = True
-
-assert do_template or do_complement or do_2d, "Nothing to do"
-
-if do_template or do_2d:
- print "loading template net"
- temp_net = RnnPredictor(args.template_net)
- print "done"
-if do_complement or do_2d:
- print "loading complement net"
- comp_net = RnnPredictor(args.complement_net)
- print "done"
-if do_2d:
- print "loading 2D net"
- big_net = RnnPredictor(args.big_net)
- big_net_orig = RnnPredictor("nets_data/map6-2d-big.npz")
- print "done"
-
-chars = "ACGT"
-mapping = {"A": 0, "C": 1, "G": 2, "T": 3, "N": 4}
-
-fo = open(args.output, "w")
-
-total_bases = [0, 0, 0]
-
-for i, read in enumerate(args.reads):
- if True:
- data = load_read_data(read)
-# except Exception as e:
-# print e
-# print "error at file", read
-# continue
- if not data:
- continue
- if args.output_orig:
- print >>fo, ">%d_template" % i
- print >>fo, data["called_template"]
- print >>fo, ">%d_complement" % i
- print >>fo, data["called_complement"]
- print >>fo, ">%d_2d" % i
- print >>fo, data["called_2d"]
-
- if do_template or do_2d:
- o1, o2 = temp_net.predict(data["temp_events"])
- o1m = (np.argmax(o1, 1))
- o2m = (np.argmax(o2, 1))
- print >>fo, ">%d_temp_rnn" % i
- for a, b in zip(o1m, o2m):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
- o1, o2 = temp_net.predict(data["temp_events2"])
- o1m = (np.argmax(o1, 1))
- o2m = (np.argmax(o2, 1))
- if do_template:
- print >>fo, ">%d_temp_rnn2" % i
- for a, b in zip(o1m, o2m):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
-
- if do_complement or do_2d:
- o1c, o2c = comp_net.predict(data["comp_events"])
- o1cm = (np.argmax(o1c, 1))
- o2cm = (np.argmax(o2c, 1))
- print >>fo, ">%d_comp_rnn" % i
- for a, b in zip(o1cm, o2cm):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
- o1c, o2c = comp_net.predict(data["comp_events2"])
- o1cm = (np.argmax(o1c, 1))
- o2cm = (np.argmax(o2c, 1))
- if do_complement:
- print >>fo, ">%d_comp_rnn2" % i
- for a, b in zip(o1cm, o2cm):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
-
- if do_2d:
- f2d = open("2d.in", "w")
- print >>f2d, len(o1)+len(o2)
- for a, b in zip(o1, o2):
- print >>f2d, " ".join(map(str, a))
- print >>f2d, " ".join(map(str, b))
- print >>f2d, len(o1c)+len(o2c)
- for a, b in zip(o1c, o2c):
- print >>f2d, " ".join(map(str, a))
- print >>f2d, " ".join(map(str, b))
- f2d.close()
- os.system("/usr/lib/deepnano/align_2d <2d.in >2d.out")
- f2do = open("2d.out")
- call2d = f2do.next().strip()
- print >>fo, ">%d_2d_rnn_simple" % i
- print >>fo, call2d
-
- start_temp_ours = None
- end_temp_ours = None
- start_comp_ours = None
- end_comp_ours = None
- events_2d = []
- for l in f2do:
- temp_ind, comp_ind = map(int, l.strip().split())
- e = []
- if temp_ind == -1:
- e += [0, 0, 0, 0, 0]
- else:
- e += [1] + list(data["temp_events2"][temp_ind])
- if not start_temp_ours:
- start_temp_ours = temp_ind
- end_temp_ours = temp_ind
- if comp_ind == -1:
- e += [0, 0, 0, 0, 0]
- else:
- e += [1] + list(data["comp_events2"][comp_ind])
- if not end_comp_ours:
- end_comp_ours = comp_ind
- start_comp_ours = comp_ind
- events_2d.append(e)
- events_2d = np.array(events_2d, dtype=np.float32)
- o1c, o2c = big_net.predict(events_2d)
- o1cm = (np.argmax(o1c, 1))
- o2cm = (np.argmax(o2c, 1))
- print >>fo, ">%d_2d_rnn2" % i
- for a, b in zip(o1cm, o2cm):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
- o1c, o2c = big_net.predict(data["2d_events"])
- o1cm = (np.argmax(o1c, 1))
- o2cm = (np.argmax(o2c, 1))
- print >>fo, ">%d_2d_rnn" % i
- for a, b in zip(o1cm, o2cm):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
-
- start_temp_th = None
- end_temp_th = None
- start_comp_th = None
- end_comp_th = None
- for a in data["al"]:
- if a[0] != -1:
- if not start_temp_th:
- start_temp_th = a[0]
- end_temp_th = a[0]
- if a[1] != -1:
- if not end_comp_th:
- end_comp_th = a[1]
- start_comp_th = a[1]
-
- print "Ours:",
- print start_temp_ours, end_temp_ours, start_comp_ours, end_comp_ours,
- print 1. * len(events_2d) / (end_temp_ours - start_temp_ours + end_comp_ours - start_comp_ours)
- print "Their:",
- print start_temp_th, end_temp_th, start_comp_th, end_comp_th,
