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commit e0c504307e4bc5c8810438718360470c656b891d Author: Andreas Tille <[email protected]> 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 <[email protected]> -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 Binary files a/debian/deepnano/usr/share/doc/deepnano/changelog.Debian.gz and /dev/null differ 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 <[email protected]> -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 deleted file mode 100644 index d08f7f0..0000000 Binary files a/debian/deepnano/usr/share/doc/deepnano/examples/nets_data/map5-2d.npz.gz and /dev/null differ diff --git a/debian/deepnano/usr/share/doc/deepnano/examples/nets_data/map5comp.npz.gz b/debian/deepnano/usr/share/doc/deepnano/examples/nets_data/map5comp.npz.gz deleted 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a/debian/deepnano/usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch13_read1130_strand.fast5.gz and /dev/null differ 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 index 699f576..0000000 Binary files a/debian/deepnano/usr/share/doc/deepnano/examples/test_data/2016_3_4_3507_1_ch13_read1132_strand.fast5.gz and /dev/null differ 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 -- Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-med/deepnano.git _______________________________________________ debian-med-commit mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/debian-med-commit
