From 53fbb7c6175e6a282dad3e0dc2456ca75cea7b6c Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 3 May 2019 17:53:49 +0200 Subject: [PATCH 01/96] update logo --- README.md | 2 +- assets/img/coproID_nf-core_logo.svg | 30 ++++++++++++++--------------- docs/README.md | 2 +- docs/introduction.md | 4 ++-- 4 files changed, 19 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 78a0cce..acd6d7b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) -# ![logo_coproid](assets/img/coproid_logo_small.jpg) ![logo_nf_core](assets/img/coproID_nf-core_logo_small.png) +# ![logo_coproid](assets/img/coproid_logo_small.jpg) ![logo_nf_core](assets/img/coproID_nf-core_logo.svg) ## Documentation diff --git a/assets/img/coproID_nf-core_logo.svg b/assets/img/coproID_nf-core_logo.svg index cedf837..4eb0845 100644 --- a/assets/img/coproID_nf-core_logo.svg +++ b/assets/img/coproID_nf-core_logo.svg @@ -12,8 +12,8 @@ viewBox="0 0 1456.7841 522.44342" xml:space="preserve" id="svg2" - inkscape:version="0.92.2 5c3e80d, 2017-08-06" - sodipodi:docname="coproID_nf-core_logo.svg" + inkscape:version="0.91 r13725" + sodipodi:docname="EmptyName_logo.svg" width="1456.7842" height="522.44342">coproID \ No newline at end of file + style="fill:url(#f)" /> \ No newline at end of file diff --git a/docs/README.md b/docs/README.md index 4a6ae1c..a351aec 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,6 +1,6 @@ # nf-core/coproid: Documentation -![logo](source/_static/img/coproid_logo_small.jpg) ![nf-core-logo](../assets/img/coproID_nf-core_logo_small.png) +![nf-core-logo](../assets/img/coproID_nf-core_logo.svg) **coproID** (**CO**prolite **ID**entification) is a tool developed at the [Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) diff --git a/docs/introduction.md b/docs/introduction.md index be2e958..45624fe 100644 --- a/docs/introduction.md +++ b/docs/introduction.md @@ -1,6 +1,6 @@ # Introduction -![logo](../assets/img/coproid_logo_small.jpg) ![nf-core-logo](../assets/img/coproID_nf-core_logo_small.png) +![](../assets/img/coproid_logo_small.jpg) ![](../assets/img/coproID_nf-core_logo.svg) **coproID** (**CO**prolite **ID**entification) is a tool developed at the [Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) @@ -20,7 +20,7 @@ Example: ## coproID example workFlow -![dag](../assets/img/coproid_dag.png) +![](../assets/img/coproid_dag.png) ## How to cite coproID From 918651757ed8808c2be3fd8bc736945f6da666e4 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 3 May 2019 17:55:26 +0200 Subject: [PATCH 02/96] remove old logo --- README.md | 2 +- docs/introduction.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index acd6d7b..d3ecd2f 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) -# ![logo_coproid](assets/img/coproid_logo_small.jpg) ![logo_nf_core](assets/img/coproID_nf-core_logo.svg) +![logo_nf_core](assets/img/coproID_nf-core_logo.svg) ## Documentation diff --git a/docs/introduction.md b/docs/introduction.md index 45624fe..175b818 100644 --- a/docs/introduction.md +++ b/docs/introduction.md @@ -1,6 +1,6 @@ # Introduction -![](../assets/img/coproid_logo_small.jpg) ![](../assets/img/coproID_nf-core_logo.svg) +![](../assets/img/coproID_nf-core_logo.svg) **coproID** (**CO**prolite **ID**entification) is a tool developed at the [Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) From f0dc714f66cb435942bebc883528349e8674ef08 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 3 May 2019 18:00:46 +0200 Subject: [PATCH 03/96] update changelog --- CHANGELOG.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index f7ef924..698246f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # nf-core/coproid: Changelog +## v1.0.1dev + +Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) + ## v1.0 - 2019-04-26 Initial release of nf-core/coproid, created with the [nf-core](http://nf-co.re/) template. From 8c9ea2570154b9d7772da2dce5393e8fb598e47f Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:01:27 +0200 Subject: [PATCH 04/96] remove outdated scripts --- bin/plotAndReport | 239 --------------------------------- bin/plotAndReport2 | 323 --------------------------------------------- 2 files changed, 562 deletions(-) delete mode 100755 bin/plotAndReport delete mode 100755 bin/plotAndReport2 diff --git a/bin/plotAndReport b/bin/plotAndReport deleted file mode 100755 index d363e3a..0000000 --- a/bin/plotAndReport +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import sys -import datetime -import numpy as np -import matplotlib.pyplot as plt -plt.switch_backend('agg') - - -def get_args(): - '''This function parses and return arguments passed in''' - parser = argparse.ArgumentParser( - prog='plotAndReport', - description='plotAndReport') - parser.add_argument( - '-c', - dest='countFile', - default=None, - help="Normalized read count file") - parser.add_argument( - '-i', - dest='identity', - default=0.97, - help='Identity threshold to retain read alignment' - ) - parser.add_argument( - '-v', - dest="version", - default=None, - help='coproID version' - ) - parser.add_argument( - "-csv", - dest="csv", - default=None, - help="csv filename output. Default = coproid.csv" - ) - parser.add_argument( - "-adna", - dest="adna", - default='true', - help="adna (true | false)" - ) - parser.add_argument( - '-o', - dest="output", - default="coproID_result.md", - help="Output file basename. Default = coproID_result.md") - - args = parser.parse_args() - - countFile = args.countFile - identity = float(str(args.identity)) - version = str(args.version) - adna = str(args.adna) - outfile = args.output - csv = args.csv - - return(countFile, identity, version, adna, outfile, csv) - - -def getBasename(file_name): - if ("/") in file_name: - basename = file_name.split("/")[-1].split(".")[0] - else: - basename = file_name.split(".")[0] - return(basename) - - -def getFile(path): - if ("/") in path: - filename = path.split("/")[-1] - else: - filename = path - return(filename) - - -def write_csv(adict, CSV): - with open(CSV, "w") as f: - f.write("Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,nb_aDNA_bp_aligned_genome1,nb_aDNA_bp_aligned_genome2,NormalizedReadRatio\n") - for akey in adict: - f.write(akey + "," + adict[akey]["orga1"] + "," + adict[akey] - ["orga2"] + "," + str(adict[akey]["gs1"]) + "," + str(adict[akey]["gs2"]) + "," + str(adict[akey]["nbp1"]) + "," + str(adict[akey]["nbp2"]) + "," + str(adict[akey]["nrr"]) + "\n") - - -if __name__ == "__main__": - CF, ID, VERSION, ADNA, OUTFILE, CSV = get_args() - - if ADNA == 'true': - ADNA = True - elif ADNA == 'false': - ADNA = False - - identity = ID * 100 - - if CF is None: - print("Count file is missing") - sys.exit(1) - - # Template output file structure - # Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,NormalizedReadRatio - - d = {} - d2 = {} - all_orga = [] - with open(CF, "r") as f: - for line in f: - line = line.rstrip().split(",") - sample = line[0] - organism1 = line[1] - organism2 = line[2] - gs1 = int(line[3]) - gs2 = int(line[4]) - nbp1 = int(line[5]) - nbp2 = int(line[6]) - nrr = float(line[7]) - if sample not in d.keys(): - d[sample] = {} - d[sample]["orga1"] = organism1 - d[sample]["orga2"] = organism2 - d[sample]["gs1"] = gs1 - d[sample]["gs2"] = gs2 - d[sample]["nbp1"] = nbp1 - d[sample]["nbp2"] = nbp2 - d[sample]["nrr"] = nrr - - write_csv(d, CSV) - - orga1_clean = organism1.replace("_", " ") - orga2_clean = organism2.replace("_", " ") - - y = [d[i]["nrr"] for i in d.keys()] - x = list(d.keys()) - - maxratio = max(y) - minratio = min(y) - if minratio == maxratio and minratio > 0: - minratio = -1.2 - maxratio = maxratio - elif minratio == maxratio and minratio < 0: - minratio = minratio - maxratio = 1.2 - elif maxratio < 0 and minratio < 0: - maxratio = 1.2 - elif minratio > 0 and maxratio > 0: - minratio = -1.2 - - step = round((maxratio - minratio) / 10, 1) - - ticklist = [orga2_clean] + [str(round(x, 1)) for x in list( - np.arange(minratio, maxratio + step, step))] + [orga1_clean] - - # plt.plot(x, y, "ro") - plt.bar(x, y) - for a, b in zip(x, y): - if (b < 0): - fac = -1 - else: - fac = 0.05 - plt.text(a, b + fac, str(round(b, 1)), - color="red", ha="center", rotation=45) - - # plt.grid(True) - - plt.axhline(y=0, color="black") - plt.axhline(y=1, color="orange") - plt.axhline(y=-1, color="orange") - - if minratio < 0: - plt.yticks((np.arange(minratio - step, maxratio + 2 * step, step)), - (ticklist)) - else: - plt.yticks((np.arange(-(2 * step), maxratio + 2 * step, step)), - (ticklist)) - plt.xticks(rotation='45', ha="right", rotation_mode="anchor") - plt.yticks(rotation='45') - plt.subplots_adjust(bottom=0.4, left=0.19) - - plt.savefig("./ratio.png") - - now = datetime.datetime.now() - with open(OUTFILE, "w") as fw: - fw.write("---\ntitle: coproID - Coprolite Identification\n---\n ") - fw.write( - '\n\n') - fw.write( - '**Homepage/Documentation**: [github.com/maxibor/coproID](https://github.com/maxibor/coproID) \n**Author:** Maxime Borry [borry@shh.mpg.de](mailto:borry@shh.mpg.de)\n\n ') - fw.write("Report automatically generated by coproID version " + VERSION + " on " + str(now.year) + "-" + str(now.month) + "-" + - str(now.day) + " " + str(now.hour) + ":" + str(now.minute) + ":" + str(now.second) + " \n") - fw.write("\n\n") - fw.write("***\n") - fw.write("- **Organisms:** \n\n - *" + orga1_clean + "* (genome size = " + str(gs1) + - " bp ) \n\n - *" + orga2_clean + "* (genome size = " + str(gs2) + " bp) \n") - fw.write( - "- **The formula used to compute the read ratio is the following:** \n\n $NormalizedRead_{Ratio} = \\log2\\left(\\frac{\\frac{N_{\\ aDNA\\ bp \\ aligned \\ genome1}}{size_{genome2} }}{\\frac{N_{ \\ aDNA \\ bp \\ aligned \\ genome2}}{size_{genome2}}}\\right)$\n\n ") - fw.write("***\n") - fw.write("## coproID read ratio plot\n\n") - fw.write("![Normalized read ratio (*" + orga1_clean + - "*/*" + orga2_clean + "*). The black line at 0 is an equal proportion of both genomes, while orange lines at |1| represent the uncertainty zone.](./ratio.png)\n") - fw.write("\n\n***\n\n") - fw.write("***\n\n") - for asample in d.keys(): - orga1_clean = d[asample]["orga1"].replace("_", " ") - orga2_clean = d[asample]["orga2"].replace("_", " ") - if d[asample]["nrr"] > 1: - conclusion = 'There are more *' + orga1_clean + '* aDNA reads than *' + orga2_clean + \ - '* by > 2 folds. Therefore this coprolite is most likely originating from *' + \ - orga1_clean + '*' - elif d[asample]["nrr"] < -1: - conclusion = 'There are more *' + orga2_clean + '* aDNA reads than *' + orga1_clean + \ - '* by > 2 folds. Therefore this coprolite is most likely originating from *' + \ - orga2_clean + '*' - else: - conclusion = 'There is a similar amount of aDNA reads originating from *' + \ - orga1_clean + '* and *' + orga2_clean + \ - '*. coproID can not reliably estimate which of these two organisms this coprolite is coming from.' - fw.write("### **Sample:** " + asample + " \n") - fw.write("- **Number of base pairs (bp) in reads aligned to *" + - orga1_clean + "* (genome 1) at " + str(identity) + "\% identity:** " + str(d[asample]["nbp1"]) + " \n") - fw.write("- **Number of base pairs (bp) in reads aligned to *" + - orga2_clean + "* (genome 2) at " + str(identity) + "\% identity:** " + str(d[asample]["nbp2"]) + " \n\n") - fw.write("- $NormalizedRead_{ratio} = \\log2\\left(\\frac{" + d[asample]["orga1"].replace("_", "\\;") + "}{" + - d[asample]["orga2"].replace("_", "\\;") + "}\\right) = " + str(round(d[asample]["nrr"], 3)) + "$ \n\n") - fw.write(conclusion + '\n\n') - fw.write("- **MapDamage plots**\n\n") - if d[asample]["nbp1"] > 20 and ADNA == True: - fw.write("![MapDamage Fragmisincorporation plot for " + - orga1_clean + ". Very jagged and irregular substitutions are likely indicating spurious damages caused by a lack of reads. ](./" + asample + "." + d[asample]["orga1"] + ".fragmisincorporation_plot.png)\n\n") - else: - fw.write( - "\n\nNo plot is displayed because the number of bases aligned is too low for mapDamage, or it is Modern DNA\n\n") - if d[asample]["nbp2"] > 20 and ADNA == True: - fw.write("![MapDamage Fragmisincorporation plot for " + - orga2_clean + ". Very jagged and irregular substitutions are likely indicating spurious damages caused by a lack of reads. If there is not plot displayed, the aligned read count was too low for mapDamage](./" + asample + "." + d[asample]["orga2"] + ".fragmisincorporation_plot.png)\n\n") - else: - fw.write( - "\n\nNo plot is displayed because the number of bases aligned is too low for mapDamage, or it is Modern DNA\n\n") - fw.write("\n\n***\n") diff --git a/bin/plotAndReport2 b/bin/plotAndReport2 deleted file mode 100755 index 3844bd7..0000000 --- a/bin/plotAndReport2 +++ /dev/null @@ -1,323 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import sys -import datetime -import numpy as np -import matplotlib.pyplot as plt -import pandas as pd -plt.switch_backend('agg') - - -def get_args(): - '''This function parses and return arguments passed in''' - parser = argparse.ArgumentParser( - prog='plotAndReport', - description='plotAndReport') - parser.add_argument( - '-c', - dest='countFile', - default=None, - help="Normalized read count file") - parser.add_argument( - '-i', - dest='identity', - default=0.97, - help='Identity threshold to retain read alignment' - ) - parser.add_argument( - '-v', - dest="version", - default=None, - help='coproID version' - ) - parser.add_argument( - "-csv", - dest="csv", - default=None, - help="csv filename output. Default = coproid.csv" - ) - parser.add_argument( - "-adna", - dest="adna", - default='true', - help="adna (true | false)" - ) - parser.add_argument( - '-o', - dest="output", - default="coproID_result.md", - help="Output file basename. Default = coproID_result.md") - - args = parser.parse_args() - - countFile = args.countFile - identity = float(str(args.identity)) - version = str(args.version) - adna = str(args.adna) - outfile = args.output - csv = args.csv - - return(countFile, identity, version, adna, outfile, csv) - - -def getBasename(file_name): - if ("/") in file_name: - basename = file_name.split("/")[-1].split(".")[0] - else: - basename = file_name.split(".")