- print 1. * len(data["al"]) / (end_temp_th - start_temp_th + end_comp_th - start_comp_th)
- print
diff --git a/debian/deepnano/usr/share/deepnano/helpers.py b/debian/deepnano/usr/share/deepnano/helpers.py
deleted file mode 100644
index 6808562..0000000
--- a/debian/deepnano/usr/share/deepnano/helpers.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from rnn_fin import RnnPredictor
-import h5py
-import sys
-import numpy as np
-import theano as th
-import os
-import re
-import dateutil.parser
-import datetime
-import argparse
-
-chars = "ACGT"
-mapping = {"A": 0, "C": 1, "G": 2, "T": 3, "N": 4}
-
-def preproc_event(mean, std, length):
- mean = mean / 100.0 - 0.66
- std = std - 1
- return [mean, mean*mean, std, length]
-
-def predict_and_write(events, ntwk, fo, read_name):
- o1, o2 = ntwk.predict(events)
- if fo:
- o1m = (np.argmax(o1, 1))
- o2m = (np.argmax(o2, 1))
- print >>fo, ">%s" % read_name
- for a, b in zip(o1m, o2m):
- if a < 4:
- fo.write(chars[a])
- if b < 4:
- fo.write(chars[b])
- fo.write('\n')
- return o1, o2
-
-def extract_timing(h5, ret):
- try:
- log = h5["Analyses/Basecall_2D_000/Log"][()]
- temp_time = dateutil.parser.parse(re.search(r"(.*) Basecalling template.*", log).groups()[0])
- comp_time = dateutil.parser.parse(re.search(r"(.*) Basecalling complement.*", log).groups()[0])
- comp_end_time = dateutil.parser.parse(re.search(r"(.*) Aligning hairpin.*", log).groups()[0])
-
- start_2d_time = dateutil.parser.parse(re.search(r"(.*) Performing full 2D.*", log).groups()[0])
- end_2d_time = dateutil.parser.parse(re.search(r"(.*) Workflow completed.*", log).groups()[0])
-
- ret["temp_time"] = comp_time - temp_time
- ret["comp_time"] = comp_end_time - comp_time
- ret["2d_time"] = end_2d_time - start_2d_time
- except:
- pass
-
-def get_base_loc(h5):
- base_loc = "Analyses/Basecall_2D_000"
- try:
- events = h5["Analyses/Basecall_2D_000/BaseCalled_template/Events"]
- except:
- base_loc = "Analyses/Basecall_1D_000"
- return base_loc
-
-def extract_scaling(h5, read_type, base_loc):
- scale = h5[base_loc+"/Summary/basecall_1d_"+read_type].attrs["scale"]
- scale_sd = h5[base_loc+"/Summary/basecall_1d_"+read_type].attrs["scale_sd"]
- shift = h5[base_loc+"/Summary/basecall_1d_"+read_type].attrs["shift"]
- drift = h5[base_loc+"/Summary/basecall_1d_"+read_type].attrs["drift"]
- return scale, scale_sd, shift, drift
-
-def extract_1d_event_data(h5, read_type, base_loc, scale, scale_sd, shift, drift):
- events = h5[base_loc+"/BaseCalled_%s/Events" % read_type]
- index = 0.0
- data = []
- for e in events:
- mean = (e["mean"] - shift - index * drift) / scale
- stdv = e["stdv"] / scale_sd
- length = e["length"]
- data.append(preproc_event(mean, stdv, length))
- index += e["length"]
- return np.array(data, dtype=np.float32)
-
diff --git a/debian/deepnano/usr/share/deepnano/rnn_fin.py b/debian/deepnano/usr/share/deepnano/rnn_fin.py
deleted file mode 100644
index a1795e8..0000000
--- a/debian/deepnano/usr/share/deepnano/rnn_fin.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import theano as th
-import theano.tensor as T
-from theano.tensor.nnet import sigmoid
-import numpy as np
-import pickle
-
-def share(array, dtype=th.config.floatX, name=None):
- return th.shared(value=np.asarray(array, dtype=dtype), name=name)
-
-class OutLayer:
- def __init__(self, input, in_size, n_classes):
- w = share(np.zeros((in_size, n_classes)))
- b = share(np.zeros(n_classes))
- eps = 0.0000001
- self.output = T.clip(T.nnet.softmax(T.dot(input, w) + b), eps, 1-eps)
- self.params = [w, b]
-
-class SimpleLayer:
- def __init__(self, input, nin, nunits):
- id = str(np.random.randint(0, 10000000))
- wio = share(np.zeros((nin, nunits)), name="wio"+id) # input to output
- wir = share(np.zeros((nin, nunits)), name="wir"+id) # input to output
- wiu = share(np.zeros((nin, nunits)), name="wiu"+id) # input to output
- woo = share(np.zeros((nunits, nunits)), name="woo"+id) # output to output
- wou = share(np.zeros((nunits, nunits)), name="wou"+id) # output to output
- wor = share(np.zeros((nunits, nunits)), name="wor"+id) # output to output
- bo = share(np.zeros(nunits), name="bo"+id)
- bu = share(np.zeros(nunits), name="bu"+id)