[0] - return(basename) - - -def getFile(path): - if ("/") in path: - filename = path.split("/")[-1] - else: - filename = path - return(filename) - - -def write_csv(adict, CSV, n_samp): - with open(CSV, "w") as f: - if n_samp == 2: - f.write("Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,sourcepredict_genome1,sourcepredict_genome2,sourcepredict_unknown,nb_bp_aligned_genome1,nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,NormalizedReadRatio_1,NormalizedReadRatio_2\n") - for akey in adict: - f.write(f"{akey},{adict[akey]['orga1']}, {adict[akey]['orga2']}, {adict[akey]['gs1']}, {dict[akey]['gs2']}, {adict[akey]['sourcepredict_g1']},{adict[akey]['sourcepredict_g2']},{adict[akey]['sourcepredict_unknown']}, {adict[akey]['nbp1']}, {adict[akey]['nbp2']}, {adict[akey]['nrr1']},{adict[akey]['nrr2']}\n") - else: - f.write("Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,sourcepredict_genome1,sourcepredict_genome2,sourcepredict_genome3,sourcepredict_unknown,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome3,NormalizedReadRatio_1,NormalizedReadRatio_2,NormalizedReadRatio_3\n") - for akey in adict: - f.write(f"{akey},{adict[akey]['orga1']},{adict[akey]['orga2']},{adict[akey]['orga3']},{adict[akey]['gs1']},{adict[akey]['gs2']},{adict[akey]['gs3']},{adict[akey]['sourcepredict_g1']},{adict[akey]['sourcepredict_g2']},{adict[akey]['sourcepredict_g3']},{adict[akey]['sourcepredict_unknown']},{adict[akey]['nbp1']},{adict[akey]['nbp2']},{adict[akey]['nbp3']},{adict[akey]['nnbp1']},{adict[akey]['nnbp2']},{adict[akey]['nnbp3']},{adict[akey]['nrr1']},{adict[akey]['nrr2']},{adict[akey]['nrr3']}\n") - - -def sample_text(sample_dict): - res = [] - if nrr3 not in asample.keys(): - orga1_clean = asample["orga1"].replace("_", " ") - orga2_clean = asample["orga2"].replace("_", " ") - else: - orga1_clean = asample["orga1"].replace("_", " ") - orga2_clean = asample["orga2"].replace("_", " ") - orga3_clean = asample["orga2"].replace("_", " ") - - -if __name__ == "__main__": - CF, ID, VERSION, ADNA, OUTFILE, CSV = get_args() - - if ADNA == 'true': - ADNA = True - elif ADNA == 'false': - ADNA = False - - identity = ID * 100 - - if CF is None: - print("Count file is missing") - sys.exit(1) - - # Template output file structure - # Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,sourcepredict_genome1,sourcepredict_genome2,sourcepredict_unknown,nb_bp_aligned_genome1,nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,NormalizedReadRatio - - d = {} - d2 = {} - all_orga = [] - with open(CF, "r") as f: - for line in f: - line = line.rstrip().split(",") - if len(line) <= 14: - nsamp = 2 - sample = line[0] - organism1 = line[1] - organism2 = line[2] - gs1 = int(line[3]) - gs2 = int(line[4]) - sourcepredict_g1 = float(line[5]) - sourcepredict_g2 = float(line[6]) - sourcepredict_unknown = float(line[7]) - nbp1 = int(line[8]) - nbp2 = int(line[9]) - nnbp1 = float(line[10]) - nnbp2 = float(line[11]) - nrr1 = float(line[12]) - nrr1 = float(line[13]) - if sample not in d.keys(): - d[sample] = {} - d[sample]["orga1"] = organism1 - d[sample]["orga2"] = organism2 - d[sample]["gs1"] = gs1 - d[sample]["gs2"] = gs2 - d[sample]["sourcepredict_g1"] = sourcepredict_g1 - d[sample]["sourcepredict_g2"] = sourcepredict_g2 - d[sample]["sourcepredict_unknown"] = sourcepredict_unknown - d[sample]["nbp1"] = nbp1 - d[sample]["nbp2"] = nbp2 - d[sample]["nnbp1"] = nnbp1 - d[sample]["nnbp2"] = nnbp2 - d[sample]["nrr1"] = nrr1 - d[sample]["nrr2"] = nrr2 - else: - # Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,sourcepredict_genome1,sourcepredict_genome2,sourcepredict_genome3,sourcepredict_unknown,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome3,NormalizedReadRatio_1vs2,NormalizedReadRatio_1vs3,NormalizedReadRatio_2vs3 - nsamp = 3 - sample = line[0] - organism1 = line[1] - organism2 = line[2] - organism3 = line[3] - gs1 = int(line[4]) - gs2 = int(line[5]) - gs3 = int(line[6]) - sourcepredict_g1 = float(line[7]) - sourcepredict_g2 = float(line[8]) - sourcepredict_g3 = float(line[9]) - sourcepredict_unknown = float(line[10]) - nbp1 = int(line[11]) - nbp2 = int(line[12]) - nbp3 = int(line[13]) - nnbp1 = float(line[14]) - nnbp2 = float(line[15]) - nnbp3 = float(line[16]) - nrr1 = float(line[17]) - nrr2 = float(line[18]) - nrr3 = float(line[19]) - if sample not in d.keys(): - d[sample] = {} - d[sample]["orga1"] = organism1 - d[sample]["orga2"] = organism2 - d[sample]["orga3"] = organism3 - d[sample]["gs1"] = gs1 - d[sample]["gs2"] = gs2 - d[sample]["gs3"] = gs3 - d[sample]["sourcepredict_g1"] = sourcepredict_g1 - d[sample]["sourcepredict_g2"] = sourcepredict_g2 - d[sample]["sourcepredict_g3"] = sourcepredict_g3 - d[sample]["sourcepredict_unknown"] = sourcepredict_unknown - d[sample]["nbp1"] = nbp1 - d[sample]["nbp2"] = nbp2 - d[sample]["nbp3"] = nbp3 - d[sample]["nnbp1"] = nnbp1 - d[sample]["nnbp2"] = nnbp2 - d[sample]["nnbp3"] = nnbp3 - d[sample]["nrr1"] = nrr1 - d[sample]["nrr2"] = nrr2 - d[sample]["nrr3"] = nrr3 - write_csv(d, CSV, n_samp=nsamp) - - orga1_clean = organism1.replace("_", " ") - orga2_clean = organism2.replace("_", " ") - if nsamp == 3: - orga3_clean = organism3.replace("_", " ") - - r = list(range(len(list(d.keys())))) - raw_data = {} - barWidth = 0.85 - if nsamp == 2: - raw_data['greenBars'] = [i for i in [d[sample]['nrr1'] - for sample in list(d.keys())]] - print(raw_data['greenBars']) - raw_data['orangeBars'] = [i/j for i in [d[sample]['nrr2'] - for sample in list(d.keys())]] - print(raw_data['orangeBars']) - df = pd.DataFrame(raw_data) - - totals = [i+j for i, j in zip(df['greenBars'], df['orangeBars'])] - greenBars = [i / j * 100 for i, j in zip(df['greenBars'], totals)] - print(greenBars) - orangeBars = [i / j * 100 for i, j in zip(df['orangeBars'], totals)] - print(orangeBars) - - names = list(d.keys()) - # Create green Bars - plt.bar(r, greenBars, color='#b5ffb9', edgecolor='white', - width=barWidth, label=orga1_clean) - # Create orange Bars - plt.bar(r, orangeBars, bottom=greenBars, color='#f9bc86', - edgecolor='white', width=barWidth, label=orga2_clean) - - elif nsamp == 3: - raw_data['greenBars'] = [i for i in [d[sample]['nrr1'] - for sample in list(d.keys())]] - raw_data['orangeBars'] = [i for i in [d[sample]['nrr2'] - for sample in list(d.keys())]] - raw_data['blueBars'] = [i for i in [d[sample]['nrr3'] - for sample in list(d.keys())]] - df = pd.DataFrame(raw_data) - - totals = [i+j+k for i, - j, k in zip(df['greenBars'], df['orangeBars'], df['blueBars'])] - greenBars = [i / j * 100 for i, j in zip(df['greenBars'], totals)] - orangeBars = [i / j * 100 for i, j in zip(df['orangeBars'], totals)] - blueBars = [i / j * 100 for i, j in zip(df['blueBars'], totals)] - - names = list(d.keys()) - # Create green Bars - plt.bar(r, greenBars, color='#b5ffb9', edgecolor='white', - width=barWidth, label=orga1_clean) - # Create orange Bars - plt.bar(r, orangeBars, bottom=greenBars, color='#f9bc86', - edgecolor='white', width=barWidth, label=orga2_clean) - # Create blue Bars - plt.bar(r, blueBars, bottom=[i+j for i, j in zip(greenBars, orangeBars)], color='#a3acff', - edgecolor='white', width=barWidth, label=orga3_clean) - - plt.xticks(r, names, rotation='45', ha="right", rotation_mode="anchor") - plt.xlabel("Sample") - plt.legend(loc='upper left', bbox_to_anchor=(1, 1), ncol=1) - plt.subplots_adjust(bottom=0.4, right=0.7) - - plt.savefig("./ratio.png") - - now = datetime.datetime.now() - with open(OUTFILE, "w") as fw: - fw.write("---\ntitle: coproID - Coprolite Identification\n---\n ") - fw.write( - '\n\n') - fw.write( - '**Homepage/Documentation**: [github.com/maxibor/coproID](https://github.com/maxibor/coproID) \n**Author:** Maxime Borry [borry@shh.mpg.de](mailto:borry@shh.mpg.de)\n\n ') - fw.write("Report automatically generated by coproID version " + VERSION + " on " + str(now.year) + "-" + str(now.month) + "-" + - str(now.day) + " " + str(now.hour) + ":" + str(now.minute) + ":" + str(now.second) + " \n") - # fw.write("\n\n") - # fw.write("***\n") - # fw.write("- **Organisms:** \n\n - *" + orga1_clean + "* (genome size = " + str(gs1) + - # " bp ) \n\n - *" + orga2_clean + "* (genome size = " + str(gs2) + " bp) \n") - # fw.write( - # "- **The formula used to compute the read ratio is the following:** \n\n $NormalizedRead_{Ratio} = \\log2\\left(\\frac{\\frac{N_{\\ aDNA\\ bp \\ aligned \\ genome1}}{size_{genome2} }}{\\frac{N_{ \\ aDNA \\ bp \\ aligned \\ genome2}}{size_{genome2}}}\\right)$\n\n ") - fw.write("\n***\n") - fw.write("## coproID read ratio plot\n\n") - fw.write("![Normalized read ratio (insert legend here)](./ratio.png)\n") - fw.write("\n\n***\n\n") - fw.write("***\n\n") - for asample in d.keys(): - orga1_clean = d[asample]["orga1"].replace("_", " ") - orga2_clean = d[asample]["orga2"].replace("_", " ") - # if d[asample]["nrr"] > 1: - # conclusion = 'There are more *' + orga1_clean + '* aDNA reads than *' + orga2_clean + \ - # '* by > 2 folds. Therefore this coprolite is most likely originating from *' + \ - # orga1_clean + '*' - # elif d[asample]["nrr"] < -1: - # conclusion = 'There are more *' + orga2_clean + '* aDNA reads than *' + orga1_clean + \ - # '* by > 2 folds. Therefore this coprolite is most likely originating from *' + \ - # orga2_clean + '*' - # else: - # conclusion = 'There is a similar amount of aDNA reads originating from *' + \ - # orga1_clean + '* and *' + orga2_clean + \ - # '*. coproID can not reliably estimate which of these two organisms this coprolite is coming from.' - # fw.write("### **Sample:** " + asample + " \n") - # fw.write("- **Number of base pairs (bp) in reads aligned to *" + - # orga1_clean + "* (genome 1) at " + str(identity) + "\% identity:** " + str(d[asample]["nbp1"]) + " \n") - # fw.write("- **Number of base pairs (bp) in reads aligned to *" + - # orga2_clean + "* (genome 2) at " + str(identity) + "\% identity:** " + str(d[asample]["nbp2"]) + " \n\n") - # fw.write("- $NormalizedRead_{ratio} = \\log2\\left(\\frac{" + d[asample]["orga1"].replace("_", "\\;") + "}{" + - # d[asample]["orga2"].replace("_", "\\;") + "}\\right) = " + str(round(d[asample]["nrr"], 3)) + "$ \n\n") - # fw.write(conclusion + '\n\n') - # fw.write("- **MapDamage plots**\n\n") - # if d[asample]["nbp1"] > 20 and ADNA == True: - # fw.write("![MapDamage Fragmisincorporation plot for " + - # orga1_clean + ". Very jagged and irregular substitutions are likely indicating spurious damages caused by a lack of reads. ](./" + asample + "." + d[asample]["orga1"] + ".fragmisincorporation_plot.png)\n\n") - # else: - # fw.write( - # "\n\nNo plot is displayed because the number of bases aligned is too low for mapDamage, or it is Modern DNA\n\n") - # if d[asample]["nbp2"] > 20 and ADNA == True: - # fw.write("![MapDamage Fragmisincorporation plot for " + - # orga2_clean + ". Very jagged and irregular substitutions are likely indicating spurious damages caused by a lack of reads. If there is not plot displayed, the aligned read count was too low for mapDamage](./" + asample + "." + d[asample]["orga2"] + ".fragmisincorporation_plot.png)\n\n") - # else: - # fw.write( - # "\n\nNo plot is displayed because the number of bases aligned is too low for mapDamage, or it is Modern DNA\n\n") - fw.write("\n\n***\n") From 047c2439c91356d44162123cb5c74825734be6ee Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:02:24 +0200 Subject: [PATCH 05/96] update report --- templates/coproID_report.ipynb | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 6a0868b..14c1e90 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -36,14 +36,22 @@ "print(f\"Report generated on {datetime.datetime.now()}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](coproID_nf-core_logo_small.png)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", - "[coproID](https://github.com/maxibor/coproID) is a pipeline to identify the source of coprolites, and in general, of a metagenomic sample.\n", + "[coproID](https://github.com/nf-core/coproID) is a pipeline to identify the source of coprolites, and in general, of a metagenomic sample.\n", "\n", - "If you read these lines, coproID successfully finished running and you can find your results below." + "If you read these lines, coproID successfully finished running and you can find your results below. \n", + "You can find more informations about the different result files in the coproID documentation: [coproid.readthedocs.io/](https://coproid.readthedocs.io/en/latest/output.html)" ] }, { @@ -2178,6 +2186,19 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": false, + "toc_window_display": false } }, "nbformat": 4, From 2c1be7955fb8c437a80e5a1aee55e56366ad22fd Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:29:27 +0200 Subject: [PATCH 06/96] Update mapped basepair count --- bin/merge_bp_sp.py | 108 +++++++++++------ bin/normalizedReadCount | 250 +++++++++++++++++++++++++++++----------- docs/output.md | 10 +- environment.yml | 1 + main.nf | 207 +++++++++++++++++++++++---------- 5 files changed, 412 insertions(+), 164 deletions(-) diff --git a/bin/merge_bp_sp.py b/bin/merge_bp_sp.py index 98bc376..6af02e4 100755 --- a/bin/merge_bp_sp.py +++ b/bin/merge_bp_sp.py @@ -54,41 +54,83 @@ def compute_coproba(indic, nrr, sp): if __name__ == "__main__": CF, SP, OUTPUT = get_args() - dcf = pd.read_csv(CF, index_col=0, header=None) + # dcf = pd.read_csv(CF, index_col=0, header=None) + dcf = pd.read_csv(CF, index_col=0) print(dcf.shape) - if dcf.shape[1] < 13: - dcf.columns = ['Organism_name1', - 'Organism_name2', - 'Genome1_size', - 'Genome2_size', - 'nb_bp_aligned_genome1', - 'nb_bp_aligned_genome2', - 'normalized_nb_bp_aligned_genome1', - 'normalized_nb_bp_aligned_genome2', - 'NormalizedReadRatio_1', - 'NormalizedReadRatio_2'] - orga1 = dcf['Organism_name1'][0] - orga2 = dcf['Organism_name2'][0] - orga3 = None - else: - dcf.columns = ['Organism_name1', - 'Organism_name2', - 'Organism_name3', - 'Genome1_size', - 'Genome2_size', - 'Genome3_size', - 'nb_bp_aligned_genome1', - 'nb_bp_aligned_genome2', - 'nb_bp_aligned_genome3', - 'normalized_nb_bp_aligned_genome1', - 'normalized_nb_bp_aligned_genome2', - 'normalized_nb_bp_aligned_genome3', - 'NormalizedReadRatio_1', - 'NormalizedReadRatio_2', - 'NormalizedReadRatio_3'] - orga1 = dcf['Organism_name1'][0] - orga2 = dcf['Organism_name2'][0] + # if dcf.shape[1] == 10: + # dcf.columns = ['Organism_name1', + # 'Organism_name2', + # 'Genome1_size', + # 'Genome2_size', + # 'nb_bp_aligned_genome1', + # 'nb_bp_aligned_genome2', + # 'normalized_nb_bp_aligned_genome1', + # 'normalized_nb_bp_aligned_genome2', + # 'NormalizedReadRatio_1', + # 'NormalizedReadRatio_2'] + orga1 = dcf['Organism_name1'][0] + orga2 = dcf['Organism_name2'][0] + try: orga3 = dcf['Organism_name3'][0] + except: + orga3 = None + # elif dcf.shape[1] == 15: + # # dcf.columns = ['Organism_name1', + # # 'Organism_name2', + # # 'Organism_name3', + # # 'Genome1_size', + # # 'Genome2_size', + # # 'Genome3_size', + # # 'nb_bp_aligned_genome1', + # # 'nb_bp_aligned_genome2', + # # 'nb_bp_aligned_genome3', + # # 'normalized_nb_bp_aligned_genome1', + # # 'normalized_nb_bp_aligned_genome2', + # # 'normalized_nb_bp_aligned_genome3', + # # 'NormalizedReadRatio_1', + # # 'NormalizedReadRatio_2', + # # 'NormalizedReadRatio_3'] + # orga1 = dcf['Organism_name1'][0] + # orga2 = dcf['Organism_name2'][0] + # orga3 = dcf['Organism_name3'][0] + # elif dcf.shape[1] == 12: + # # dcf.columns = ['Organism_name1', + # # 'Organism_name2', + # # 'Genome1_size', + # # 'Genome2_size', + # # 'nb_bp_aligned_genome1', + # # 'nb_bp_aligned_genome2', + # # 'nb_ancient_bp_aligned_genome1', + # # 'nb_ancient_bp_aligned_genome2', + # # 'normalized_nb_ancient_bp_aligned_genome1', + # # 'normalized_nb_ancient_bp_aligned_genome2', + # # 'NormalizedReadRatio_1', + # # 'NormalizedReadRatio_2'] + # orga1 = dcf['Organism_name1'][0] + # orga2 = dcf['Organism_name2'][0] + # orga3 = None + # elif dcf.shape[1] == 19: + # # dcf.columns = ['Organism_name1', + # # 'Organism_name2', + # # 'Organism_name3', + # # 'Genome1_size', + # # 'Genome2_size', + # # 'Genome3_size', + # # 'nb_bp_aligned_genome1', + # # 'nb_bp_aligned_genome2', + # # 'nb_bp_aligned_genome3', + # # 'nb_ancient_bp_aligned_genome1', + # # 'nb_ancient_bp_aligned_genome2', + # # 'nb_ancient_bp_aligned_genome3', + # # 'normalized_nb_ancient_bp_aligned_genome1', + # # 'normalized_nb_ancient_bp_aligned_genome2', + # # 'normalized_nb_ancient_bp_aligned_genome3', + # # 'NormalizedReadRatio_1', + # # 'NormalizedReadRatio_2', + # # 'NormalizedReadRatio_3'] + # orga1 = dcf['Organism_name1'][0] + # orga2 = dcf['Organism_name2'][0] + # orga3 = dcf['Organism_name3'][0] dsp = pd.read_csv(SP, index_col=0).T diff --git a/bin/normalizedReadCount b/bin/normalizedReadCount index 7bf00bf..b8a84db 100755 --- a/bin/normalizedReadCount +++ b/bin/normalizedReadCount @@ -13,53 +13,68 @@ def get_args(): parser = argparse.ArgumentParser( prog='normalizedReadCount', description='Counts reads aligned to genome, and normalize by genome size') + parser.add_argument( + '-ab1', + dest='abam1', + default=None, + help="PMDTools Bam aligment file on genome 1. Default = None") parser.add_argument( '-b1', dest='bam1', default=None, - help="Bam aligment file on genome. Default = None") + help="Bam aligment file on genome 1. Default = None") parser.add_argument( '-g1', dest="genome1", default=None, - help="Fasta file of genome. Default = None") + help="Fasta file of genome 1. Default = None") parser.add_argument( '-r1', dest='organism1', default=None, - help='Organism name. Example: "Homo_sapiens". Default = None' + help='Organism 1 name. Example: "Homo_sapiens". Default = None' ) + parser.add_argument( + '-ab2', + dest='abam2', + default=None, + help="PMDTools Bam aligment file on genome 2. Default = None") parser.add_argument( '-b2', dest='bam2', default=None, - help="Bam aligment file on genome. Default = None") + help="Bam aligment file on genome 2 . Default = None") parser.add_argument( '-g2', dest="genome2", default=None, - help="Fasta file of genome. Default = None") + help="Fasta file of genome 2. Default = None") parser.add_argument( '-r2', dest='organism2', default=None, - help='Organism name. Example: "Homo_sapiens". Default = None' + help='Organism 2 name. Example: "Homo_sapiens". Default = None' ) + parser.add_argument( + '-ab3', + dest='abam3', + default=None, + help="PMDTools Bam aligment file on genome 3. Default = None") parser.add_argument( '-b3', dest='bam3', default=None, - help="Bam aligment file on genome. Default = None") + help="Bam aligment file on genome 3. Default = None") parser.add_argument( '-g3', dest="genome3", default=None, - help="Fasta file of genome. Default = None") + help="Fasta file of genome 3. Default = None") parser.add_argument( '-r3', dest='organism3', default=None, - help='Organism name. Example: "Homo_sapiens". Default = None' + help='Organism 3 name. Example: "Homo_sapiens". Default = None' ) parser.add_argument( '-n', @@ -85,18 +100,36 @@ def get_args(): default=None, help="Output bam 1 filename. Default = {BAM1_INPUT}.filtered.bam" ) + parser.add_argument( + '-aob1', + dest="output_abam1", + default=None, + help="Output PMDtools bam 1 filename. Default = {BAM1_INPUT}.ancient.filtered.bam" + ) parser.add_argument( '-ob2', dest="output_bam2", default=None, help="Output bam 2 filename. Default = {BAM2_INPUT}.filtered.bam" ) + parser.add_argument( + '-aob2', + dest="output_abam2", + default=None, + help="Output PMDtools bam 2 filename. Default = {BAM2_INPUT}.ancient.filtered.bam" + ) parser.add_argument( '-ob3', dest="output_bam3", default=None, help="Output bam 3 filename. Default = {BAM2_INPUT}.filtered.bam" ) + parser.add_argument( + '-aob3', + dest="output_abam3", + default=None, + help="Output PMDtools bam 3 filename. Default = {BAM2_INPUT}.ancient.filtered.bam" + ) parser.add_argument( '-ed1', dest="endo_dna1", @@ -124,12 +157,15 @@ def get_args(): args = parser.parse_args() + abam1 = args.abam1 bam1 = args.bam1 genome1 = args.genome1 organame1 = args.organism1 + abam2 = args.abam2 bam2 = args.bam2 genome2 = args.genome2 organame2 = args.organism2 + abam3 = args.abam3 bam3 = args.bam3 genome3 = args.genome3 organame3 = args.organism3 @@ -137,20 +173,26 @@ def get_args(): identity = float(str(args.identity)) outfile = args.output obam1 = args.output_bam1 + aobam1 = args.output_abam1 obam2 = args.output_bam2 + aobam2 = args.output_abam2 obam3 = args.output_bam3 + aobam3 = args.output_abam3 endo1 = float(str(args.endo_dna1)) endo2 = float(str(args.endo_dna2)) endo3 = float(str(args.endo_dna3)) processes = int(args.processes) - return(bam1, + return(abam1, + bam1, genome1, organame1, + abam2, bam2, genome2, organame2, + abam3, bam3, genome3, organame3, @@ -159,8 +201,11 @@ def get_args(): processes, outfile, obam1, + aobam1, obam2, + aobam2, obam3, + aobam3, endo1, endo2, endo3) @@ -201,8 +246,6 @@ def getNumberMappedReads(bam, id): def perChromosome(chr, bam, id, commonReads=[]): resdic = {} - resdic["bpCnt"] = 0 - resdic["mappedReads"] = [] min_identity = id bamfile = pysam.AlignmentFile(bam, "rb") reads = bamfile.fetch(chr, multiple_iterators=True) @@ -213,24 +256,12 @@ def perChromosome(chr, bam, id, commonReads=[]): readLen = read.query_length identity = (alnLen - mismatch) / readLen if identity >= min_identity: - resdic["bpCnt"] += (alnLen - mismatch) - resdic["mappedReads"].append(read.query_name) - else: - for read in reads: - mismatch = read.get_tag("NM") - alnLen = read.query_alignment_length - readLen = read.query_length - identity = (alnLen - mismatch) / readLen - if identity >= min_identity and (read.query_name not in(commonReads)): - resdic["bpCnt"] += (alnLen - mismatch) - resdic["mappedReads"].append(read.query_name) + resdic[read.query_name] = alnLen - mismatch return(resdic) def getNumberMappedReadsMultiprocess(bam, processes, id, commonReads=[]): resdic = {} - resdic["bpCnt"] = 0 - resdic["mappedReads"] = [] try: bamfile = pysam.AlignmentFile(bam, "rb") except ValueError: @@ -244,8 +275,7 @@ def getNumberMappedReadsMultiprocess(bam, processes, id, commonReads=[]): p.close() p.join() for i in result: - resdic["bpCnt"] += i["bpCnt"] - resdic["mappedReads"].extend(list(i["mappedReads"])) + resdic.update(i) return(resdic) @@ -272,6 +302,14 @@ def getCommonReads(readsBam1, readsBam2, readsBam3=None): return(res) +def get_total_bp(bamres, common): + bp_cnt = 0 + for i in bamres.keys(): + if i not in common: + bp_cnt += bamres[i] + return(bp_cnt) + + def check_endo(endo_dna, organism): if endo_dna >= 0 and endo_dna <= 1: return True @@ -281,7 +319,7 @@ def check_endo(endo_dna, organism): if __name__ == "__main__": - BAM1, GENOME1, ORGANAME1, BAM2, GENOME2, ORGANAME2, BAM3, GENOME3, ORGANAME3, SNAME, ID, PROCESSES, OUTFILE, OBAM1, OBAM2, OBAM3, ENDO1, ENDO2, ENDO3 = get_args() + ABAM1, BAM1, GENOME1, ORGANAME1, ABAM2, BAM2, GENOME2, ORGANAME2, ABAM3, BAM3, GENOME3, ORGANAME3, SNAME, ID, PROCESSES, OUTFILE, OBAM1, AOBAM1, OBAM2, AOBAM2, OBAM3, AOBAM3, ENDO1, ENDO2, ENDO3 = get_args() if BAM1 is None: print("Missing BAM file") @@ -315,70 +353,150 @@ if __name__ == "__main__": bam3_res = getNumberMappedReadsMultiprocess( bam=BAM3, processes=PROCESSES, id=ID) - print("nb1_first", bam1_res["bpCnt"]) - print("nb2_first", bam2_res["bpCnt"]) - if BAM3: - print("nb3_first", bam3_res["bpCnt"]) - - reads1 = bam1_res["mappedReads"] - reads2 = bam2_res["mappedReads"] + if ABAM1 and ABAM2: + abam1_res = getNumberMappedReadsMultiprocess( + bam=ABAM1, processes=PROCESSES, id=ID) + abam2_res = getNumberMappedReadsMultiprocess( + bam=ABAM2, processes=PROCESSES, id=ID) + if ABAM3: + abam3_res = getNumberMappedReadsMultiprocess( + bam=ABAM3, processes=PROCESSES, id=ID) + + reads1 = list(bam1_res.keys()) + reads2 = list(bam2_res.keys()) if BAM3: - reads3 = bam3_res["mappedReads"] + reads3 = list(bam3_res.keys()) if not BAM3: commonReads = getCommonReads(readsBam1=reads1, readsBam2=reads2) else: commonReads = getCommonReads( readsBam1=reads1, readsBam2=reads2, readsBam3=reads3) - nb1 = getNumberMappedReadsMultiprocess( - bam=BAM1, processes=PROCESSES, id=ID, commonReads=commonReads)["bpCnt"] - nnbp1 = (nb1 + 1) / gs1 - nb2 = getNumberMappedReadsMultiprocess( - bam=BAM2, processes=PROCESSES, id=ID, commonReads=commonReads)["bpCnt"] - nnbp2 = (nb2 + 1) / gs2 - if BAM3: - nb3 = getNumberMappedReadsMultiprocess( - bam=BAM3, processes=PROCESSES, id=ID, commonReads=commonReads)["bpCnt"] - nnbp3 = (nb3 + 1) / gs3 + if ABAM1 and ABAM2: + areads1 = list(abam1_res.keys()) + areads2 = list(abam2_res.keys()) + if ABAM3: + areads3 = list(abam3_res.keys()) + if not ABAM3: + acommonReads = getCommonReads(readsBam1=areads1, readsBam2=areads2) + else: + acommonReads = getCommonReads( + readsBam1=areads1, readsBam2=areads2, readsBam3=areads3) if OBAM1 is None: outbam1 = bam_basename1 + ".filtered.bam" else: outbam1 = OBAM1 + if AOBAM1 is None and ABAM1: + aoutbam1 = bam_basename1 + ".ancient.filtered.bam" + elif ABAM1: + aoutbam1 = AOBAM1 + if OBAM2 is None: outbam2 = bam_basename2 + ".filtered.bam" else: outbam2 = OBAM2 + if AOBAM2 is None and ABAM2: + aoutbam2 = bam_basename2 + ".ancient.filtered.bam" + elif ABAM2: + aoutbam2 = AOBAM2 + if OBAM3 is None and BAM3: outbam3 = bam_basename3 + ".filtered.bam" else: outbam3 = OBAM3 + if AOBAM3 is None and ABAM3: + aoutbam3 = bam_basename3 + ".ancient.filtered.bam" + elif ABAM3: + aoutbam3 = AOBAM3 writeBam(inbam=BAM1, outbam=outbam1, commonReads=commonReads) writeBam(inbam=BAM2, outbam=outbam2, commonReads=commonReads) if OBAM3: writeBam(inbam=BAM3, outbam=outbam3, commonReads=commonReads) - # Two genomes - NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) * ENDO1 - NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) * ENDO2 - if BAM3: - # Three genomes - NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2 + nnbp3)) * ENDO1 - NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2 + nnbp3)) * ENDO2 - NormalizedReadRatio_3 = (nnbp3 / (nnbp1 + nnbp2 + nnbp3)) * ENDO3 + if ABAM1 and ABAM2: + writeBam(inbam=ABAM1, outbam=aoutbam1, commonReads=acommonReads) + writeBam(inbam=ABAM2, outbam=aoutbam2, commonReads=acommonReads) + if OBAM3: + writeBam(inbam=ABAM3, outbam=aoutbam3, commonReads=acommonReads) - # sourcepredict + nb1 = get_total_bp(bam1_res, commonReads) + nb2 = get_total_bp(bam2_res, commonReads) + if BAM3: + nb3 = get_total_bp(bam3_res, commonReads) - if not BAM3: - # Template output file structure - # Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,NormalizedReadRatio_1,NormalizedReadRatio_2 - with open(OUTFILE, 'w') as w: - w.write( - f"{SNAME},{ORGANAME1},{ORGANAME2},{gs1},{gs2},{nb1},{nb2},{nnbp1},{nnbp2},{NormalizedReadRatio_1},{NormalizedReadRatio_2}\n") + print("nb1_all", nb1) + print("nb2_all", nb2) + if BAM3: + print("nb3_all", nb3) + + if ABAM1 and ABAM2: + # Two genomes + anb1 = get_total_bp(abam1_res, acommonReads) + nnbp1 = (anb1 + 1) / gs1 + anb2 = get_total_bp(abam2_res, acommonReads) + nnbp2 = (anb2 + 1) / gs2 + print("nb1_ancient", anb1) + print("nb2_ancient", anb2) + + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) * ENDO1 + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) * ENDO2 + if BAM3: + # Three genomes + anb3 = get_total_bp(abam3_res, acommonReads) + nnbp3 = (anb3 + 1) / gs3 + + print("nb3_ancient", anb3) + + NormalizedReadRatio_1 = ( + nnbp1 / (nnbp1 + nnbp2 + nnbp3)) * ENDO1 + NormalizedReadRatio_2 = ( + nnbp2 / (nnbp1 + nnbp2 + nnbp3)) * ENDO2 + NormalizedReadRatio_3 = ( + nnbp3 / (nnbp1 + nnbp2 + nnbp3)) * ENDO3 + else: + # Two genomes + nnbp1 = (nb1 + 1) / gs1 + nnbp2 = (nb2 + 1) / gs2 + + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) * ENDO1 + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) * ENDO2 + if BAM3: + # Three genomes + nnbp3 = (nb3 + 1) / gs3 + + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2 + nnbp3)) * ENDO1 + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2 + nnbp3)) * ENDO2 + NormalizedReadRatio_3 = (nnbp3 / (nnbp1 + nnbp2 + nnbp3)) * ENDO3 + + if ABAM1 and ABAM2: + if not BAM3 and not ABAM3: + # Template output file structure + # Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_ancient_bp_aligned_genome1,nb_ancient_bp_aligned_genome2,normalized_nb_ancient_bp_aligned_genome1,normalized_nb_ancient_bp_aligned_genome2,NormalizedReadRatio_1,NormalizedReadRatio_2 + with open(OUTFILE, 'w') as w: + w.write("Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_ancient_bp_aligned_genome1,nb_ancient_bp_aligned_genome2,normalized_nb_ancient_bp_aligned_genome1,normalized_nb_ancient_bp_aligned_genome2,NormalizedReadRatio_1,NormalizedReadRatio_2\n") + w.write( + f"{SNAME},{ORGANAME1},{ORGANAME2},{gs1},{gs2},{nb1},{nb2},{anb1},{anb2},{nnbp1},{nnbp2},{NormalizedReadRatio_1},{NormalizedReadRatio_2}\n") + else: + # Template output file structure + # Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,nb_ancient_bp_aligned_genome1,nb_ancient_bp_aligned_genome2,nb_ancient_bp_aligned_genome3,normalized_nb_ancient_bp_aligned_genome1,normalized_nb_ancient_bp_aligned_genome2,normalized_nb_ancient_bp_aligned_genome3,NormalizedReadRatio_1,NormalizedReadRatio_2,NormalizedReadRatio_3 + with open(OUTFILE, 'w') as w: + w.write("Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,nb_ancient_bp_aligned_genome1,nb_ancient_bp_aligned_genome2,nb_ancient_bp_aligned_genome3,normalized_nb_ancient_bp_aligned_genome1,normalized_nb_ancient_bp_aligned_genome2,normalized_nb_ancient_bp_aligned_genome3,NormalizedReadRatio_1,NormalizedReadRatio_2,NormalizedReadRatio_3\n") + w.write( + f"{SNAME},{ORGANAME1},{ORGANAME2},{ORGANAME3},{gs1},{gs2},{gs3},{nb1},{nb2},{nb3},{anb1},{anb2},{anb3},{nnbp1},{nnbp2},{nnbp3},{NormalizedReadRatio_1},{NormalizedReadRatio_2},{NormalizedReadRatio_3}\n") else: - # Template output file structure - # Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome3,NormalizedReadRatio_1,NormalizedReadRatio_2,NormalizedReadRatio_3 - with open(OUTFILE, 'w') as w: - w.write( - f"{SNAME},{ORGANAME1},{ORGANAME2},{ORGANAME3},{gs1},{gs2},{gs3},{nb1},{nb2},{nb3},{nnbp1},{nnbp2},{nnbp3},{NormalizedReadRatio_1},{NormalizedReadRatio_2},{NormalizedReadRatio_3}\n") + if not BAM3: + # Template output file structure + # Sample_name,Organism_name1,Organism_name2,Genome1_size,Genome2_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,NormalizedReadRatio_1,NormalizedReadRatio_2 + with open(OUTFILE, 'w') as w: + w.write("Sample_name, Organism_name1, Organism_name2, Genome1_size, Genome2_size, nb_bp_aligned_genome1, nb_bp_aligned_genome2, normalized_nb_bp_aligned_genome1, normalized_nb_bp_aligned_genome2, NormalizedReadRatio_1, NormalizedReadRatio_2\n") + w.write( + f"{SNAME},{ORGANAME1},{ORGANAME2},{gs1},{gs2},{nb1},{nb2},{nnbp1},{nnbp2},{NormalizedReadRatio_1},{NormalizedReadRatio_2}\n") + else: + # Template output file structure + # Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome3,NormalizedReadRatio_1,NormalizedReadRatio_2,NormalizedReadRatio_3 + with open(OUTFILE, 'w') as w: + w.write("Sample_name,Organism_name1,Organism_name2,Organism_name3,Genome1_size,Genome2_size,Genome3_size,nb_bp_aligned_genome1,nb_bp_aligned_genome2,nb_bp_aligned_genome3,normalized_nb_bp_aligned_genome1,normalized_nb_bp_aligned_genome2,normalized_nb_bp_aligned_genome3,NormalizedReadRatio_1,NormalizedReadRatio_2,NormalizedReadRatio_3\n") + w.write( + f"{SNAME},{ORGANAME1},{ORGANAME2},{ORGANAME3},{gs1},{gs2},{gs3},{nb1},{nb2},{nb3},{nnbp1},{nnbp2},{nnbp3},{NormalizedReadRatio_1},{NormalizedReadRatio_2},{NormalizedReadRatio_3}\n") diff --git a/docs/output.md b/docs/output.md index df24f9f..5d28693 100644 --- a/docs/output.md +++ b/docs/output.md @@ -28,6 +28,8 @@ These plots represents the damage patterns and read length distribution. ## coproID_report.html +This file contains the coproID report + ### coproID summary table This table summarizes the read ratios and microbiome source proportions as computed by coproID and sourcepredict. @@ -39,19 +41,23 @@ This interactive plot shows the embedding of the microbiome samples by [sourcepr ### Damage plots -These plots represents the damage patterns computed by DamageProfiler +These plots represent the damage patterns computed by DamageProfiler ## coproID_result.csv This table summarizes the read ratios and microbiome source proportions as computed by coproID and sourcepredict. +## coproID_bp.csv + +This table contains the mapped base pair counts (ancient and modern reads) for each sample. + ## kraken This directory contains the merged OTU count for all samples of the run, as counted by [Kraken2](https://ccb.jhu.edu/software/kraken2/) ## damageprofiler -This directory contains all of the output files of DamageProfiler (see multiqc section above) +This directory contains all the output files of DamageProfiler (see multiqc section above) ## alignments diff --git a/environment.yml b/environment.yml index 2e57c6d..d29a8d1 100644 --- a/environment.yml +++ b/environment.yml @@ -33,3 +33,4 @@ dependencies: - anaconda::scipy=1.2.1 - bioconda::multiqc=1.7 - conda-forge::r-markdown=0.9 + - conda-forge::jupyter_contrib_nbextensions=0.5.1 diff --git a/main.nf b/main.nf index 77dd9af..809c14e 100644 --- a/main.nf +++ b/main.nf @@ -328,6 +328,11 @@ sp_labels = file(params.sp_labels, checkIfExists: true) sp_sources = file(params.sp_sources, checkIfExists: true) +/******************* +coproID logo channel +********************/ +logo = file("$baseDir/assets/img/coproID_nf-core_logo_small.png") + /******************* Logging parameters ********************/ @@ -574,7 +579,7 @@ process AlignToGenome1 { set val(name), file(reads) from trimmed_reads_genome1 file(index) from