- br = share(np.zeros(nunits), name="br"+id)
- h0 = share(np.zeros(nunits), name="h0"+id)
-
- def step(in_t, out_tm1):
- update_gate = sigmoid(T.dot(out_tm1, wou) + T.dot(in_t, wiu) + bu)
- reset_gate = sigmoid(T.dot(out_tm1, wor) + T.dot(in_t, wir) + br)
- new_val = T.tanh(T.dot(in_t, wio) + reset_gate * T.dot(out_tm1, woo) + bo)
- return update_gate * out_tm1 + (1 - update_gate) * new_val
-
- self.output, _ = th.scan(
- step, sequences=[input],
- outputs_info=[h0])
-
- self.params = [wio, woo, bo, wir, wiu, wor, wou, br, bu, h0]
-
-class BiSimpleLayer():
- def __init__(self, input, nin, nunits):
- fwd = SimpleLayer(input, nin, nunits)
- bwd = SimpleLayer(input[::-1], nin, nunits)
- self.params = fwd.params + bwd.params
- self.output = T.concatenate([fwd.output, bwd.output[::-1]], axis=1)
-
-class RnnPredictor:
- def __init__(self, filename):
- package = np.load(filename)
- assert(len(package.files) % 20 == 4)
- n_layers = len(package.files) / 20
-
- self.input = T.fmatrix()
- last_output = self.input
- last_size = package['arr_0'].shape[0]
- hidden_size = package['arr_0'].shape[1]
- par_index = 0
- for i in range(n_layers):
- layer = BiSimpleLayer(last_output, last_size, hidden_size)
- for i in range(20):
- layer.params[i].set_value(package['arr_%d' % par_index])
- par_index += 1
-
- last_output = layer.output
- last_size = 2*hidden_size
- out_layer1 = OutLayer(last_output, last_size, 5)
- for i in range(2):
- out_layer1.params[i].set_value(package['arr_%d' % par_index])
- par_index += 1
- out_layer2 = OutLayer(last_output, last_size, 5)
- for i in range(2):
- out_layer2.params[i].set_value(package['arr_%d' % par_index])
- par_index += 1
- output1 = out_layer1.output
- output2 = out_layer2.output
-
- self.predict = th.function(inputs=[self.input], outputs=[output1, output2])
diff --git a/debian/deepnano/usr/share/doc/deepnano/changelog.Debian.gz b/debian/deepnano/usr/share/doc/deepnano/changelog.Debian.gz
deleted file mode 100644
index e9af2e1..0000000
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diff --git a/debian/deepnano/usr/share/doc/deepnano/copyright b/debian/deepnano/usr/share/doc/deepnano/copyright
deleted file mode 100644
index 573e566..0000000
--- a/debian/deepnano/usr/share/doc/deepnano/copyright
+++ /dev/null
@@ -1,36 +0,0 @@
-Format: https://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
-Upstream-Name: DeepNano
-Source: https://bitbucket.org/vboza/deepnano
-Files-Excluded: training/realign
-
-Files: *
-Copyright: 2016, Vladimir Boza, Comenius University
-License: BSD-3-clause
-
-Files: debian/*
-Copyright: 2016 Andreas Tille <tille at debian.org>
-License: BSD-3-clause
-
-License: BSD-3-clause
- 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 the Comenius University nor the
- names of its contributors may be used to endorse or promote products
- derived from this software without specific prior written permission.
- .
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND 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 COMENIUS UNIVERSITY 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.
-
diff --git a/debian/deepnano/usr/share/doc/deepnano/examples/nets_data/map5-2d.npz.gz b/debian/deepnano/usr/share/doc/deepnano/examples/nets_data/map5-2d.npz.gz
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diff --git a/debian/deepnano/usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch13_read1132_strand.fast5.gz b/debian/deepnano/usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch13_read1132_strand.fast5.gz
deleted file mode 100644
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diff --git a/debian/deepnano/usr/share/python/runtime.d/deepnano.rtupdate b/debian/deepnano/usr/share/python/runtime.d/deepnano.rtupdate
deleted file mode 100755
index 4563b9e..0000000
--- a/debian/deepnano/usr/share/python/runtime.d/deepnano.rtupdate
+++ /dev/null
@@ -1,7 +0,0 @@
-#! /bin/sh
-set -e
-
-if [ "$1" = rtupdate ]; then
- pyclean -p deepnano /usr/share/deepnano
- pycompile -p deepnano /usr/share/deepnano
-fi
\ No newline at end of file
--
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