bt1_ch output: - set val(name), file("*.aligned.sorted.bam") into alignment_genome1 + set val(name), file("*.aligned.sorted.bam") into alignment_genome1, alignment_genome1_pmd set val(name), file("*.unaligned.sorted.bam") into unaligned_genome1 set val(name), file("*.stats.txt") into align1_multiqc script: @@ -675,7 +680,7 @@ process AlignToGenome2 { set val(name), file(reads) from trimmed_reads_genome2 file(index) from bt2_ch output: - set val(name), file("*.aligned.sorted.bam") into alignment_genome2 + set val(name), file("*.aligned.sorted.bam") into alignment_genome2, alignment_genome2_pmd set val(name), file("*.unaligned.sorted.bam") into unaligned_genome2 set val(name), file("*.stats.txt") into align2_multiqc script: @@ -712,7 +717,7 @@ if (params.name3) { set val(name), file(reads) from trimmed_reads_genome3 file(index) from bt3_ch output: - set val(name), file("*.aligned.sorted.bam") into alignment_genome3 + set val(name), file("*.aligned.sorted.bam") into alignment_genome3, alignment_genome3_pmd set val(name), file("*.unaligned.sorted.bam") into unaligned_genome3 set val(name), file("*.stats.txt") into align3_multiqc script: @@ -747,7 +752,7 @@ if (params.adna){ publishDir "${params.outdir}/pmdtools/${params.name1}", mode: 'copy', pattern: '*.pmd_filtered.bam' input: - set val(name), file(bam1) from alignment_genome1 + set val(name), file(bam1) from alignment_genome1_pmd output: set val(name), file("*.pmd_filtered.bam") into pmd_aligned1 script: @@ -765,7 +770,7 @@ if (params.adna){ publishDir "${params.outdir}/pmdtools/${params.name2}", mode: 'copy', pattern: '*.pmd_filtered.bam' input: - set val(name), file(bam2) from alignment_genome2 + set val(name), file(bam2) from alignment_genome2_pmd output: set val(name), file("*.pmd_filtered.bam") into pmd_aligned2 script: @@ -784,7 +789,7 @@ if (params.adna){ publishDir "${params.outdir}/pmdtools/${params.name3}", mode: 'copy', pattern: '*.pmd_filtered.bam' input: - set val(name), file(bam3) from alignment_genome3 + set val(name), file(bam3) from alignment_genome3_pmd output: set val(name), file("*.pmd_filtered.bam") into pmd_aligned3 script: @@ -905,37 +910,68 @@ if (params.name3 == ''){ input: - set val(name), file(bam1), file(bam2) from ( params.adna ? pmd_aligned1.join(pmd_aligned2) : alignment_genome1.join(alignment_genome2)) + set val(name), file(abam1), file(abam2), file(bam1), file(bam2) from ( (params.adna ? pmd_aligned1.join(pmd_aligned2) : alignment_genome1_pmd.join(alignment_genome2_pmd)).join(alignment_genome1).join(alignment_genome2)) file(genome1) from genome1Size file(genome2) from genome2Size output: set val(name), file("*.bpc.csv") into bp_count - set val(name), file("*"+params.name1+".filtered.bam") into filtered_bam1 - set val(name), file("*"+params.name2+".filtered.bam") into filtered_bam2 + set val(name), file("*"+params.name1+".ancient.filtered.bam") optional true into ancient_filtered_bam1 + set val(name), file("*"+params.name2+".ancient.filtered.bam") optional true into ancient_filtered_bam2 script: outfile = name+".bpc.csv" organame1 = params.name1 organame2 = params.name2 obam1 = name+"_"+organame1+".filtered.bam" obam2 = name+"_"+organame2+".filtered.bam" - """ - samtools index $bam1 - samtools index $bam2 - normalizedReadCount -n $name \\ - -b1 $bam1 \\ - -b2 $bam2 \\ - -g1 $genome1 \\ - -g2 $genome2 \\ - -r1 $organame1 \\ - -r2 $organame2 \\ - -i ${params.identity} \\ - -o $outfile \\ - -ob1 $obam1 \\ - -ob2 $obam2 \\ - -ed1 ${params.endo1} \\ - -ed2 ${params.endo2} \\ - -p ${task.cpus} - """ + if (params.adna) { + aobam1 = name+"_"+organame1+".ancient.filtered.bam" + aobam2 = name+"_"+organame2+".ancient.filtered.bam" + """ + samtools index $bam1 + samtools index $bam2 + samtools index $abam1 + samtools index $abam2 + normalizedReadCount -n $name \\ + -b1 $bam1 \\ + -ab1 $abam1 \\ + -b2 $bam2 \\ + -ab2 $abam2 \\ + -g1 $genome1 \\ + -g2 $genome2 \\ + -r1 $organame1 \\ + -r2 $organame2 \\ + -i ${params.identity} \\ + -o $outfile \\ + -ob1 $obam1 \\ + -aob1 $aobam1 \\ + -ob2 $obam2 \\ + -aob2 $aobam2 \\ + -ed1 ${params.endo1} \\ + -ed2 ${params.endo2} \\ + -p ${task.cpus} + """ + } + else { + """ + samtools index $bam1 + samtools index $bam2 + normalizedReadCount -n $name \\ + -b1 $bam1 \\ + -b2 $bam2 \\ + -g1 $genome1 \\ + -g2 $genome2 \\ + -r1 $organame1 \\ + -r2 $organame2 \\ + -i ${params.identity} \\ + -o $outfile \\ + -ob1 $obam1 \\ + -ob2 $obam2 \\ + -ed1 ${params.endo1} \\ + -ed2 ${params.endo2} \\ + -p ${task.cpus} + """ + } + } } else { process countBp3genomes{ @@ -947,47 +983,88 @@ if (params.name3 == ''){ input: - set val(name), file(bam1), file(bam2), file(bam3) from ( params.adna ? pmd_aligned1.join(pmd_aligned2).join(pmd_aligned3) : alignment_genome1.join(alignment_genome2).join(alignment_genome3)) + set val(name), file(abam1), file(abam2), file(abam3), file(bam1), file(bam2), file(bam3) from ( (params.adna ? pmd_aligned1.join(pmd_aligned2).join(pmd_aligned3) : alignment_genome1_pmd.join(alignment_genome2_pmd).join(alignment_genome3_pmd)).join(alignment_genome1).join(alignment_genome2).join(alignment_genome3)) file(genome1) from genome1Size file(genome2) from genome2Size file(genome3) from genome3Size output: set val(name), file("*.bpc.csv") into bp_count - set val(name), file("*"+params.name1+".filtered.bam") into filtered_bam1 - set val(name), file("*"+params.name2+".filtered.bam") into filtered_bam2 - set val(name), file("*"+params.name3+".filtered.bam") into filtered_bam3 + set val(name), file("*"+params.name1+".ancient.filtered.bam") optional true into ancient_filtered_bam1 + set val(name), file("*"+params.name2+".ancient.filtered.bam") optional true into ancient_filtered_bam2 + set val(name), file("*"+params.name3+".ancient.filtered.bam") optional true into ancient_filtered_bam3 script: outfile = name+".bpc.csv" organame1 = params.name1 organame2 = params.name2 organame3 = params.name3 + obam1 = name+"_"+organame1+".filtered.bam" obam2 = name+"_"+organame2+".filtered.bam" obam3 = name+"_"+organame3+".filtered.bam" - """ - samtools index $bam1 - samtools index $bam2 - samtools index $bam3 - normalizedReadCount -n $name \\ - -b1 $bam1 \\ - -b2 $bam2 \\ - -b3 $bam3 \\ - -g1 $genome1 \\ - -g2 $genome2 \\ - -g3 $genome3 \\ - -r1 $organame1 \\ - -r2 $organame2 \\ - -r3 $organame3 \\ - -i ${params.identity} \\ - -o $outfile \\ - -ob1 $obam1 \\ - -ob2 $obam2 \\ - -ob3 $obam3 \\ - -ed1 ${params.endo1} \\ - -ed2 ${params.endo2} \\ - -ed3 ${params.endo3} \\ - -p ${task.cpus} - """ + if (params.adna) { + aobam1 = name+"_"+organame1+".ancient.filtered.bam" + aobam2 = name+"_"+organame2+".ancient.filtered.bam" + aobam3 = name+"_"+organame3+".ancient.filtered.bam" + """ + samtools index $bam1 + samtools index $bam2 + samtools index $bam3 + samtools index $abam1 + samtools index $abam2 + samtools index $abam3 + normalizedReadCount -n $name \\ + -b1 $bam1 \\ + -ab1 $abam1 \\ + -b2 $bam2 \\ + -ab2 $abam2 \\ + -b3 $bam3 \\ + -ab3 $abam3 \\ + -g1 $genome1 \\ + -g2 $genome2 \\ + -g3 $genome3 \\ + -r1 $organame1 \\ + -r2 $organame2 \\ + -r3 $organame3 \\ + -i ${params.identity} \\ + -o $outfile \\ + -ob1 $obam1 \\ + -aob1 $aobam1 \\ + -ob2 $obam2 \\ + -aob2 $aobam2 \\ + -ob3 $obam3 \\ + -aob3 $aobam3 \\ + -ed1 ${params.endo1} \\ + -ed2 ${params.endo2} \\ + -ed3 ${params.endo3} \\ + -p ${task.cpus} + """ + + } else { + """ + samtools index $bam1 + samtools index $bam2 + samtools index $bam3 + normalizedReadCount -n $name \\ + -b1 $bam1 \\ + -b2 $bam2 \\ + -b3 $bam3 \\ + -g1 $genome1 \\ + -g2 $genome2 \\ + -g3 $genome3 \\ + -r1 $organame1 \\ + -r2 $organame2 \\ + -r3 $organame3 \\ + -i ${params.identity} \\ + -o $outfile \\ + -ob1 $obam1 \\ + -ob2 $obam2 \\ + -ob3 $obam3 \\ + -ed1 ${params.endo1} \\ + -ed2 ${params.endo2} \\ + -ed3 ${params.endo3} \\ + -p ${task.cpus} + """ + } } } @@ -1005,7 +1082,7 @@ if (params.adna){ publishDir "${params.outdir}/damageprofiler/${params.name1}", mode: 'copy' input: - set val(name), file(align) from filtered_bam1 + set val(name), file(align) from ancient_filtered_bam1 file(fasta) from genome1damageprofiler output: file("*_freq.txt") into damage_result_genome1 @@ -1034,7 +1111,7 @@ if (params.adna){ publishDir "${params.outdir}/damageprofiler/${params.name2}", mode: 'copy' input: - set val(name), file(align) from filtered_bam2 + set val(name), file(align) from ancient_filtered_bam2 file(fasta) from genome2damageprofiler output: file("*_freq.txt") into damage_result_genome2 @@ -1064,7 +1141,7 @@ if (params.adna){ publishDir "${params.outdir}/damageprofiler/${params.name3}", mode: 'copy' input: - set val(name), file(align) from filtered_bam3 + set val(name), file(align) from ancient_filtered_bam3 file(fasta) from genome3damageprofiler output: file("*_freq.txt") into damage_result_genome3 @@ -1092,17 +1169,19 @@ process concatenateRatios { label 'ristretto' - publishDir "${params.outdir}", mode: 'copy', pattern: 'coproID_result.csv' + publishDir "${params.outdir}", mode: 'copy', pattern: '*.csv' input: file(count) from bp_count.collect() file(sp) from sourcepredict_out output: file("coproID_result.csv") into coproid_res + file("coproID_bp.csv") into coproid_bp_count script: outfile = "coproID_result.csv" """ - cat *.bpc.csv > coproid_bp.csv + ls -1 *.bpc.csv | head -1 | xargs head -1 > coproid_bp.csv + tail -q -n +2 *.bpc.csv >> coproid_bp.csv merge_bp_sp.py -c coproid_bp.csv -s $sp -o $outfile """ } @@ -1118,6 +1197,7 @@ if (params.adna) { input: file(copro_csv) from coproid_res + file(thelogo) from logo file(dplot1) from damage_result_genome1.collect().ifEmpty([]) file(dplot1) from damage_result_genome2.collect().ifEmpty([]) file(dplot3) from damage_result_genome3.collect().ifEmpty([]) @@ -1135,7 +1215,7 @@ if (params.adna) { --TemplateExporter.exclude_output_prompt=True \\ --ExecutePreprocessor.timeout=200 \\ --execute \\ - --to html $report + --to html_embed $report """ } } else { @@ -1147,6 +1227,7 @@ if (params.adna) { input: file(copro_csv) from coproid_res + file(thelogo) from logo file(dplot1) from damage_result_genome1.collect().ifEmpty([]) file(dplot1) from damage_result_genome2.collect().ifEmpty([]) file(umap) from sourcepredict_embed_out @@ -1163,7 +1244,7 @@ if (params.adna) { --TemplateExporter.exclude_output_prompt=True \\ --ExecutePreprocessor.timeout=200 \\ --execute \\ - --to html $report + --to html_embed $report """ } } @@ -1190,7 +1271,7 @@ if (params.adna) { --TemplateExporter.exclude_output_prompt=True \\ --ExecutePreprocessor.timeout=200 \\ --execute \\ - --to html $report + --to html_embed $report """ } } From fd1f7508af948f7a079863594cf4b3424816c80c Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:29:46 +0200 Subject: [PATCH 07/96] bump version number up --- nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index eef8c5f..976b506 100644 --- a/nextflow.config +++ b/nextflow.config @@ -119,7 +119,7 @@ manifest { description = 'Coprolite Identification' mainScript = 'main.nf' nextflowVersion = '>=0.32.0' - version = '1.0' + version = '1.1dev' } // Function to ensure that resource requirements don't go beyond // a maximum limit From 46e768e3620f47f36330d6dcabc4eef9d0eb9084 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:29:58 +0200 Subject: [PATCH 08/96] update changelog --- CHANGELOG.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 698246f..97590bf 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,8 +1,10 @@ # nf-core/coproid: Changelog -## v1.0.1dev +## v1.1dev -Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) +- Update mapped basepair count to be quicker and include it in report +- Remove outdated scripts +- Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) ## v1.0 - 2019-04-26 From ced90debd915de32fc286b3470c068e016781ff5 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:32:21 +0200 Subject: [PATCH 09/96] docker container to dev --- nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index 976b506..3ee6c3d 100644 --- a/nextflow.config +++ b/nextflow.config @@ -61,7 +61,7 @@ params { } // Container slug. Stable releases should specify release tag! // Developmental code should specify :dev -process.container = 'nfcore/coproid:1.0' +process.container = 'nfcore/coproid:dev' // Load base.config by default for all pipelines includeConfig 'conf/base.config' From de6ec0e34843443acdbe02dbcdbe57efad46c36f Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:36:41 +0200 Subject: [PATCH 10/96] update version number in env and dockerfile --- .travis.yml | 2 +- Dockerfile | 2 +- environment.yml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 177c572..6353fec 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,7 @@ before_install: # Fake the tag locally so that the pipeline runs properly # Looks weird when this is :dev to :dev, but makes sense when testing code for a release (:dev to :1.0.1) # - docker tag nfcore/coproid:dev nfcore/coproid:dev - - docker tag nfcore/coproid:dev nfcore/coproid:1.0 + - docker tag nfcore/coproid:dev nfcore/coproid:dev install: # Install Nextflow diff --git a/Dockerfile b/Dockerfile index ad6477c..b656cd2 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,4 +4,4 @@ LABEL authors="Maxime Borry" \ COPY environment.yml / RUN conda env create -f /environment.yml && conda clean -a -ENV PATH /opt/conda/envs/nf-core-coproid-1.0/bin:$PATH +ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH diff --git a/environment.yml b/environment.yml index d29a8d1..427c23e 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: nf-core-coproid-1.0 +name: nf-core-coproid-1.1dev channels: - bioconda - conda-forge From ce217ff3ce26030bc8608aa1a03f59afb8754b99 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 16:49:06 +0200 Subject: [PATCH 11/96] fix filename case --- main.nf | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/main.nf b/main.nf index 809c14e..c0751c9 100644 --- a/main.nf +++ b/main.nf @@ -1180,9 +1180,9 @@ process concatenateRatios { script: outfile = "coproID_result.csv" """ - ls -1 *.bpc.csv | head -1 | xargs head -1 > coproid_bp.csv - tail -q -n +2 *.bpc.csv >> coproid_bp.csv - merge_bp_sp.py -c coproid_bp.csv -s $sp -o $outfile + ls -1 *.bpc.csv | head -1 | xargs head -1 > coproID_bp.csv + tail -q -n +2 *.bpc.csv >> coproID_bp.csv + merge_bp_sp.py -c coproID_bp.csv -s $sp -o $outfile """ } From 17a409dbc218afad6d9084e5fd3d845cd961a6c9 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 17:04:53 +0200 Subject: [PATCH 12/96] remove dev comments --- bin/merge_bp_sp.py | 69 ---------------------------------------------- 1 file changed, 69 deletions(-) diff --git a/bin/merge_bp_sp.py b/bin/merge_bp_sp.py index 6af02e4..f78a2a4 100755 --- a/bin/merge_bp_sp.py +++ b/bin/merge_bp_sp.py @@ -54,83 +54,14 @@ def compute_coproba(indic, nrr, sp): if __name__ == "__main__": CF, SP, OUTPUT = get_args() - # dcf = pd.read_csv(CF, index_col=0, header=None) dcf = pd.read_csv(CF, index_col=0) print(dcf.shape) - # if dcf.shape[1] == 10: - # dcf.columns = ['Organism_name1', - # 'Organism_name2', - # 'Genome1_size', - # 'Genome2_size', - # 'nb_bp_aligned_genome1', - # 'nb_bp_aligned_genome2', - # 'normalized_nb_bp_aligned_genome1', - # 'normalized_nb_bp_aligned_genome2', - # 'NormalizedReadRatio_1', - # 'NormalizedReadRatio_2'] orga1 = dcf['Organism_name1'][0] orga2 = dcf['Organism_name2'][0] try: orga3 = dcf['Organism_name3'][0] except: orga3 = None - # elif dcf.shape[1] == 15: - # # dcf.columns = ['Organism_name1', - # # 'Organism_name2', - # # 'Organism_name3', - # # 'Genome1_size', - # # 'Genome2_size', - # # 'Genome3_size', - # # 'nb_bp_aligned_genome1', - # # 'nb_bp_aligned_genome2', - # # 'nb_bp_aligned_genome3', - # # 'normalized_nb_bp_aligned_genome1', - # # 'normalized_nb_bp_aligned_genome2', - # # 'normalized_nb_bp_aligned_genome3', - # # 'NormalizedReadRatio_1', - # # 'NormalizedReadRatio_2', - # # 'NormalizedReadRatio_3'] - # orga1 = dcf['Organism_name1'][0] - # orga2 = dcf['Organism_name2'][0] - # orga3 = dcf['Organism_name3'][0] - # elif dcf.shape[1] == 12: - # # dcf.columns = ['Organism_name1', - # # 'Organism_name2', - # # 'Genome1_size', - # # 'Genome2_size', - # # 'nb_bp_aligned_genome1', - # # 'nb_bp_aligned_genome2', - # # 'nb_ancient_bp_aligned_genome1', - # # 'nb_ancient_bp_aligned_genome2', - # # 'normalized_nb_ancient_bp_aligned_genome1', - # # 'normalized_nb_ancient_bp_aligned_genome2', - # # 'NormalizedReadRatio_1', - # # 'NormalizedReadRatio_2'] - # orga1 = dcf['Organism_name1'][0] - # orga2 = dcf['Organism_name2'][0] - # orga3 = None - # elif dcf.shape[1] == 19: - # # dcf.columns = ['Organism_name1', - # # 'Organism_name2', - # # 'Organism_name3', - # # 'Genome1_size', - # # 'Genome2_size', - # # 'Genome3_size', - # # 'nb_bp_aligned_genome1', - # # 'nb_bp_aligned_genome2', - # # 'nb_bp_aligned_genome3', - # # 'nb_ancient_bp_aligned_genome1', - # # 'nb_ancient_bp_aligned_genome2', - # # 'nb_ancient_bp_aligned_genome3', - # # 'normalized_nb_ancient_bp_aligned_genome1', - # # 'normalized_nb_ancient_bp_aligned_genome2', - # # 'normalized_nb_ancient_bp_aligned_genome3', - # # 'NormalizedReadRatio_1', - # # 'NormalizedReadRatio_2', - # # 'NormalizedReadRatio_3'] - # orga1 = dcf['Organism_name1'][0] - # orga2 = dcf['Organism_name2'][0] - # orga3 = dcf['Organism_name3'][0] dsp = pd.read_csv(SP, index_col=0).T From a4d5f4168f31c8f28291f5c293b07f5921367125 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 17:08:56 +0200 Subject: [PATCH 13/96] add PR #14 ref to changelog --- CHANGELOG.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 97590bf..31ad55c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,8 +2,8 @@ ## v1.1dev -- Update mapped basepair count to be quicker and include it in report -- Remove outdated scripts +- Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14) +- Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) - Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) ## v1.0 - 2019-04-26 From 0fe13a1b3f3999e9d820c33f41fcc9d500037f26 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 17:20:39 +0200 Subject: [PATCH 14/96] env and docker update for v1.1 --- Dockerfile | 2 +- environment.yml | 3 ++- nextflow.config | 4 ++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/Dockerfile b/Dockerfile index ad6477c..b656cd2 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,4 +4,4 @@ LABEL authors="Maxime Borry" \ COPY environment.yml / RUN conda env create -f /environment.yml && conda clean -a -ENV PATH /opt/conda/envs/nf-core-coproid-1.0/bin:$PATH +ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH diff --git a/environment.yml b/environment.yml index 2e57c6d..427c23e 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: nf-core-coproid-1.0 +name: nf-core-coproid-1.1dev channels: - bioconda - conda-forge @@ -33,3 +33,4 @@ dependencies: - anaconda::scipy=1.2.1 - bioconda::multiqc=1.7 - conda-forge::r-markdown=0.9 + - conda-forge::jupyter_contrib_nbextensions=0.5.1 diff --git a/nextflow.config b/nextflow.config index eef8c5f..3ee6c3d 100644 --- a/nextflow.config +++ b/nextflow.config @@ -61,7 +61,7 @@ params { } // Container slug. Stable releases should specify release tag! // Developmental code should specify :dev -process.container = 'nfcore/coproid:1.0' +process.container = 'nfcore/coproid:dev' // Load base.config by default for all pipelines includeConfig 'conf/base.config' @@ -119,7 +119,7 @@ manifest { description = 'Coprolite Identification' mainScript = 'main.nf' nextflowVersion = '>=0.32.0' - version = '1.0' + version = '1.1dev' } // Function to ensure that resource requirements don't go beyond // a maximum limit From acb9e54acfa2b0b9829e026c52582ed19162ba78 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 17 May 2019 17:20:59 +0200 Subject: [PATCH 15/96] travis update for v1.1dev --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 177c572..6353fec 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,7 @@ before_install: # Fake the tag locally so that the pipeline runs properly # Looks weird when this is :dev to :dev, but makes sense when testing code for a release (:dev to :1.0.1) # - docker tag nfcore/coproid:dev nfcore/coproid:dev - - docker tag nfcore/coproid:dev nfcore/coproid:1.0 + - docker tag nfcore/coproid:dev nfcore/coproid:dev install: # Install Nextflow From 4e4f687073a659af9e887c154e9f3c7000149be1 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 24 May 2019 16:15:36 +0200 Subject: [PATCH 16/96] fix erroneous doc on index params --- docs/usage.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/usage.md b/docs/usage.md index 9b8c3b7..3b244a1 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -350,7 +350,7 @@ Name of iGenomes reference for candidate organism 3. Must be provided if \`fasta Path to Bowtie2 index genome candidate 2 Coprolite maker's genome ```bash ---index1 'path/to/bt_index/basename*' +--index1 'path/to/bt_index/basename' ``` ### `--index2` @@ -358,7 +358,7 @@ Path to Bowtie2 index genome candidate 2 Coprolite maker's genome Path to Bowtie2 index genome candidate 2 Coprolite maker's genome ```bash ---index2 'path/to/bt_index/basename*' +--index2 'path/to/bt_index/basename' ``` ### `--index3` @@ -366,7 +366,7 @@ Path to Bowtie2 index genome candidate 2 Coprolite maker's genome Path to Bowtie2 index genome candidate 3 Coprolite maker's genome ```bash ---index3 'path/to/bt_index/basename*' +--index3 'path/to/bt_index/basename' ``` ## Job resources From fe891d05e1a1cbd40b03719a5e1b679225d4e7ce Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 24 May 2019 16:34:47 +0200 Subject: [PATCH 17/96] add more details in doc for krakendb --- docs/usage.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/usage.md b/docs/usage.md index 3b244a1..0d1bf75 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -104,7 +104,12 @@ Name of the second candidate species. Example : `"Canis_familiaris"` ### `--krakenDB` -Path to Path to Kraken2 MiniKraken2_v2_8GB Database. Can be downloaded [here](https://ccb.jhu.edu/software/kraken2/dl/old/minikraken2_v2_8GB.tgz) +Path to the directory containing the Kraken2 MiniKraken2_v2_8GB database files. +The MiniKraken2_v2_8GB database can be downloaded [here](https://ccb.jhu.edu/software/kraken2/dl/old/minikraken2_v2_8GB.tgz) + +```bash +--krakendb "path/to/kraken2_db_dir" +``` ## Reference genomes From bb6e4f6edc6f3992454cf89821b5026de4e01ae4 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 24 May 2019 17:06:04 +0200 Subject: [PATCH 18/96] remove errorSrategy from main.nf --- main.nf | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/main.nf b/main.nf index c0751c9..dd64942 100644 --- a/main.nf +++ b/main.nf @@ -607,8 +607,6 @@ process bam2fq { label 'intenso' - errorStrategy 'ignore' - input: set val(name), file(bam) from unaligned_genome1 output: @@ -1069,7 +1067,7 @@ if (params.name3 == ''){ } -// 5: MapDamage +// 5: damageprofiler if (params.adna){ process damageprofilerGenome1 { @@ -1077,8 +1075,6 @@ if (params.adna){ label 'ristretto' - errorStrategy 'ignore' - publishDir "${params.outdir}/damageprofiler/${params.name1}", mode: 'copy' input: @@ -1106,8 +1102,6 @@ if (params.adna){ label 'ristretto' - errorStrategy 'ignore' - publishDir "${params.outdir}/damageprofiler/${params.name2}", mode: 'copy' input: @@ -1136,8 +1130,6 @@ if (params.adna){ label 'ristretto' - errorStrategy 'ignore' - publishDir "${params.outdir}/damageprofiler/${params.name3}", mode: 'copy' input: @@ -1313,8 +1305,6 @@ process multiqc { label 'ristretto' - errorStrategy 'ignore' - publishDir "${params.outdir}", mode: 'copy' input: From fcabcdbc8de972f95a44c0a68b473c2bb9be564f Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 24 May 2019 17:16:11 +0200 Subject: [PATCH 19/96] update maxRetries to 2 --- conf/base.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/conf/base.config b/conf/base.config index cfc672a..b0031e7 100644 --- a/conf/base.config +++ b/conf/base.config @@ -15,7 +15,7 @@ process { time = { check_max( 30.m * task.attempt, 'time' )} errorStrategy = { task.exitStatus in [143,137,104,134,139] ? 'retry' : 'finish' } - maxRetries = 1 + maxRetries = 2 maxErrors = '-1' // Process-specific resource requirements From 4816cb982c94fca1d1faf4bccc7546f4653b2b25 Mon Sep 17 00:00:00 2001 From: Maxime Date: Sat, 25 May 2019 18:24:55 +0200 Subject: [PATCH 20/96] update default parameters --- conf/base.config | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/conf/base.config b/conf/base.config index b0031e7..59202f2 100644 --- a/conf/base.config +++ b/conf/base.config @@ -25,12 +25,18 @@ process { withLabel:expresso{ cpus = { check_max(6 * task.attempt, 'cpus')} memory = { check_max( 2.GB * task.attempt, 'memory' )} + time = { check_max( 45.m * task.attempt, 'time' )} } withLabel:intenso{ cpus = { check_max(8 * task.attempt, 'cpus')} memory = { check_max( 8.GB * task.attempt, 'memory' )} } + + withLabel:elephanto{ + cpus = { check_max(16 * task.attempt, 'cpus')} + memory = { check_max( 32.GB * task.attempt, 'memory' )} + } } params { From 71359af9b1b6ff1c0b61ceb9e893c36c0646d13e Mon Sep 17 00:00:00 2001 From: Maxime Date: Sat, 25 May 2019 18:26:00 +0200 Subject: [PATCH 21/96] update process requirements --- main.nf | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/main.nf b/main.nf index dd64942..1c7a279 100644 --- a/main.nf +++ b/main.nf @@ -874,7 +874,7 @@ process kraken_merge { process sourcepredict { - label 'intenso' + label 'elephanto' input: file(otu_table) from kraken_merged @@ -1461,10 +1461,10 @@ workflow.onComplete { c_green = params.monochrome_logs ? '' : "\033[0;32m"; c_red = params.monochrome_logs ? '' : "\033[0;31m"; - if (workflow.stats.ignoredCountFmt > 0 && workflow.success) { + if (workflow.stats.ignoredCount > 0 && workflow.success) { log.info "${c_purple}Warning, pipeline completed, but with errored process(es) ${c_reset}" - log.info "${c_red}Number of ignored errored process(es) : ${workflow.stats.ignoredCountFmt} ${c_reset}" - log.info "${c_green}Number of successfully ran process(es) : ${workflow.stats.succeedCountFmt} ${c_reset}" + log.info "${c_red}Number of ignored errored process(es) : ${workflow.stats.ignoredCount} ${c_reset}" + log.info "${c_green}Number of successfully ran process(es) : ${workflow.stats.succeedCount} ${c_reset}" } if(workflow.success){ From 99d85bfb2da36051cd98b1104b8a30027ad3a898 Mon Sep 17 00:00:00 2001 From: Maxime Date: Sun, 26 May 2019 13:23:51 +0200 Subject: [PATCH 22/96] fix normalized read ratio computation --- bin/normalizedReadCount | 35 ++++++++++++++++------------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/bin/normalizedReadCount b/bin/normalizedReadCount index b8a84db..c605ccb 100755 --- a/bin/normalizedReadCount +++ b/bin/normalizedReadCount @@ -434,41 +434,38 @@ if __name__ == "__main__": if ABAM1 and ABAM2: # Two genomes anb1 = get_total_bp(abam1_res, acommonReads) - nnbp1 = (anb1 + 1) / gs1 + nnbp1 = ((anb1 + 1) / gs1) * (1/ENDO1) anb2 = get_total_bp(abam2_res, acommonReads) - nnbp2 = (anb2 + 1) / gs2 + nnbp2 = ((anb2 + 1) / gs2) * (1/ENDO2) print("nb1_ancient", anb1) print("nb2_ancient", anb2) - NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) * ENDO1 - NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) * ENDO2 + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) if BAM3: # Three genomes anb3 = get_total_bp(abam3_res, acommonReads) - nnbp3 = (anb3 + 1) / gs3 + nnbp3 = ((anb3 + 1) / gs3) * (1/ENDO3) print("nb3_ancient", anb3) - NormalizedReadRatio_1 = ( - nnbp1 / (nnbp1 + nnbp2 + nnbp3)) * ENDO1 - NormalizedReadRatio_2 = ( - nnbp2 / (nnbp1 + nnbp2 + nnbp3)) * ENDO2 - NormalizedReadRatio_3 = ( - nnbp3 / (nnbp1 + nnbp2 + nnbp3)) * ENDO3 + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2 + nnbp3)) + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2 + nnbp3)) + NormalizedReadRatio_3 = (nnbp3 / (nnbp1 + nnbp2 + nnbp3)) else: # Two genomes - nnbp1 = (nb1 + 1) / gs1 - nnbp2 = (nb2 + 1) / gs2 + nnbp1 = ((nb1 + 1) / gs1) * (1/ENDO1) + nnbp2 = ((nb2 + 1) / gs2) * (1/ENDO2) - NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) * ENDO1 - NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) * ENDO2 + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2)) + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2)) if BAM3: # Three genomes - nnbp3 = (nb3 + 1) / gs3 + nnbp3 = ((nb3 + 1) / gs3) * (1/ENDO3) - NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2 + nnbp3)) * ENDO1 - NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2 + nnbp3)) * ENDO2 - NormalizedReadRatio_3 = (nnbp3 / (nnbp1 + nnbp2 + nnbp3)) * ENDO3 + NormalizedReadRatio_1 = (nnbp1 / (nnbp1 + nnbp2 + nnbp3)) + NormalizedReadRatio_2 = (nnbp2 / (nnbp1 + nnbp2 + nnbp3)) + NormalizedReadRatio_3 = (nnbp3 / (nnbp1 + nnbp2 + nnbp3)) if ABAM1 and ABAM2: if not BAM3 and not ABAM3: From a53936a41e3babc954a69c51316dc0a038443101 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Jun 2019 17:21:07 +0200 Subject: [PATCH 23/96] adding rmd report --- environment.yml | 2 +- templates/rmd/coproid_report.Rmd | 90 ++++++++++++++++++++++++++++++++ 2 files changed, 91 insertions(+), 1 deletion(-) create mode 100644 templates/rmd/coproid_report.Rmd diff --git a/environment.yml b/environment.yml index 427c23e..e360f0b 100644 --- a/environment.yml +++ b/environment.yml @@ -31,6 +31,6 @@ dependencies: - anaconda::numpy=1.16.3 - anaconda::pandas=0.24.2 - anaconda::scipy=1.2.1 - - bioconda::multiqc=1.7 + - bioconda::multiqc=1.7=py_3 - conda-forge::r-markdown=0.9 - conda-forge::jupyter_contrib_nbextensions=0.5.1 diff --git a/templates/rmd/coproid_report.Rmd b/templates/rmd/coproid_report.Rmd new file mode 100644 index 0000000..04e2e1b --- /dev/null +++ b/templates/rmd/coproid_report.Rmd @@ -0,0 +1,90 @@ +--- +title: "coproID run report" +output: html_document +--- + +# coproID report + +Report generated on the `r Sys.Date()` + +![](coproID_nf-core_logo_small.png) + +## Introduction +[coproID](https://github.com/nf-core/coproID) is a pipeline to identify the source of coprolites, and in general, of a metagenomic sample. + +If you read these lines, coproID successfully finished running and you can find your results below. +You can find more informations about the different result files in the coproID documentation: [coproid.readthedocs.io/](https://coproid.readthedocs.io/en/latest/output.html) + + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +require(ggplot2) +require(rmarkdown) +require(magrittr) +require(DT) +require(plotly) +require(gridExtra) +``` + + +```{r, echo=FALSE, include=FALSE} +d = read.csv('coproID_result.csv', row.names = 1) +``` + +## coproID summary table + +```{r, table1, echo=FALSE} +# d %>% +# knitr::kable(format = "html", col.names = colnames(d)) %>% +# kableExtra::kable_styling() %>% +# kableExtra::scroll_box(width = "100%", height = "400px") +d %>% + datatable( + extensions = 'Buttons', + width = '80%', + options = list(dom = 'Bfrtip', + buttons = c('excel', "csv"), + autowidth='True'), + caption='coproID summary table') +``` + + +## Microbiome composition embedding + + +```{r, echo=FALSE} +e = read.csv("sourcepredict_embedding.csv", row.names = 1) +e['ml'] = as.factor(ifelse(e["labels"] == 'sink', "reference", "test")) + + +g = ggplot(data=e, mapping = aes(x=PC1, y=PC2, label=name)) + geom_point(aes(color=labels, shape=ml)) + scale_color_discrete(name='Organism') + scale_shape_discrete(name='Reference') + theme_classic() + labs(x='DIM1', y='DIM2') +ggplotly(g) +``` + +## Damage profiles + +```{r, echo=FALSE, results='asis'} +files <- list.files(pattern = "\\_freq.txt$") + +for (i in seq(1, length(files))){ + # print(files[i]) + afile = files[i] + spt = strsplit(as.character(afile), "[.]") + samp_name = spt[[1]][1] + cat('\n') + cat("# ", samp_name, "\n") + fwd = paste(append(samp_name, "5pCtoT_freq.txt"), collapse = ".") + fwd = read.csv(fwd, skip=3, sep="\t", col.names = c('pos','X5pCtoT')) + rev = paste(append(samp_name, "3pGtoA_freq.txt"), collapse = ".") + rever = read.csv(rev, skip=3, sep="\t", col.names = c('pos','X3pGtoA')) + rever$pos = rev(rever$pos * -1) + rever$X3pGtoA = rev(rever$X3pGtoA) + + f = ggplot(fwd, aes(x=pos,y=X5pCtoT)) + geom_line() + labs(title='5p') + r = ggplot(rever, aes(x=pos,y=X3pGtoA)) + geom_line() + labs(title='3p') + grid.arrange(f, r, nrow = 1) + cat('\n') +} + +``` + From cb53480d7d0b9ad0029c9dfbfd3c897a2cb19521 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Jun 2019 17:30:54 +0200 Subject: [PATCH 24/96] updating rmd report --- templates/rmd/coproid_report.Rmd | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/templates/rmd/coproid_report.Rmd b/templates/rmd/coproid_report.Rmd index 04e2e1b..a805dc6 100644 --- a/templates/rmd/coproid_report.Rmd +++ b/templates/rmd/coproid_report.Rmd @@ -66,13 +66,21 @@ ggplotly(g) ```{r, echo=FALSE, results='asis'} files <- list.files(pattern = "\\_freq.txt$") +samp_names = c() for (i in seq(1, length(files))){ # print(files[i]) afile = files[i] spt = strsplit(as.character(afile), "[.]") samp_name = spt[[1]][1] + samp_names = append(samp_names, samp_name) +} +samp_names = unique(samp_names) + +for (i in seq(1, length(samp_names))){ + samp_name = samp_names[i] + print(samp_name) cat('\n') - cat("# ", samp_name, "\n") + cat("### Sample: ", strsplit(samp_name, "_otu_")[[1]][1]," - species: ", strsplit(samp_name, "_otu_")[[1]][2], "\n") fwd = paste(append(samp_name, "5pCtoT_freq.txt"), collapse = ".") fwd = read.csv(fwd, skip=3, sep="\t", col.names = c('pos','X5pCtoT')) rev = paste(append(samp_name, "3pGtoA_freq.txt"), collapse = ".") @@ -80,8 +88,8 @@ for (i in seq(1, length(files))){ rever$pos = rev(rever$pos * -1) rever$X3pGtoA = rev(rever$X3pGtoA) - f = ggplot(fwd, aes(x=pos,y=X5pCtoT)) + geom_line() + labs(title='5p') - r = ggplot(rever, aes(x=pos,y=X3pGtoA)) + geom_line() + labs(title='3p') + f = ggplot(fwd, aes(x=pos,y=X5pCtoT)) + geom_line() + labs(title='5pC>t', y="") + r = ggplot(rever, aes(x=pos,y=X3pGtoA)) + geom_line() + labs(title='3pG>A', y = "") grid.arrange(f, r, nrow = 1) cat('\n') } From 44b21ec74338fb32eecac97a984df7b87fc7462d Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 14 Jun 2019 16:47:38 +0200 Subject: [PATCH 25/96] update coproid report --- templates/coproID_report.ipynb | 1981 ++------------------------------ 1 file changed, 85 insertions(+), 1896 deletions(-) diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 14c1e90..5df9536 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -9,25 +9,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "remove_cell" ] }, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'version.txt'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"version.txt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Version {line.rstrip()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Report generated on {datetime.datetime.now()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'version.txt'" - ] - } - ], + "outputs": [], "source": [ "import datetime\n", "with open(\"version.txt\", \"r\") as f:\n", @@ -56,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -68,6 +56,7 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import os\n", + "from IPython.display import display, Markdown, Latex\n", "from bokeh.plotting import figure, show, output_notebook\n", "from bokeh.models import ColumnDataSource\n", "from bokeh.transform import factor_cmap\n", @@ -76,12 +65,15 @@ "from bokeh.layouts import widgetbox\n", "from bokeh.models.widgets import Button\n", "from bokeh.models.widgets import DataTable, DateFormatter, TableColumn\n", - "from bokeh.models import CustomJS" + "from bokeh.models import CustomJS\n", + "from plotnine import *\n", + "import warnings\n", + "warnings.simplefilter('ignore')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -106,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -116,7 +108,8 @@ "source": [ "def bokeh_table(df):\n", " \n", - " d = pd.read_csv(df, index_col = 0)\n", + " d = pd.read_csv(df, index_col=0)\n", + " d.insert(0, \"sample\", d.index)\n", " source = ColumnDataSource(d)\n", "\n", " Columns = [TableColumn(field=Ci, title=Ci) for Ci in d.columns] # bokeh columns\n", @@ -165,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -180,843 +173,90 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1- coproID summary table" + "## 1- coproID summary\n", + "### Table" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "tags": [ "remove_cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " normalized_bp_proportion_aligned_Homo_sapiens \\\n", - "Z3 0.857235 \n", - "Z9C 0.802099 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.018553 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.906477 \n", - "Zape28A_MinE2X 0.853615 \n", - "Zape28A_MinE 0.865285 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.862480 \n", - "Zape28B_MinE2X 0.896460 \n", - "Zape28B_MinE 0.875937 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.043645 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.028461 \n", - "Zape2A_MinE2X 0.018443 \n", - "Zape2A_MinE 0.020043 \n", - "Zape2B_MinE2X 0.017746 \n", - "Zape_31_TCACGTTC-CGACACTT 0.875926 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.945436 \n", - "Zape5A_MinE2X 0.967882 \n", - "Zape5A_MinE 0.966209 \n", - "Zape5B_MinE2X 0.968354 \n", - "Zape5B_MinE 0.979624 \n", - "\n", - " normalized_bp_proportion_aligned_Canis_familiaris \\\n", - "Z3 0.084852 \n", - "Z9C 0.081930 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.945514 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.053226 \n", - "Zape28A_MinE2X 0.122085 \n", - "Zape28A_MinE 0.114465 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.094690 \n", - "Zape28B_MinE2X 0.075733 \n", - "Zape28B_MinE 0.107109 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.922545 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.928157 \n", - "Zape2A_MinE2X 0.950826 \n", - "Zape2A_MinE 0.951501 \n", - "Zape2B_MinE2X 0.960561 \n", - "Zape_31_TCACGTTC-CGACACTT 0.064774 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.033044 \n", - "Zape5A_MinE2X 0.020839 \n", - "Zape5A_MinE 0.017509 \n", - "Zape5B_MinE2X 0.019224 \n", - "Zape5B_MinE 0.012064 \n", - "\n", - " normalized_bp_aligned_Sus_scrofa \\\n", - "Z3 0.057913 \n", - "Z9C 0.115971 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.035933 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.040296 \n", - "Zape28A_MinE2X 0.024300 \n", - "Zape28A_MinE 0.020250 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.042830 \n", - "Zape28B_MinE2X 0.027808 \n", - "Zape28B_MinE 0.016954 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.033810 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.043381 \n", - "Zape2A_MinE2X 0.030731 \n", - "Zape2A_MinE 0.028456 \n", - "Zape2B_MinE2X 0.021693 \n", - "Zape_31_TCACGTTC-CGACACTT 0.059300 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.021521 \n", - "Zape5A_MinE2X 0.011279 \n", - "Zape5A_MinE 0.016282 \n", - "Zape5B_MinE2X 0.012422 \n", - "Zape5B_MinE 0.008311 \n", - "\n", - " metagenomic_proportion_Homo_sapiens \\\n", - "Z3 1.0 \n", - "Z9C 1.0 \n", - "Zape_23_ACACGGTT-ACCGACAA 1.0 \n", - "Zape_25_CTTACCTG-AGTGGCAA 1.0 \n", - "Zape28A_MinE2X 1.0 \n", - "Zape28A_MinE 1.0 \n", - "Zape_28B_ACACGGTT-GACTTGTG 1.0 \n", - "Zape28B_MinE2X 1.0 \n", - "Zape28B_MinE 1.0 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 1.0 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.0 \n", - "Zape2A_MinE2X 0.0 \n", - "Zape2A_MinE 0.0 \n", - "Zape2B_MinE2X 0.0 \n", - "Zape_31_TCACGTTC-CGACACTT 1.0 \n", - "Zape_5A_ACACGGTT-CACAGACT 1.0 \n", - "Zape5A_MinE2X 1.0 \n", - "Zape5A_MinE 1.0 \n", - "Zape5B_MinE2X 1.0 \n", - "Zape5B_MinE 1.0 \n", - "\n", - " metagenomic_proportion_Canis_familiaris \\\n", - "Z3 0.0 \n", - "Z9C 0.0 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.0 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.0 \n", - "Zape28A_MinE2X 0.0 \n", - "Zape28A_MinE 0.0 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.0 \n", - "Zape28B_MinE2X 0.0 \n", - "Zape28B_MinE 0.0 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.0 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.0 \n", - "Zape2A_MinE2X 0.0 \n", - "Zape2A_MinE 0.0 \n", - "Zape2B_MinE2X 0.0 \n", - "Zape_31_TCACGTTC-CGACACTT 0.0 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.0 \n", - "Zape5A_MinE2X 0.0 \n", - "Zape5A_MinE 0.0 \n", - "Zape5B_MinE2X 0.0 \n", - "Zape5B_MinE 0.0 \n", - "\n", - " metagenomic_proportion_Sus_scrofa \\\n", - "Z3 0.0 \n", - "Z9C 0.0 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.0 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.0 \n", - "Zape28A_MinE2X 0.0 \n", - "Zape28A_MinE 0.0 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.0 \n", - "Zape28B_MinE2X 0.0 \n", - "Zape28B_MinE 0.0 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.0 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 1.0 \n", - "Zape2A_MinE2X 1.0 \n", - "Zape2A_MinE 1.0 \n", - "Zape2B_MinE2X 1.0 \n", - "Zape_31_TCACGTTC-CGACACTT 0.0 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.0 \n", - "Zape5A_MinE2X 0.0 \n", - "Zape5A_MinE 0.0 \n", - "Zape5B_MinE2X 0.0 \n", - "Zape5B_MinE 0.0 \n", - "\n", - " coproID_proba_Homo_sapiens \\\n", - "Z3 0.857235 \n", - "Z9C 0.802099 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.018553 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.906477 \n", - "Zape28A_MinE2X 0.853615 \n", - "Zape28A_MinE 0.865285 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.862480 \n", - "Zape28B_MinE2X 0.896460 \n", - "Zape28B_MinE 0.875937 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.043645 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.000000 \n", - "Zape2A_MinE2X 0.000000 \n", - "Zape2A_MinE 0.000000 \n", - "Zape2B_MinE2X 0.000000 \n", - "Zape_31_TCACGTTC-CGACACTT 0.875926 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.945436 \n", - "Zape5A_MinE2X 0.967882 \n", - "Zape5A_MinE 0.966209 \n", - "Zape5B_MinE2X 0.968354 \n", - "Zape5B_MinE 0.979624 \n", - "\n", - " coproID_proba_Canis_familiaris \\\n", - "Z3 0.0 \n", - "Z9C 0.0 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.0 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.0 \n", - "Zape28A_MinE2X 0.0 \n", - "Zape28A_MinE 0.0 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.0 \n", - "Zape28B_MinE2X 0.0 \n", - "Zape28B_MinE 0.0 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.0 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.0 \n", - "Zape2A_MinE2X 0.0 \n", - "Zape2A_MinE 0.0 \n", - "Zape2B_MinE2X 0.0 \n", - "Zape_31_TCACGTTC-CGACACTT 0.0 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.0 \n", - "Zape5A_MinE2X 0.0 \n", - "Zape5A_MinE 0.0 \n", - "Zape5B_MinE2X 0.0 \n", - "Zape5B_MinE 0.0 \n", - "\n", - " coproID_proba_Sus_scrofa \n", - "Z3 0.000000 \n", - "Z9C 0.000000 \n", - "Zape_23_ACACGGTT-ACCGACAA 0.000000 \n", - "Zape_25_CTTACCTG-AGTGGCAA 0.000000 \n", - "Zape28A_MinE2X 0.000000 \n", - "Zape28A_MinE 0.000000 \n", - "Zape_28B_ACACGGTT-GACTTGTG 0.000000 \n", - "Zape28B_MinE2X 0.000000 \n", - "Zape28B_MinE 0.000000 \n", - "Zape_29_Redo_CGTTGCAA-CACAGACT 0.000000 \n", - "Zape_2A__GTTAAGGC-ACCGACAA 0.043381 \n", - "Zape2A_MinE2X 0.030731 \n", - "Zape2A_MinE 0.028456 \n", - "Zape2B_MinE2X 0.021693 \n", - "Zape_31_TCACGTTC-CGACACTT 0.000000 \n", - "Zape_5A_ACACGGTT-CACAGACT 0.000000 \n", - "Zape5A_MinE2X 0.000000 \n", - "Zape5A_MinE 0.000000 \n", - "Zape5B_MinE2X 0.000000 \n", - "Zape5B_MinE 0.000000 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "d = \"coproID_result.csv\"\n", - "pd.read_csv(d, index_col = 0)" + "pd.read_csv(d, index_col=0)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "tags": [ "remove_cell" ] }, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = {\"09c5aef3-a6e9-45d3-b499-4b6bbea26a49\":{\"roots\":{\"references\":[{\"attributes\":{\"button_type\":\"success\",\"callback\":{\"id\":\"1015\",\"type\":\"CustomJS\"},\"icon\":null,\"label\":\"Download\"},\"id\":\"1014\",\"type\":\"Button\"},{\"attributes\":{},\"id\":\"1016\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1017\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"args\":{\"source\":{\"id\":\"1001\",\"type\":\"ColumnDataSource\"}},\"code\":\"\\n function table_to_csv(source) {\\n const columns = Object.keys(source.data)\\n const nrows = source.get_length()\\n const lines = [columns.join(',')]\\n\\n for (let i = 0; i < nrows; i++) {\\n let row = [];\\n for (let j = 0; j < columns.length; j++) {\\n const column = columns[j]\\n row.push(source.data[column][i].toString())\\n }\\n lines.push(row.join(','))\\n }\\n return lines.join('\\\\n').concat('\\\\n')\\n }\\n\\n\\n const filename = 'coproID_result.csv'\\n filetext = table_to_csv(source)\\n const blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' })\\n\\n //addresses IE\\n if (navigator.msSaveBlob) {\\n navigator.msSaveBlob(blob, filename)\\n } else {\\n const link = document.createElement('a')\\n link.href = URL.createObjectURL(blob)\\n link.download = filename\\n link.target = '_blank'\\n link.style.visibility = 'hidden'\\n link.dispatchEvent(new MouseEvent('click'))\\n }\\n \"},\"id\":\"1015\",\"type\":\"CustomJS\"},{\"attributes\":{\"callback\":null,\"data\":{\"coproID_proba_Canis_familiaris\":{\"__ndarray__\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\",\"dtype\":\"float64\",\"shape\":[20]},\"coproID_proba_Homo_sapiens\":{\"__ndarray__\":\"zjC8/3du6z/o/waEyqrpP0X5h4F8/5I/46Rog9wB7T8p95T70FDrP9pF0stqsOs/++26VW+Z6z9bSPfgy6/sP00pQRmsB+w/cbxSfpZYpj8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALfE1PaWB+w/aPyVrAJB7j+IAT/U4vjuP/RqpKUv6+4/f1+2cMH87j9stOE5FVnvPw==\",\"dtype\":\"float64\",\"shape\":[20]},\"coproID_proba_Sus_scrofa\":{\"__ndarray__\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADmuqjAETamP5FTgxTUd58/TnbhrIQjnT9tpzAvqzaWPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\",\"dtype\":\"float64\",\"shape\":[20]},\"index\":[\"Z3\",\"Z9C\",\"Zape_23_ACACGGTT-ACCGACAA\",\"Zape_25_CTTACCTG-AGTGGCAA\",\"Zape28A_MinE2X\",\"Zape28A_MinE\",\"Zape_28B_ACACGGTT-GACTTGTG\",\"Zape28B_MinE2X\",\"Zape28B_MinE\",\"Zape_29_Redo_CGTTGCAA-CACAGACT\",\"Zape_2A__GTTAAGGC-ACCGACAA\",\"Zape2A_MinE2X\",\"Zape2A_MinE\",\"Zape2B_MinE2X\",\"Zape_31_TCACGTTC-CGACACTT\",\"Zape_5A_ACACGGTT-CACAGACT\",\"Zape5A_MinE2X\",\"Zape5A_MinE\",\"Zape5B_MinE2X\",\"Zape5B_MinE\"],\"metagenomic_proportion_Canis_familiaris\":{\"__ndarray__\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\",\"dtype\":\"float64\",\"shape\":[20]},\"metagenomic_proportion_Homo_sapiens\":{\"__ndarray__\":\"AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPw==\",\"dtype\":\"float64\",\"shape\":[20]},\"metagenomic_proportion_Sus_scrofa\":{\"__ndarray__\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADwPwAAAAAAAPA/AAAAAAAA8D8AAAAAAADwPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\",\"dtype\":\"float64\",\"shape\":[20]},\"normalized_bp_aligned_Sus_scrofa\":{\"__ndarray__\":\"W15Eo86mrT9oAbBKR7C9P2MrFEnYZaI/eqgLyrqhpD+FhKYPBOKYP/GrDa5KvJQ/q8Ju3N/tpT/bbFrlqnmcP2KVxLVzXJE/r2589ZJPoT/muqjAETamP5FTgxTUd58/TnbhrIQjnT9tpzAvqzaWP+vUgdOOXK4/q86Cu4EJlj+WvhfReRmHP8lwC7dJrJA/zA8pDd9wiT8D71i9ggWBPw==\",\"dtype\":\"float64\",\"shape\":[20]},\"normalized_bp_proportion_aligned_Canis_familiaris\":{\"__ndarray__\":\"Zkp8sNi4tT9W/xeVZPm0P4F9YpemQe4/PQlq/3xAqz+UpW4f90C/PzNm6vWWTb0/0S7xY5U9uD/xIe8+NmOzPz2QhUiCa7s/Tg3DaH2F7T+kOZ/5drPtP+PojEgrbe4/WhPm9LFy7j9pwBbV6bzuP9jvmN8AlbA/KNLeWBTroD/n75OP6FaVP7kwZ5TB7ZE/HIifYmKvkz8N9jnKLrWIPw==\",\"dtype\":\"float64\",\"shape\":[20]},\"normalized_bp_proportion_aligned_Homo_sapiens\":{\"__ndarray__\":\"zjC8/3du6z/o/waEyqrpP0X5h4F8/5I/46Rog9wB7T8p95T70FDrP9pF0stqsOs/++26VW+Z6z9bSPfgy6/sP00pQRmsB+w/cbxSfpZYpj/JVcdK/SSdPzSQ39nC4pI/hR5ctjyGlD9/S/ctGiySP7fE1PaWB+w/aPyVrAJB7j+IAT/U4vjuP/RqpKUv6+4/f1+2cMH87j9stOE5FVnvPw==\",\"dtype\":\"float64\",\"shape\":[20]}},\"selected\":{\"id\":\"1016\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1017\",\"type\":\"UnionRenderers\"}},\"id\":\"1001\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"1014\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n", - " var render_items = [{\"docid\":\"09c5aef3-a6e9-45d3-b499-4b6bbea26a49\",\"roots\":{\"1014\":\"8d96168d-5a32-47d8-a586-ce34e0cadb24\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "1014" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "bokeh_table(d)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "def coproid_summary_plot(df):\n", + " df = pd.read_csv(df, index_col=0)\n", + " organisms = [i.replace(\"normalized_bp_proportion_aligned_\",\"\") for i in list(df.columns) if \"normalized_bp_proportion_aligned_\" in i]\n", + " organisms_clean = [i.replace(\"_\",\" \") for i in organisms]\n", + " if len(organisms_clean) < 3:\n", + " display(Markdown(\"### Plot\"))\n", + " species_text = pd.DataFrame()\n", + " species_text['x'] = [0.25, 0.75, 0.75, 0.25]\n", + " species_text['y'] = [0.25, 0.25, 0.75, 0.75]\n", + " species_text['text'] = ['Unknown', organisms_clean[0], 'Unknown', organisms_clean[1]]\n", + " \n", + " df['samp_name'] = df.index\n", + " df['coproID_prediction'] = ['Unknown'] * df.shape[0]\n", + " df['coproID_prediction'] = np.where(df[f\"coproID_proba_{organisms[0]}\"] > 0.5, organisms_clean[0], df['coproID_prediction'])\n", + " df['coproID_prediction'] = np.where(df[f\"coproID_proba_{organisms[1]}\"] > 0.5, organisms_clean[1], df['coproID_prediction'])\n", + " \n", + " p = ggplot(df, aes(x = f\"coproID_proba_{organisms[0]}\",y = f\"coproID_proba_{organisms[1]}\"))\n", + " p = p + geom_point(aes(color='coproID_prediction'), size=2)\n", + " p = p + geom_label(aes(label=\"samp_name\", color='coproID_prediction'), size=8, nudge_x = 0.02, ha='left', va='bottom')\n", + " p = p + theme_classic() + labs(x=f\"coproID proba {organisms_clean[0]}\",y = f\"coproID proba {organisms_clean[1]}\")\n", + " p = p + geom_text(data=species_text, mapping=aes(x='x',y='y', label='text'), alpha=0.3, color='grey')\n", + " p = p + geom_hline(yintercept=0.5, linetype='dashed', alpha=0.1) \n", + " p = p + geom_vline(xintercept=0.5, linetype='dashed', alpha=0.1)\n", + " p = p + scale_color_manual(name='Predicted Organism', values = {organisms_clean[0]:'#ef7576', organisms_clean[1]:'#4daf49', 'Unknown':'#a2a3a1'})\n", + " p = p + coord_cartesian(xlim=[0,1],ylim=[0,1])\n", + " p.draw()\n", + " else:\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "coproid_summary_plot(d)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1026,339 +266,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "scrolled": true, "tags": [ "remove_cell" ] }, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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"plot_bokeh(umap)" ] @@ -1372,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -1391,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -1404,7 +319,8 @@ " r = pd.read_csv(rev, skiprows=3, delimiter=\"\\t\")\n", " r['pos'] = list(r['pos']*-1)[::-1]\n", " fig = plt.figure(figsize=(18,3))\n", - " fig.suptitle(f\"Sample: {sample} - Species: {organism}\", fontsize=\"x-large\", fontweight='bold', y = 1.2)\n", + " fig.suptitle(f\"Sample: {sample} - Species: {organism.replace('_',' ')}\", fontsize=\"x-large\", fontweight='bold', y = 1.2)\n", + " display(Markdown(f\"- **Sample**: {sample} - **Species**: *{organism.replace('_',' ')}*\"))\n", " plt.title('Test')\n", " plt.subplot(1, 2, 1)\n", " plt.plot(f['pos'],f['5pC>T'])\n", @@ -1416,747 +332,20 @@ " plt.plot(r['pos'][::-1],r['3pG>A'], color = 'red')\n", " plt.title('3pG>A')\n", " plt.ylabel('Frequency')\n", - " plt.xticks(r['pos'])" + " plt.xticks(r['pos'])\n", + " plt.show()\n", + " " ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "tags": [ "remove_cell" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/projects1/users/borry/15_miniconda3/envs/coproid/lib/python3.6/site-packages/matplotlib/pyplot.py:522: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " max_open_warning, RuntimeWarning)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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hIiIiIiIiImmlYoSIiEgDmNll1S5puc0Udz6RhjCzu+v4Ps+LO5+IiLRe6qYhIiLSAGbWlTC4Yo3qu8ymSHNiZj0I43jUpMzdP01nHhERaTtUjBARERERERGRtFI3DRERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQERERERERkbRSMUJERERERERE0krFCBERERERERFJKxUjRERERERERCStVIwQaYPM7FUzKzazjdG0sAGv/Y6Z/dPMisxstZm9ZmbjGvD6yxLet9jMKhIez2vcFomIiEhLYmaPmNlyM9tgZh+Y2cQGvHa0mT1vZmvNbJ2ZvW9mPzezLo3M8g0zczO7qDGvF5HGUTFCpO2a7O750bR7Mi8ws2OBp4CHgb5AT+BK4Ijo+QIza1/XOtz9hqr3Bc4C3kzIsceObJCIiIi0GDcC/d29IzAOuN7M9qnvRWZ2APAq8AYwxN07A4cC5cCIaJl690eqOQVYE92KSJqoGCEiW5nZqWb2hpn91szWm9kCM/tW9JwBtwLXuft97r7e3Svd/TV3PyNaxZ7AMjO7x8z2i2s7REREpHlz93nuXlL1MJoGRa0UlkQtKb8ws8VmdkLCS28CHnT3G919ZbSuz9z9Knd/NVom6f0RM+sAHAv8GBhsZqObcDNFpA4qRoi0XTdG/8m/YWbfSJi/L/Ax0B24CnjGzLoCuwM7A0/XtkJ3fxMYBSwDHjWz+WZ2kZn1TtVGiIiISMtkZnea2WZgAbAcmB491YuwH7ITobXCvWa2u5nlAfsDf6xrvQ3cHzkG2Eho+fkycPKOb5mIJEPFCJG26WJgIOE/+XuB58xsUPTcKuDX7l7m7k8AC4H/A7pFzy+va8Xu/om7XwPsCpwJDAHej/p29mv6TREREZGWyN1/BBQAXweeAUoSnr7C3Uvc/TXgBeA4oAvh+GVF1UJmdlM0bsQmM/tZwrqT3R85BXjC3SuAx4DjzSw7FdsrIttSMUKkDXL3t9y9KPpP/veEfpeHRU8vdXdPWPxToA/wZfQ4qVYO0TrmA7OBJcAeQF5T5BcREZHWwd0r3P11wlhUZ0ez17r7poTFqvZF1gKVJOyLuPtF0bgRfwKyalh/rfsjZrYzcDDwaLT4s0A7wkkYEUkxFSNEBEI/TYvu7xSND1GlH6GZ40Lgc0JzxlqZWa6ZHWtm04APgX2Ac4GB7j6/yZOLiIhIa5AFVLXS7BJ1yajSD1gWFSjeAo6ub2VJ7o+cRDgees7MVhC6qbZDXTVE0kLFCJE2xsw6R5fnbGdmWdGgUAcR+kkC9ADONbNsMxsPDAWmR2cWLgCuMLPTzKyjmWWY2YFmdm+07uGEbhznEc4u7OzuJ7v7P6q1thAREZE2ysx6mNkPzCzfzDLN7DvA8cDfExa7xsxyzOzrwOGEMR0ALgImmNklZtYjWl9fYEDC+pPdHzkZuAYYmTAdA/yfmXVDRFJqu6ZMItLqZQPXE/pOVhAGjfqeuy80s/0JZxwGA18AK4Fj3f1LAHd/2sw2ApcDvwW2APOAm6N1rwLGuvuiNG6PiIiItCxO6JJxN+Hk6KfA+e7+bDSo9gpCl4xlwGbgLHdfAODur5vZNwmDbF8SNeZcQig6/DZaf737I9FVNvoDd7j76oSnppnZIkJx5HdNsrUiUiPTyUoRqWJmpwIT3f3AuLOIiIhI2xMVIx5x975xZxGR1FI3DRERERERERFJKxUjRERERERERCSt1E1DRERERERERNJKLSNEREREREREJK1UjBARERERERGRtGpxl/bs3r279+/fP+4YIiIizc4777zzhbsXxp2jLdD+iIiIyPYasi/S4ooR/fv3Z+bMmXHHEBERaXbM7NO4M7QV2h8RERHZXkP2RdRNQ0RERERERETSSsUIEREREREREUkrFSNEREREREREJK1UjBARERERERGRtGrTxYiS8gquf/59npu9LO4oIiIiIiIiIunhDiefDC+9FFuENl2MyMnM4O8LV/HoWxp8XERERERERNqIf/8bpk6FZfGdmG/TxQgz44jhfXjrkzWsWF8cdxwRERERERGR1JsyBQoK4LjjYovQposRAONG9sEdnp+jrhoiIiIiIiLSyq1bB08+CccfD/n5scVo88WIQYX57NGno8aNEBERERERkdbvscdgyxY444xYY7T5YgTAuBF9mL1kPYu/2BR3FBEREREREZHUcA9dNEaOhH32iTWKihHA4SP6AOqqISIiIiIiIq3YO+/Au++GVhFmsUZRMQLYqXN7xvTvwjR11RAREREREZHWasoUaN8eTjgh7iQqRlQ5YkQfPli5kQUrNsQdRURERERERKRpbdwYxos47jjo1CnuNCpGVDlsr95kZhjT3lXrCBEREREREWllnnwyFCRiHriySkqLEWZ2qJktNLNFZnZJDc+famarzezdaJqYyjx16Z6fywGDuvHcnGW4e1wxRERERERERJrelCkwdCgccEDcSYAUFiPMLBO4A/guMAw43syG1bDoE+4+MpruS1WeZIwb0YfP12zhv5+vizOGiIiIiIiISNN57z34z3+axcCVVVLZMmIssMjdP3b3UuBx4MgUvt8O+86evcjJyuA5DWQpIiIiIiIircWUKZCTAyedFHeSrVJZjNgJ+Dzh8ZJoXnXHmNkcM3vazHZOYZ56dWyXzcG7F/L8nOVUVKqrhoiIiIiIiLRwxcUwdSocfTR07x53mq1SWYyoqe1H9SP854D+7j4c+Bvw+xpXZDbJzGaa2czVq1c3ccxtjRuxE6uLSnjr4y9T+j4iIiIiIiIiKffHP8LatTAxtiEaa5TKYsQSILGlQ19gm/4P7v6lu5dED6cA+9S0Ine/191Hu/vowsLClISt8s0hPcjLyWSaumqIiIi0Ci1pQG0REZEmN2UKDBwIBx8cd5JtpLIYMQMYbGYDzCwH+AEwLXEBM+ud8HAcMD+FeZLSPieTbw/ryYvvraC0vDLuOCIiIrIDWuKA2iIiIk3mgw/gtddCq4iMlF5Ms8FSlsbdy4HJwMuEIsOT7j7PzK41s3HRYuea2Twzmw2cC5yaqjwNMW5kH9ZvKeOfH6S2S4iIiIikXIsbUFtERKTJ3HcfZGbCqafGnWQ7WalcubtPB6ZXm3dlwv1LgUtTmaExDty1kM4dsnluzjIOGdYz7jgiIiLSeDUNqL1vDcsdY2YHAR8AP3H3z2tYRkREpOUoLYWHHoIjjoDevetdPN2aVzuNZiInK4Pv7tmbv76/ki2lFXHHERERkcZrkQNqi4iI7LBp02D1ajjjjLiT1EjFiFqMG9GHzaUV/G3+yrijiIiISOO1yAG1RUREdth998HOO8N3vhN3khqpGFGLsQO60qMgV1fVEBERadla5IDaIiIiO2TxYvjLX2DChDBmRDOkYkQtMjOMw4f34bWFq1m/pSzuOCIiItIILXlAbRERkUZ74IFwO2FCvDnqoGJEHcaN7ENpRSUvv7ci7igiIiLSSO4+3d13c/dB7v7zaN6V7j4tun+pu+/h7iPc/WB3XxBvYhERkR1QXh6KEYceCv36xZ2mVipG1GFE307s0q0Dz81RVw0RERERERFpAV56CZYubbYDV1ZRMaIOZsYRw/vwxqIvWF1UUv8LREREREREROI0ZQr07AmHHx53kjqpGFGPcSP7UOkwfe7yuKOIiIiIiIiI1G7ZMnjhBTj1VMjOjjtNnVSMqMduPQvYvWeBrqohIiIiIiIizduDD0JFBUycGHeSeqkYkYRxI/vwzqdrWbJ2c9xRRERERERERLZXWQn33w8HHwy77hp3mnqpGJGEI4b3AeC52eqqISIiIiIiIs3QK6/AJ580+4Erq6gYkYR+3TowcufOPKeuGiIiIiIiItIcTZkCXbvCUUfFnSQpKkYkadyIPry/fAOLVm2MO4qIiIiIiIjIV1avhj//GU4+Gdq1iztNUlSMSNLhw3tjhgayFBERERERkebl4YehrKzFdNEAFSOS1qNjO/Yb0I3nZi/D3eOOIyIiIiIiIgLuoYvGAQfAsGFxp0maihENMG5kHz75YhPvLd0QdxQREREREREReP11WLiwRbWKABUjGuS7e/YiO9OYNntp3FFEREREREREQquIjh1h/Pi4kzSIihEN0LlDDgcNLuT5OcuprFRXDREREREREYnR2rXw1FPwwx9CXl7caRpExYgGGjeyD8vXFzPz07VxRxEREREREZG27NFHobi4xXXRgBQXI8zsUDNbaGaLzOySOpY71szczEanMk9TOGRoT9plZ6irhoiIiIiIiMSnauDKUaPC1MKkrBhhZpnAHcB3gWHA8Wa23dCeZlYAnAu8laosTSkvN4tvDe3J9LkrKKuojDuOiIiIiIiItEUzZsCcOS2yVQSktmXEWGCRu3/s7qXA48CRNSx3HXATUJzCLE1q3Ig+rNlUyhuLvog7ioiIiIiIiLRFU6ZAhw5hvIgWKJXFiJ2AzxMeL4nmbWVmewM7u/vzda3IzCaZ2Uwzm7l69eqmT9pA39i9kIJ2WUybvSzuKCIiIiIiItLWFBXBH/4A3/9+uJJGC5TKYoTVMG/rJSjMLAO4DbiwvhW5+73uPtrdRxcWFjZhxMbJzcrk0D168Zd5Kykuq4g7joiIiNShNY5hJSIibdwTT8CmTS22iwakthixBNg54XFfILEpQQGwJ/CqmS0G9gOmtZQdgHEj+7CxpJxXF66KO4qIiIjUorWOYSUiIm3clCmwxx6w335xJ2m0VBYjZgCDzWyAmeUAPwCmVT3p7uvdvbu793f3/sB/gHHuPjOFmZrM/gO70T0/R101REREmrdWO4aViIi0UbNnw9tvh1YRVlOHhJYhZcUIdy8HJgMvA/OBJ919nplda2bjUvW+6ZKVmcFhe/XmlfmrKCouizuOiIiI1KzVjmElIiJt1G9+A+3bw0knxZ1kh6SyZQTuPt3dd3P3Qe7+82jele4+rYZlv9FSWkVUGTeiDyXllfz1/ZVxRxEREZGatdoxrEREpA1auRIeeQROOw26do07zQ5JaTGitRvVrws7dW6vrhoiIiLNV6sew0pERNqYO++EsjI477y4k+wwFSN2QEaGcfiI3rz+4Res2VQadxwRERHZXqsew0pERNqQLVtCMeKII2C33eJOs8NUjNhB40b0obzSefG95XFHERERkWpa+xhWIiLShkydCl98ARdcEHeSJpEVd4CWbljvjgwqzOOP7yzhh2P7YS14NFMREZHWyN2nA9OrzbuylmW/kY5MIiIiDVJZCbfdBqNGwUEHxZ2mSSTVMsLM9kx1kJbKzDj1gP7M+mwdf5y1NO44IiIirZb2R0REpM166SVYsCC0imglJ8CT7aZxt5m9bWY/MrPOKU3UAp2w7y6M6d+Fa5+bx8oNujy5iIhIimh/RERE2qZbb4WddoLx4+NO0mSSKka4+4HACYTRqGea2WNm9u2UJmtBMjKMm44dQUl5JZf/6T3cvf4XiYiISINof0RERNqk2bPhlVfgnHMgJyfuNE0m6QEs3f1D4GfAxcD/AL8xswVmdnSqwrUkA7rn8dP/3Z2/zV+pS32KiIikiPZHRESkzbntNsjLg0mT4k7SpJIdM2K4md1GGIX6m8AR7j40un9bCvO1KBMOHMDe/Tpz1bR5rC4qiTuOiIjsMxDOAAAgAElEQVRIq6L9ERERaXOWL4fHHoMJE6BLl7jTNKlkW0b8DpgFjHD3H7v7LAB3X0Y4OyFAZoZx87HD2VxawZXPvhd3HBERkdZG+yMiItK23HEHlJfDeefFnaTJJVuMOAx4zN23AJhZhpl1AHD3qakK1xLt2qOA8w8ZzIvvreCFOcvjjiMiItKaaH9ERETajs2b4a674Hvfg0GD4k7T5JItRvwNaJ/wuEM0T2ow6esDGd63E1c++x5fblR3DRERkSai/REREWk7Hn4Y1qwJl/NshZItRrRz941VD6L7HVITqeXLyszg5mNHsKG4jKufez/uOCIiIq2F9kdERKRtqKwMA1eOGQNf+1rcaVIi2WLEJjMbVfXAzPYBtqQmUuuwe68Czv3mYJ6bvYyX562IO46IiEhroP0RERFpG154AT74ILSKMIs7TUpkJbnc+cBTZlZ1zcrewPdTE6n1OOsbg3hp3gou/9N77DugK507tJ5rwoqIiMRA+yMiItI23Hor7LwzHHNM3ElSJqmWEe4+AxgCnA38CBjq7u+kMlhrkB1111i3uZRr1V1DRERkh2h/RERE2oRZs+DVV+HccyE7O+40KZNsNw2AMcBwYG/geDM7OTWRWpdhfTryo4N35Zn/LuWV+SvjjiMiItLSaX9ERERat9tug/x8mDgx7iQplVQ3DTObCgwC3gUqotkOPJyiXK3K5IN35S/zVnDZn+byl/5d6dS+9Va3REREUkX7IyIi0uotXQqPPw6TJ0PnznGnSalkx4wYDQxzd2/Iys3sUOB2IBO4z91/Ue35s4AfE3YoNgKT3L3V9WfIyQrdNb535xtc//z73Dx+RNyRREREWqJG7Y+IiIi0GL/7XbiSxrnnxp0k5ZLtpvEe0KshKzazTOAO4LvAMEJTymHVFnvM3fdy95HATcCtDXmPlmSvvp0486CBPPXOEl5duCruOCIiIi1Rg/dHREREWoyNG+Huu+Hoo2HAgLjTpFyyxYjuwPtm9rKZTaua6nnNWGCRu3/s7qXA48CRiQu4+4aEh3mEppat1rnfGsyuPfK59Jm5FBWXxR1HRESkpWnM/oiIiEjL8Pvfw7p14XKebUCy3TSubsS6dwI+T3i8BNi3+kJm9mPgAiAH+GYj3qfFaJedyc3HDueYu/7NDdMXcOPRe8UdSUREpCW5Ou4AIiIiKVFRAb/+Ney3H+y/f9xp0iLZS3u+BiwGsqP7M4BZ9bzMalpVDeu+w90HARcDP6txRWaTzGymmc1cvXp1MpGbrb37dWHi1wfyh7c/441FX8QdR0REpMVo5P4IZnaomS00s0VmdkkNz59lZnPN7F0ze72GbqUiIiKp9fzzsGhRm2kVAUkWI8zsDOBp4J5o1k7An+t52RJg54THfYFldSz/OPC9mp5w93vdfbS7jy4sLEwmcrN2wbd3Y2D3PC7+4xw2lZTHHUdERKRFaMz+iMawEhGRFuGWW2CXXeCoo+JOkjbJjhnxY+BrwAYAd/8Q6FHPa2YAg81sgJnlAD8AtunXaWaDEx7+H/BhknlatHbZmdx07HCWrtvCL19aEHccERGRlqIx+yMaw0pERJq3GTPgX/+C886DrGRHUmj5ki1GlET/gQNgZlnU8x+1u5cDk4GXgfnAk+4+z8yuNbNx0WKTzWyemb1LGDfilAZvQQs1un9XTj2gPw+/+Sn/+fjLuOOIiIi0BA3eH6HmMax2qr6Qmf3YzD4itIyo8XpqranbqIiINCO33QYFBXD66XEnSatkixGvmdllQHsz+zbwFPBcfS9y9+nuvpu7D3L3n0fzrnT3adH989x9D3cf6e4Hu/u8xm5IS/T/vrM7u3TrwMV/nMOW0oq444iIiDR3jdkfabIxrFpbt1EREWkGPv8cnnwSzjgDOnaMO01aJVuMuARYDcwFzgSmU8t/1JK8DjlZ/PKY4Xz65WZufnlh3HFERESau8bsjzTZGFYiIiJN7re/Dbfn1tgor1VLqkOKu1cCU6JJmtB+A7tx8v678OC/P+GwvXoxun/XuCOJiIg0S43cH9k6hhWwlDCG1Q8TFzCzwdH4E9CGxrASEZGYFRXBvffCsceGwSvbmKSKEWb2CTU3aRzY5InaoIsPHcLfF6zioqfn8OfJX6Nju+y4I4mIiDQ7jdkfcfdyM6sawyoTeKBqDCtgZtR1dLKZHQKUAWtpQ2NYiYhIjB58ENavb1OX80yU7FCdoxPutwPGAzqF30TycrO46djhnHz/25ww5S0enjCWLnk5cccSERFpbhq1P+Lu0wldOhLnXZlw/7ymCigiIpKUigr49a/ha1+DsWPjThOLpMaMcPcvE6al7v5r4JspztamHDCoO1NOHs0HK4v4/r1vsmpDcdyRREREmhXtj4iISKvx7LPwySdttlUEJFmMMLNRCdNoMzsLKEhxtjbn4CE9ePC0MSxZu4Xx97zJkrWb444kIiLSbGh/REREWo1bb4UBA+DII+NOEptku2ncknC/HFgMHNfkaYQDBnXnkYn7cuoDbzP+7jd5dOK+DCzMjzuWiIhIc6D9ERERafneegveeANuvx0yM+NOE5tkr6ZxcKqDyFdG9evC45P256T73+K4e95k6un7MrR327rmrIiISHXaHxERkVbh1luhUyc47bS4k8Qq2atp1NmRxd1vbZo4UmVYn448edb+nHjfW3z/njf5/YSx7N2vS9yxREREYqP9ERERafEWL4ann4YLL4SCtt3TMKkxIwijV58N7BRNZwHDCP002/YnmEKDCvN58sz96dwhhxPve4s3P/oy7kgiIiJx0v6IiIi0XJWVcOmlkJEB55wTd5rYJTtmRHdglLsXAZjZ1cBT7j4xVcEk2LlrB56KWkic+uDb3H3iPhw8pEfcsUREROKg/REREWmZKith4kR4/HG47jrYeee4E8Uu2ZYR/YDShMelQP8mTyM16tmxHU+cuT+De+YzaepMXpizPO5IIiIicdD+iIiItDxVhYgHH4SrroKf/SzuRM1Csi0jpgJvm9mfAAeOAh5OWSrZTte8HB47Yz8mPDiDc/4wi82lwxk/WtU0ERFpU7Q/IiIiLUtFRShEPPRQKERcfXXciZqNpFpGuPvPgdOAtcA64DR3vyGVwWR7Hdtl8/DpYzlgUHf+39NzePjNxXFHEhERSRvtj4iISItSUQGnnx4KEVdfrUJENcl20wDoAGxw99uBJWY2IEWZpA4dcrK475TRfHtYT658dh53vroo7kgiIiLppP0RERFp/qoKEb//PVxzTWgVIdtIqhhhZlcBFwOXRrOygUdSFUrq1i47kztPGMWRI/tw00sLuemlBbh73LFERERSSvsjIiLSIlRUwIQJXxUirrwy7kTNUrJjRhwF7A3MAnD3ZWamS2jFKDszg1uPG0mHnCzufPUjNpdWcOXhw8jIsLijiYiIpIr2R0REpHmrqIDTToOpU+Haa+GKK+JO1GwlW4wodXc3Mwcws7wUZpIkZWYYNxy1J/m5mUz51ydsLCnnl8cMJ1MFCRERaZ20PyIiIs1XYiHiuut01Yx6JFuMeNLM7gE6m9kZwARgSupiSbLMjMsOG0pebha//tuHbCmt4LbvjyQnqyHDgYiIiLQI2h8REZHmqaICTj0VHnkErr8eLr887kTNXlLFCHf/lZl9G9gA7A5c6e5/re91ZnYocDuQCdzn7r+o9vwFwESgHFgNTHD3Txu2CWJmnH/IbuTlZPHz6fMB+O3xe6vLhoiItCqN3R8RERFJKRUiGqXeYoSZZQIvu/shQNL/4UevuwP4NrAEmGFm09z9/YTF/guMdvfNZnY2cBPw/YZsgHzljIMGUunOjS8uoLAgl6uOGIaZChIiItLyNXZ/REREJKUqKuCUU+DRR+HnP4fLLos7UYtRb1t+d68ANptZpwaueyywyN0/dvdS4HHgyGrr/oe7b44e/gfo28D3kGomHTSQ0w8cwEP/Xsxdr30UdxwREZEmsQP7I5jZoWa20MwWmdklNTx/gZm9b2ZzzOwVM9ulSUKLiEjrVlEBJ58cChE33KBCRAMlO2ZEMTDXzP4KbKqa6e7n1vGanYDPEx4vAfatY/nTgRdresLMJgGTAPr165dk5LbJzLj8sKGsLirhppcWUpify/jRO8cdS0REpCk0eH9ELTVFRCQlystDi4jHHguFiEsvrf81so1kixEvRFND1NQ/wGtc0OxEYDTwPzU97+73AvcCjB49usZ1yFcyMoxfjR/Bmk2lXPLMXLrn53LwkB5xxxIREdlRjdkf2dpSE8DMqlpqbi1GuPs/Epb/D3DiDuYUEZHWrLw8tIj4wx/gxhvhku0a3UkS6ixGmFk/d//M3X/fiHUvARJPyfcFltXwHocAlwP/4+4ljXgfqUFOVgZ3nTiKH9z7H3706CweO2Nf9u7XJe5YIiIiDbaD+yNN1lJTRERkm0LEL34BF18cd6IWq74xI/5cdcfM/tjAdc8ABpvZADPLAX4ATEtcwMz2Bu4Bxrn7qgauX+pR0C6bh04bS2FBLhMemsHHqzfGHUlERKQxdmR/pDEtNW+u5flJZjbTzGauXr26gTFERKTFKy+Hk05SIaKJ1FeMSPwPfGBDVuzu5cBk4GVgPvCku88zs2vNbFy02M1APvCUmb1rZtNqWZ00UmFBLg9PGEuGGSc/8DarNhTHHUlERKShGr0/QsNbao6rraWmu9/r7qPdfXRhYWEDY4iISItWVYh4/HH45S9ViGgC9RUjvJb7SXH36e6+m7sPcvefR/OudPdp0f1D3L2nu4+MpnF1r1Eao3/3PB48bQxrNpVyyoMz2FBcFnckERGRhtiR/RG11BQRkR1TdfnOqkLERRfFnahVqK8YMcLMNphZETA8ur/BzIrMbEM6AkrTGN63M3efuA8frixi0sMzKSmviDuSiIhIshq9P6KWmiIiskMqKuC008JVM268UYWIJlTnAJbunpmuIJJ6B+1WyM3jh/OTJ2ZzwROz+e3xe5ORUVNXWhERkeZjR/dH3H06ML3avCsT7h+yI+sXEZFWqrISJk6EqVPh+ut11YwmluylPaWVOGrvvqwuKuGG6QsoLMjlqiOGYaaChIiIiIiIyFaVlTBpEjz0EFx9NVx+edyJWh0VI9qgSQcNYtWGEu57/RN6dMzlR9/YNe5IIiIiIiIizUNlJZx9Ntx/P1xxBVx1VdyJWiUVI9qoyw4byuqNJdz00kIK83MZP3rn+l8kIiIiIiLSmrnD5Mlw771w2WVwzTVxJ2q1VIxoozIyjJuPHcGXG0u55Jm5dM/P5eAhPeKOJSIiIiIiEg93OPdcuOuuMFDl9deDurSnTH1X05BWLCcrg7tP2oehvQv40aOz+O9na+OOJCIiIiIikn7u8JOfwO9+BxdeCL/4hQoRKaZiRBuXn5vFg6eOpbAglwkPzeCj1RvjjiQiIiIiIpI+7vDTn8Ltt8P558PNN6sQkQYqRgiFBbk8PGEsGWacfP/brNxQHHckERERERGR1HMPl+y89VY455xwq0JEWqgYIQD0757Hg6eNYe3mUk554G02FJfFHUlERERERCR13MMlO2+6KVw94/bbVYhIIxUjZKvhfTtz94n7sGjVRk66/21WrFcLCRERERERaaWuugpuvBEmTQpjRagQkVYqRsg2DtqtkDtOGMWilUUc/tt/8eZHX8YdSUREREREpGldcw1cdx1MnBiunpGhQ+N00ycu2/nOHr14dvLX6NQ+mxPvf4t7//kR7h53LBERERERkR13/fVw9dVw6qlwzz0qRMREn7rUaNceBTw7+UD+d1hPbpi+gB8/NouNJeVxxxIREREREWm8G2+EK66Ak06C++5TISJG+uSlVvm5Wdx5wiguO2wIL723giN/9zqLVunSnyIiIiIi0gLdfDNcdhmccAI8+CBkZsadqE1TMULqZGZMOmgQj0zcl3Wbyzjyd6/z4tzlcccSERERERFJ3q23wkUXwQ9+AA89pEJEM6BihCTlgEHdef7cAxncs4CzH53FjdPnU15RGXcsERERERGR2i1fHgoQF14I48fD1KmQlRV3KkHFCGmA3p3a88SZ+3HSfrtwzz8/5sT73+KLjSVxxxIREREREdlWRQXccQcMGQJ//nO4esajj6oQ0YyktBhhZoea2UIzW2Rml9Tw/EFmNsvMys3s2FRmkaaRm5XJdd/bk1vGj+C/n63j8N+8zqzP1sYdS0REREREJJg1C/bfHyZPhrFjYe5cuPJKyM6OO5kkSFkxwswygTuA7wLDgOPNbFi1xT4DTgUeS1UOSY1j9unLMz86gOws4/v3vMnU/3yqy3+KiIiIiEh8iorgJz+BMWPgs8/gscfgL3+BwYPjTiY1SGXLiLHAInf/2N1LgceBIxMXcPfF7j4H0OADLdAefTrx/OSvc+Cu3bniz+9x4VOz2VJaEXcsERGRbailpohIK+cOzzwDQ4fC7bfDmWfCggVw/PFgFnc6qUUqixE7AZ8nPF4SzZNWpFOHbO4/ZQznHzKYP/13KUff9W8++3Jz3LFEREQAtdQUEWn1Fi+GcePgmGOge3f497/hzjuhc+e4k0k9UlmMqKkE1ah2/GY2ycxmmtnM1atX72AsaWoZGcb5h+zGA6eMYenazRz+23/xjwWr4o4lIiICaqkpItI6lZXBTTfBHnvAP/4Bt9wCM2fCfvvFnUySlMpixBJg54THfYFljVmRu9/r7qPdfXRhYWGThJOmd/CQHjx/ztfp26UDE34/g9v++gGVlRpHQkREYtVkLTV1ckREpJl44w0YNQouvhj+93/h/ffhggt0pYwWJpXFiBnAYDMbYGY5wA+AaSl8P2kG+nXrwDM/OoCj9+7L7a98yITfz2Dd5tK4Y4mISNvVZC01dXJERCRma9bAGWfAgQfC+vXw7LPwpz9Bv35xJ5NGSFkxwt3LgcnAy8B84El3n2dm15rZOAAzG2NmS4DxwD1mNi9VeSR92mVn8qvxw7n+e3vyxqIvOOJ3r/Pe0vVxxxIRkbapyVpqiohITNxh6lQYMgQefBB++tPQGmLcuLiTyQ5IaTsWd58OTK8278qE+zMIOwXSypgZJ+63C3v06cjZj8zimLv+zc+P2otj99GfW0RE0mprS01gKaGl5g/jjSQiIkn78MNwdYx//COMB/HXv8KIEXGnkiaQym4aIuzdrwvPn3sgo/p14adPzeZnf55LSbku/ykiIumhlpoiIi1UZWW4TOeIETBrFtx9dxgrQoWIVkMjfEjKdc/PZerpY7n5Lwu557WPeW/pBu46cRS9O7WPO5qIiLQBaqkpItLCfPwxTJgAr70Ghx0GU6ZAnz5xp5ImppYRkhZZmRlc+t2h3HXCKD5cWcThv3mdf3/0RdyxRERERESkuXAPLSCGD4f//hceeACef16FiFZKxQhJq+/u1ZtnJx9I5w7ZnHjfW9zz2ke46/KfIiIiIiJt2mefhct0nn02HHAAzJ0Lp50GVtNFkaQ1UDFC0m7XHvk8O/lADt2zFze+uICzH5lFUXFZ3LFERERERCTd3OH++2HPPeHNN0PLiJdf1uU62wAVIyQW+blZ3PHDUVx22BD+8v4KvnfHGyxaVRR3LBERERERSZelS+H//g8mToR99gmtIc48U60h2ggVIyQ2ZsakgwbxyMR9Wbe5jCN/9wYvzFkedywREREREUkld5g6NbSGePVV+M1v4JVXYMCAuJNJGqkYIbE7YFB3nj/3QHbrVcCPH5vFDdPnU15RGXcsERERERFpaitWwFFHwcknwx57wJw5cM45kKFD07ZGf3FpFnp3as8Tk/bn5P134d5/fsyJ97/F6qKSuGOJiIiIiEhTcIfHHw8FiJdegl/9Kly6c9dd404mMVExQpqNnKwMrj1yT24ZP4L/fraOI377Ou98ujbuWCIiIiIisiNWr4bjjoPjjw/Fh3ffhQsvhMzMuJNJjLLiDiBS3TH79GVI7wLOfmQWx93zJv26dqBbXg5d83Lolp9L9/yc8Dg/l+7RvG75OXTpkENmhga7ERERERFpNp55Bs46C9avhxtvhJ/+FLJ0GCoqRkgztUefTjw3+UDu/udHfL5mM19uLOXTLzcz67O1rNlUSqVv/xoz6NIhFCq65efQLS93622n9lnk5WZR0C6L/Nxs8nIzKWgX5uXnZpGXk0WGChkiIiIiIskrKYGVK8M4EDXdfvopzJgBe+8Nf/97GLBSJKJihDRbnTpkc/GhQ7abX1HprNtcyppNpXyxsZQvN5V8dX9jCV9uDM/NX7GBLzeWsn5LWVLvl5eTGYoT7UKBYpup3VfFjIKty2STv7XAEd22yyI3q3HNzSornaKScoqKyygqLo+msq23G6J5m0rK6dkxl6G9OzK0d0d6d2qH6fJHIiIiIi1TeTl88EEYyHHu3PB49GgYMwZ22SWey1wWF8OCBeHSm3UVG9atq/n1XbpAz57QqxfccENoDZGdnd5tkGZPxQhpcTIzLOqakcvgnvUvX1ZRufUgvqi4nI0l0f3odmM0b2PV/dJwu6mknM82bQ6vLQ2vraipSUY1OZkZ2xQ0Ctp9VbBol53JptKKbYoMVYWHjSXl9a47O9PokJO1TYGlU/tshvQqiIoT4Xa3ngW0y1YfvGQVl1Xw4cqNrN9Sxm498yksyFWBR0RERJqWeziAryo6zJkTpvnzQwsDCGMoZGRAWbSvV1gYihJjxsDYseG2sLBpc61aBbNnh3EcZs8O0/z5UFGx7XKdOn1VYBg+PNxWPe7Z86v7PXpAbm7TZpRWScUIafWyMzPoGo05sSPcnZLyyq2Fg43F5RSVlIXbhIJGuF+2tchRVFzO8vXFbCwpZ3NpBXk5mRS0y6agXRbdu+dtvV/QLpuO7bK23t/2NouO7bLJzcrAzCgqLmPhiiLmL9/A/Oj2yZmfs7k0/KeRYTCge97W1hNVRYpeHZtHK4otpRWs2FDM8vVbWF1UQucOOezUuT07dW5P+5zUFVEqKp3P1mxm4YoNLFhRxMJoWvzlpm26/nTLy2FI7wKG9Aqf35BeBQzumd/oVi8NsaG4jOXrigHIy80M3Yhys8jO1HjDIiIiLcaWLTBv3rZFh7lzw0COVXr3Dgf1hxwSbocPhyFRq+C5c0P3hrffDrcvvhiKGRBaS1QVJsaMgX32gYKC+jNVVMCHH35VdKi6Xb78q2X69oURI+DII0OeXXb5qtjQrl3TfT4igLnXf6a3ORk9erTPnDkz7hgizU5ldKCdWKCYv3wDS9Zu2bpM5w6hFcWQXh3ZpVsHCtpt29UkP6EbSvvszAYXLtxDV5MV64tZvr6YFeu3sHx9MSs3VD0Ot3V1nemWl8NOXdpvLU707dKenbp0CI+7tKdT++Sa+K0uKmHhiiIWrNgQig4ri/hgZRHFZZVAaPG4S9cO7N6rgN17hYJD5/bZLFxZxILlRcyPXldSHpbPzDAGFeZFxYmvCjw9GtCKoryikpVFJSxbt4Wla7ewdN0Wlm2dilm2bgtFtbSQycnKoCAqTISxTjK33q9pfn5uFt3zcxnQPY9eHdtpTJQ2wszecffRcedoC5rl/khlZfjHrRkUnUXSorw8HPSXlITvf0VFuK1tquv5ioqwvrKy7W9rmlfTbWkpLFoUCgkffhjWC9C+fRgrYa+9vio67LUXdO+e/LYWFcGsWaEwUTV98kl4zgyGDt229cTAgaGbRWLRYe7c8HlB6DIxbFgoPIwcGW5HjIBu3Zr2byRtTkP2RVSMEGnlNkStKBYs38D7y0ORYuGKIraUVdT5uswM26arSX7CeBpVLTYyM4xVG0pYsWHL1mJDVeuMRN3zc+ndqR29OrWjV8dwW/W4MD+XtZvLWLpu89YD9CXR7dK1W7YWA6oU5GZ9VaxIuN1UUr5Na4cvN5Vu8/5DehWEwkPPcDu4Zz4dcupuHFZR6XzyxSYWrAiFnQXR57dsffHWZbrm5Wwt8AztXcDAwjzWbS4LBYeowFA1rdhQvN3gq106ZNOnc3v6RMWXPp3b0btTezLMQjeiqu5ECd2HNpZUsKkkdB/amND1aFMNnz1A++xM+nfPY2BhHoO65zGgMI+B3fMZWBha5khyyioqWb+ljA1bythcWsGWsopwW1rBlrLyr+6XVrC5rIb7icuUVXDoHr249LChTZpRxYj0Scv+iDts3BiaUFefVq/eft4XX4Qzl7vvvu00ZAjstht06JDavG1VZWXoX19SEm6rT1XzS0pC8/oBA8KZ5rZaNCorg88+CwfSn3wCa9aEA+QtW2Dz5ppva3uuvP4urmmVlRVaEiQWHYYPD4WBVFzCcvVqmDlz2xYUq1Ztv1zXrl8VHKpuhw6FnB1rNSxSExUjRKRO/7+9Mw+S467u+Oftzu7soV3JslbWjezENmBiHGyMqWAgNiYOSSwcYjABAmVSFBATjiIEYzAidggGwpEyCUVhA4EEB0xBxJHY5iYJNviSkJBl7PVakiWxOtC1q71f/vj9RtM76u7pX8/s7Gj3faq65tcz3d953fPm16/f7+ipKeXgsfFpQ02mDzMp3+QeHhk/YV6NI/51fHKKpT1Fn1zo5LTecpKh9Lq0p4P2Qr4hBqrK/qGx40mKE5MVwxweKQcinW2tnLWsh6f6hEMpAXHqgvqOWzw0PF5OUPheKNsivS5KtLUKyxeWkgydrFzUcTzxsMInHqolREKYmlKGxiYYGp3k6OgEg0dG6N87xOP7hujfe5T+fUPsODA8LSHS1+N6T/yWT1Cc7pMWqxd3zbmhIarKsfFJDh0b90mFiUh5vFwecevTPh8Zj020JdHWKnS0tdLV3kpXeyFSbj1efu4Zp3L1hWvqeoyWjGgcdY1HDh+Gm26KTzqUxpFX0tvrxmX39bnXUnloyLWGbtvmZrGPxnmrV5eTE9FkxapVbox6rYyNueTJ0aPu5nvJEjeJXaNvuqem3Lj87dvdOdi3z90Aj42Vl7T1uF1HoXwAABR7SURBVM9GR6cnG6Ll8WwTZU+jowPWrnWJibjllFPyH7+q86ldu5KXQ4eczyxf7pZly04sL1qU77dTdb7b3++SDaXXUnnHjnKPgcpz0tnpkmbR16Ry9L1isTzXQkvL9HLlkvZZW5tLKOR5bW2d/QSTqju/pV4TT32qSz6sXDn7thnzBktGGIbREFR11uegODIyzpMHj9HZ1srqU7pmbTjC5JQysH+IgX1DLO52c2AsWVBsuuERoxOT7DgwzGN7h3yi4ij9e4fo3zfEgUhvkkKLsObULtYs7qK7vUCx0EKxrYViobX8Wmjx77tyR1vkvePbufc721rpbi/Q2d6aOzmVxOSUcmBojMEjIwweGWWvXwYPj7D36CiDh0ePv1brEdRTLNDb2cbCzjZ6OwssLJU73OvCLtcrqHQsXe2tdLaVy6VEw2wlciwZ0TjqGo8cOVJOKESXaKIhut7Xl23s9rFjrqv4tm3Tl4cfdt9ZoqvL9ZwoJSdWrnT7Dg2VkwtZynGt1B0dsGKF01y5slyufC9kLPrwsEs0VC5PPOFed+yoniBoa3Otwu3t08tJ77W1ORujS7EYX076rK3N3aSXbsyjS+UTCRYuLCcmKpMW7e3piYZdu9w5qqS3153rFStceXDQPRFh9+5y1/0oxWI5MRGXrFiyxCV9ogmH/n4YGDjx+5ctc70DSscQLZf8uR4JMcMwZp2mSUaIyOXAJ4FW4LOq+qGKz4vAvwLnA/uBV6jqQJqmJSMMwzBmhoPDY/Tvc0mK/r1HeXzfEDt+M8zI+BSjE5OMjk8xOjHFyPjkCcNnQig9FabUU6C76OYo6S66m/pu35tg+metjE5MucTCkVEGj5QTDfuHxmKfdNPTUWBpT5G+niJLezro6ymyZEHxeIKhMuHQ09FGa5Mlj0KxZETjqGs8UorFGpXcVXU3odHkRKk8MDC91bpQgAULykt3d7Zyseh6JDz5pFt27SqX4258Fy8+MUGxYoXbNppo2L7d6UZpaXHbrlnjusivWTN9Kc3sX0osFArN10p88GA5MTEwcGKyIu6clejsnH7O4pbly91vE0epJ0UpMbF7d7lc+d7+/fEaPT3JyYa1a22IkGHMI5oiGSEircAjwGXATuDnwCtV9ZeRbd4MnKuqbxSRq4ErVfUVabqWjDAMw5h9VJWxyUhywicqRicmpyUsRn0i49jYJENjkwyPTjA87l/HJv3i5rsYHvPvjZbLExVJhhZxc4As7S3St8AlGZb2lhIO0xMP8/HxtpaMiMcaRwIYGXE3+11d7ua13mPKVd0QgcoERWV5z55yUmTBgvgkQ+m9FStcr4O5SmnYQykxMT4+PfnQ29u45MroqOsNsWePm6/gtNNcwmHx4uZL8BiGMSuExCIz+WjPC4FHVbXfG3U7sA74ZWSbdcB6X74DuEVERE+2sSOGYRjzDBHxQzVa6Z3BSTDHJqaOJyva/WN6T/beC0Zj8Y0jnyLSOCIiG6KNI8Drgd+o6m/7xpGbgdTGkTlLR4ebQ2KmEHFzESxaBOeck7zdxIS7Ae/szD93wVxBxN30n3YaXHTR7NpSLJaTQYZhGDUyk4OzVgI7Ius7/Xux26jqBHAIOOF5MiLyBhG5T0Tu2xt9Nq9hGIYxp2kvtLCoy83B0ddTtESEkYfjjSOqOgaUGkeirAO+4Mt3AJfKbE+IM98pFFyr/2xMgGkYhmE0hJlMRsRdOSp7PGTZBlX9jKpeoKoX9PX11cU4wzAMwzDmBdY4YhiGYRhNyEwmI3YCqyPrq4BdSduISAFYCByYQZsMwzAMw5hfWOOIYRiGYTQhM5mM+DlwpoicLiLtwNXAhoptNgCv9eU/A75v80UYhmEYhlFHrHHEMAzDMJqQGUtG+G6O1wJ3AluBr6jqFhH5OxG5wm92K3CqiDwKvAN490zZYxiGYRjGvMQaRwzDMAyjCZnJp2mgqt8BvlPx3g2R8ghw1UzaYBiGYRjG/EVVJ0Sk1DjSCtxWahwB7lPVDbjGkS/6xpEDuISFYRiGYRgziJxsiX8R2Qs8UWfZJcC+OaLRTLbY8cyMRjPZYsfT3LY0i0Yz2TLXjqeSp6iqTWbQACweOalsaRaNZrLFjmdmNJrJFjue5ralWTTqqVMicyxy0iUjZgIRuU9VL5gLGs1kix3PzGg0ky12PM1tS7NoNJMtc+14jLlFs/jVXPufNItGM9lixzMzGs1kix1Pc9vSLBr11MnDTE5gaRiGYRiGYRiGYRiGcQKWjDAMwzAMwzAMwzAMo6FYMsLxmTmkUS+dZtGol85c0qiXTrNo1EunWTTqpTOXNOql0ywa9dKply3G3KFZ/Gqu/U+aRaNeOs2iUS+duaRRL51m0aiXTrNo1EtnLmnUUycYmzPCMAzDMAzDMAzDMIyGYj0jDMMwDMMwDMMwDMNoKPM6GSEit4nIoIhsrkFjtYj8QES2isgWEXlrDo0OEfmZiGz0Gh+owZ5WEXlQRL5Vg8aAiPxCRB4SkftyaiwSkTtE5GF/bp4buP/Z/vtLy2EReVsOO97uz+lmEfmyiHSEanidt3qNLVntiPMvEVksIneLyK/86yk5da7ytkyJSNXZbxM0PuJ/n00i8nURWZRD40a//0MicpeIrAjViHz2ThFREVmS83jWi8iTEZ95SR5bROQtIrLNn98P57DjPyI2DIjIQzmP5zwRuaf0PxSRC3NoPFNEfur/z98Ukd4qGrH1WYjfpmhk9tkUjVCfTdLJ7LdJGpHPq/ptih1BPmvMTZL8WkTWisixiH98Oo9O5PM1InJURN6Zw5YLI3ZsFJErc2hcJiL3+/rofhG5JIfGqf6/dFREbkk7H9XOiYhcJyKPiqvv/yBFI7buEpF2EfmcP56NIvLCHBptIvIFr7FVRK6rcjxJOq+S6THTlIicF6LhPztX3DVji7cpNmZKsSPUZ1OvCxl9NsmWEJ9N0gjx2SSNUJ9N+30y+WzFPkFxQIJGUDySohMcHyXoZI7TEvav67VXAuLXmH2D4ugEjaDYKEEj6L6irqjqvF2A5wPPAjbXoLEceJYv9wCPAE8P1BBggS+3AfcCF+W05x3AvwPfquGYBoAlNZ7bLwB/6cvtwKIatFqBPbhn1obstxJ4HOj0618BXpfj+58BbAa6gALwXeDMPP4FfBh4ty+/G7g5p87TgLOBHwIX5NR4MVDw5Zur2ZKg0Rsp/zXw6VAN//5q4E7giSy+l2DLeuCdAb9rnMbv+9+36NeX5jmeyOf/CNyQ05a7gD/05ZcAP8yh8XPgBb58DXBjFY3Y+izEb1M0MvtsikaozybpZPbbJI0Qv02xI8hnbZmbS5JfA2uT6pYQncjnXwO+muZzKbZ0Rd5fDgyW1gM0fhdY4cvPAJ7MYUc38DzgjcAtNZzbpwMbgSJwOvAY0JqgEVt3AX8FfM6XlwL3Ay2BGn8O3B45xwPA2pTjqVqPAr8D9Idq4OKbTcAz/fqpOc5JqM+mHk9Gn02yJcRnkzRCfDZJI9Rnk3Qy+2yFXlAckKARFI9k1MwUH8XsFxSnJWisT/OpQK2g+DVm/6A4OkEjKDYK8btGLPO6Z4Sq/hg4UKPGblV9wJePAFtxN8EhGqqqR/1qm1+CJ/MQkVXAHwGfDd23nvis6/OBWwFUdUxVD9YgeSnwmKo+kWPfAtApIgXchWlXDo2nAfeo6rCqTgA/AhIz7CUS/GsdLlGDf31pHh1V3aqq2zLYnqZxlz8egHuAVTk0DkdWu6nityn/uY8D76q2fwadzCRovAn4kKqO+m0G89ohIgK8HPhyTlsUKLVgLKSK7yZonA382JfvBl5WRSOpPsvst0kaIT6bohHqs0k6mf22Sh2fyW/rcZ0w5i6hfp1HR0ReCvQDW/JoRK5/AB2k/2eSNB5U1VI9tgXoEJFioMaQqv4PMJJ2HNV0cHXa7ao6qqqPA48Csa29KXXX04Hv+W0GgYNAbItiioYC3T5G6QTGgMMx21XTifJKUq47KRovBjap6ka/3X5VnazBjqqk6QT4bKxGoM8maYT4bJJGqM8mnZPMPltBUByQZBYB8Ug1QuKjGILitAYQFL9WEhpHJ2jUfA2p1386D/M6GVFvRGQtLot6b459W313pUHgblUN1gA+gftDTOXYN4oCd/kuaW/Isf8ZwF7gc+KGjHxWRLprsOdqclRYqvok8FFgO7AbOKSqd+X4/s3A831Xuy5cVnh1Dh2A01R1t7dvN641pRm4BvivPDuKyN+LyA7gVcANOfa/AtfasDHP91dwre+mdptkGAITw1nAxSJyr4j8SESeXYMtFwO/VtVf5dz/bcBH/Ln9KJDafTeBzcAVvnwVAX5bUZ/l8tta6sQMGkE+W6mTx2+jGnn9NuZ4avVZY25R6den++voj0Tk4jw6/vr7t0DoENBptojIc0RkC/AL4I2R4DezRoSXAQ+WbihyaoQS1VkJ7Ih8tpPwBOFGYJ2IFETkdOB8wmODO4AhXIyyHfioqtaUaAdeQb6bvLMAFZE7ReQBEXlXzu/P67PHqcFnK3Xy+GwSIT47E+T12dxxQIR6xCNRaomP6hWn1XztrVf8WmscXUG96suGUZhtA+YKIrIA153sbRVZrkz47PN5fpzP10XkGaqaeS4LEfljYFBV75eUcYsZ+T1V3SUiS4G7ReRh3+qalQKuu/hbVPVeEfkkrmv3+0INEZF2XCUaXPH5ymUdrjvbQeCrIvJqVf1SiI6qbhWRm3EZ5aO4AKSWC1pTISLX447n3/Lsr6rXA9eLG+t6LfD+gO/uAq7HtcjUyr8AN+KSaTfiugBeE6hRAE4BLgKeDXxFRM5Q1TwZ79TWqQy8CXi7qn5NRF6O62n0okCNa4B/EpEbgA24VreqVNZnrhEjjFrrxDSNUJ+N0wn126iG/+5gv405r/XwWeMkQES+CyyL+eh6Vf1Pv02lX+8G1qjqfhE5H/iGiDwK9AXqfAD4uKoe9f/lN4jI6wI18I0k54jI04B7ROQmTmzFS9Xw75+D60q8R+Ln7KqqUaGX59xWVmqXA5eJyPokjRhuw/WcvA/XRXsC+JicOO9XmsaFwCSwAnft+YmIvBbX+px4PEmIyHOAYeATIpJ6TmIo4IYTPNtr7BKRN+GSJVk1cvlsDME+G0eozyYR4rNZyOKzcbvFvKfV9MgYB1TRuJSM8UjGY0uNj6rYkilOq6KR+dpbRec9ZIgDqp2TLPFIznouWGNW0AaOCWnGhcDxbQkabbjxQu+ok03vJ3AsE/APuCzpAG5+hWHgS3WwZX0OW5YBA5H1i4Fv5/z+dcBdOfe9Crg1sv4XwD/X4Zx8EHhzHv8CtgHLfXk5sC2PTuT9H5JxbFecBvBa4KdAVy12+M+ekuW/FNXAjW0d9H47gKtEtwPLarQl0/865vf5b+CFkfXHgL4c57UA/BpYFeBXlbYcguOPXxbgcI3n5CzgZxk0TqjPQv02TiPUZ5M0cvhsav2cxW8rNfL4bQY7MvmsLXNzyeLXWf47cTrATyK+ehA3nOvaGm35QZotSRq47sOP4Bo9cp8T4HVkGH+fck6uA66LrN8JPLeKTur5B/6PKnOGVWoAnwJeE1m/DXh5hmOKtQXXZfw9Gc9LpS1XA5+PrL8P+Jsaz0lVn02wJchnM9qS6rNJGiE+W+W3yeyzCeck2GdjNDPFATH7BccjKVrB8VHF/sFxWhW9teS49lJD/JqimSmOTtg3KDbK6neNWGyYRo2IS9neCmxV1Y/l1OiT8kzRnbhs48MhGqp6naquUtW1uAvK91X11Tls6RaRnlIZl/ELetqIqu4BdojI2f6tS4FfhtriqaV1eTtwkYh0+d/pUtxY7WB8LxFEZA3wpzXYtAFXYeBfZy0TKSKX47pBXqGqwzk1zoysXkG43/5CVZeq6lrvuztxE/3tyWHL8sjqlQT6recbwCVe7yzc5Kv7cui8CHhYVXfm2LfELuAFvnwJENydMeK3LcB7gWozmyfVZ5n9tk51YqxGqM+m6GT22ziNUL9NsaMePmuc5CT5tY8NWn35DOBM3Bj6IB1VvTjiq58APqiqsbP6p9hyurh5DRCRp+DGoQ8EaiwCvo27ofrf5DNSn+tTFZ0NwNUiUhQ3xOJM4GeB2l0+TkJELgMmVDU01tkOXCKOblxrb9B1NGJPC64R5vY8++Nubs/1x1XAXX+CjifUZ5MI8dkUWzL7bIpGZp9tALl8NjQOSKDmeCRCrfFRzXFaPa699Ypfa42jvUZd6stZo5GZj2ZbcDeUu4FxnBO9PofG83DdfDYBD/nlJYEa5wIPeo3N5JhdtkLvheR8mgZuvoeNftmC67qTR+c8XNfFTbiK45QcGl3AfmBhDefiA7g/9mbgi/jZd3Po/AR3Ud4IXJrXv3CzU38PV5F/D1icU+dKXx7FZZjvzKHxKG78Yclvqz0JI07ja/7cbgK+iZscMPd/joxPckmw5Yu4caGbcBft5Tk02oEv+WN6ALgkz/EAn8eNT83qX3G2PA83O/tG3PwC5+fQeCuuRecR4EP4lo0Ujdj6LMRvUzQy+2yKRqjPJulk9tskjRC/TbEjyGdtmZtLkl/jxqhv8XXAA8Cf5NGp2GY96U8mSLLlNd6Wh7wtL82h8V5cl/+HIkvsTPhpx+L/bwdwwyZ3ktIboYrO9bhW1W34JwUkaMTWXbgW1W24Ro7vkvLErxSNBbinRWzBxRjVeiIk1qO42O+eDP6WpvFqb8tm4MM5jifUZ6teFzL4bJItIT6bpBHis2nnNcRn03Qy+WyFXlAckKARFI9U0fo8AfFRzP5BcVqCRt2vveR8EiGBcXSCRlBsFOp3M72UutwYhmEYhmEYhmEYhmE0BBumYRiGYRiGYRiGYRhGQ7FkhGEYhmEYhmEYhmEYDcWSEYZhGIZhGIZhGIZhNBRLRhiGYRiGYRiGYRiG0VAsGWEYhmEYhmEYhmEYRkOxZIRhGIZhGIZhGIZhGA3FkhGGYRiGYRiGYRiGYTQUS0YYhmEYhmEYhmEYhtFQ/h8ge0ocsApQlAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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T8kJERERERERE0pqSFyIiIiIiIiKS1pS8EBEREREREZG0puSFiIiIiIiIiKQ1JS9EREREREREJK0peSEiIiIiIiIiaU3JCxERERERERFJa0peiIiIiIiIiEhaU/JCRERERERERNKakhciIiIiIiIiktaUvBARERERERGRtKbkhYiIiIiIiIikNSUvRERERERERCStKXkhIiIiIiIiImlNyQsRERERERERSWtKXoiIiIiIiIhIWlPyQkRERERERETSmpIXIiIiIiIiIpLWlLwQERERERERkbSm5IWIiIiIiIiIpDUlL0REREREREQkrSl5ISIiIiIiIiJpTckLEREREREREUlrSl6IiIiIiIiISFpT8kJERERERERE0pqSFyIiIiIiIiKS1pS8EBEREREREZG0puSFiIiIiIiIiKQ1JS9EREREREREJK0peSEiIiIiIiIiaU3JCxERERERERFJa0peiIiIiIiIiEhaU/JCRERERERERNKakhciIiIiIiIiktaUvBARERERERGRtKbkhYiIiIiIiIikNSUvRCQhZvaVmW0wszXRMqUQZY81s2/MbLWZLTGzr82sVyHK/y3muBvMbGvM54lFOyMREREpLczsZTNbYGarzGyqmf2hEGVzzOx9M1tuZivMbJKZ3WNmtYsYyxFm5mZ2fVHKi0jRKHkhIoVxhbtXi5Z9EilgZqcAbwAvAk2AhsCtwAnR9upmVjm/Otz977nHBS4FvouJo+2unJCIiIiUCvcCzd29BtALuNvMOhdUyMwOBr4CvgX2dfdaQA9gC7B/tE+B9yJxzgOWRf+KSAlR8kJEdomZnW9m35rZ42a20sx+MrOjom0G/BO4y92fcfeV7r7N3b9294ujKtoB882sn5kdmKrzEBERkfTl7hPdfWPux2hpGbWCmBu10vzVzGaZ2VkxRe8Hnnf3e919UVTXbHe/zd2/ivZJ+F7EzKoApwB/BFqZWU4xnqaI5EPJCxEpjHujG4NvzeyImPUHADOBesBtwNtmVgfYB2gKvLmzCt39O6ATMB94xcwmm9n1ZtY4WSchIiIipY+Z/cvM1gE/AQuAD6NNjQj3IFmE1hD9zWwfM6sKHAS8lV+9hbwX+T9gDaFV6VDg3F0/MxFJhJIXIpKoG4C9CDcG/YH3zKxltG0x8Ii7b3b314ApwO+ButH2BflV7O4/u/sdwN7AJcC+wKSof2qz4j8VERERKW3c/XKgOvA74G1gY8zmW9x9o7t/DXwAnAbUJjzvLMzdyczuj8a9WGtmN8fUnei9yHnAa+6+FRgInGFm5ZNxviLyW0peiEhC3P17d18d3Ri8QOg7ely0eZ67e8zuvwB7AEujzwm1oojqmAyMA+YCbYGqxRG/iIiIlH7uvtXdhxHG0bosWr3c3dfG7JZ7H7Ic2EbMfYi7Xx+NezEYKJdH/Tu9FzGzpsCRwCvR7u8AlQgvbEQkyZS8EJGicsCin7Oi8S1yNSM0vZwCzCE0sdwpM6toZqeY2bvANKAz8CdgL3efXOyRi4iISGlXDshtAVo76iKSqxkwP0pofA+cXFBlCd6LnEN4fnrPzBYSusxWQl1HREqEkhciUiAzqxVNd1rJzMpFA2EdRujrCdAA+JOZlTezU4H9gA+jtxd/Bm4xswvMrIaZZZjZoWbWP6q7A6FbyVWENxhN3f1cd/8yrjWHiIiIlEFm1sDM+phZNTPLNLNjgTOAL2J2u8PMKpjZ74DjCWNSAFwPXGhmN5pZg6i+JkCLmPoTvRc5F7gD6Biz/B/wezOri4gk1Q5NpURE8lAeuJvQ/3MrYaCsk9x9ipkdRHir0Qr4FVgEnOLuSwHc/U0zWwPcBDwOrAcmAg9EdS8Gurr79BI8HxERESk9nNBF5CnCy9dfgKvd/Z1oAPGFhC4i84F1wKXu/hOAuw8zs26EAcVvjBqKziUkKR6P6i/wXiSahaQ58KS7L4nZ9K6ZTSckU54olrMVkTyZXmyKyK4ws/OBP7j7oamORURERMqWKHnxsrs3SXUsIpJc6jYiIiIiIiIiImlNyQsRERERERERSWvqNiIiIiIiIiIiaU0tL0REREREREQkrSl5ISIiIiIiIiJprUxMlVqvXj1v3rx5qsMQERFJKz/88MOv7l4/1XGUFbofERER2VGi9yNlInnRvHlzRo0aleowRERE0oqZ/ZLqGMoS3Y+IiIjsKNH7EXUbEREREREREZG0puSFiIiIiIiIiKQ1JS9EREREREREJK0peSEiIiIiIiIiaS2pyQsz62FmU8xsupndmMf2P5vZJDMbb2afm9meMdvOM7Np0XJezPrOZvZjVOdjZmbJPId4azdu4fJXfuCdsfNYu3FLSR5aREREREREpPBGjoR77oHly1MdSZElLXlhZpnAk0BPoA1whpm1idttDJDj7h2AN4H7o7J1gNuAA4CuwG1mVjsq82+gL9AqWnok6xzy8vOvaxn9ywquGjSWTnd9ymUv/8AH4xewbpMSGSIiIiIiIpKGrrwSbr4Z9t4bHn0UNm1KdUSFlsyWF12B6e4+0903AYOAE2N3cPcv3X1d9HE40CT6+VjgU3df5u7LgU+BHmbWGKjh7t+5uwMvAicl8Rx20C6rJv+9sRtvXHoQfbo0ZdQvy/njwNF0vuszrhg4mo8nLGDD5q0lGZKIiIiIiIhI3saNg++/hyuugE6d4OqroV07GDIE3FMdXcLKJbHuLGBOzOe5hJYUO3MR8FE+ZbOiZW4e63dgZn0JLTRo1qxZYeIuUEaG0aV5Hbo0r8OtJ7RlxM/LeH/8fD6esJD3xy+gaoVMjm7TkOM77MFhretRsVxmsR5fREREREREJCH9+kHFinDHHVC7Nnz0EVx3HfTuDYcfDg89BJ07pzrKAiUzeZHXWBR5pnXM7GwgBzi8gLIJ1+nu/YH+ADk5OUlLJ2VmGAe1rMtBLetyR6+2DJ8ZJTImLuSdsfOpXrEcx7RtyPEdGnPo3vWpUE5jpIqIiIiIiEgJWLMGXn4ZTj0V6tQJ6447Drp3h6efhttug5wcOOecMCZG06apjTcfyXySngvEnnkTYH78TmZ2NHAT0MvdNxZQdi7bu5bstM5UKZeZwaGt6vGP/+vAyJuOZsAFXTi2XSM+nbSICweMoss9n3H9m+P4euoSNm/dlupwRUREREREZHf22muwejVccslv15crB5ddBtOmwY03wuuvQ+vWcMstYf80ZJ6kPi5mVg6YChwFzANGAme6+8SYfbIJA3X2cPdpMevrAD8AnaJVo4HO7r7MzEYCVwLfAx8Cj7v7h/nFkpOT46NGjSq2cyusjVu2Mmzar3wwfgGfTFrEmo1bqF2lPHef1J7fd2icsrhERKRsM7Mf3D0n1XGUFam+HxERkTKoa1dYuxYmTID8JuqcNQv+9jd49VVo2BDuugsuvBAykz8EQqL3I0lreeHuW4ArgKHAZOB1d59oZneaWa9otweAasAbZjbWzN6Nyi4D7iIkPEYCd0brAC4DngGmAzPYPk5G2qpYLpOj9mvIP0/vyKibj6b/OZ2pV60iD34yhWQlj0RERERERKQMGzMmTJF6ySX5Jy4AmjeHgQNh+HBo2RL69oXsbPjkkxIJNRHJHPOCqEXEh3Hrbo35+eh8yj4HPJfH+lFAu2IMs0RVKp9J97aNWLF+M9e/OZ6xc1aQ3ax2wQVFREREREREEtWvH1SqFMazSNQBB8CwYfDWW3DDDXDssdCjBzz4ILRtm7xYE6DRI1OkZ7tGVCyXwZAx81IdioiIiIiIiOxOVq+GV16B008PM4wUhhmccgpMmhSSFsOHQ4cOcOmlsGhRcuJNgJIXKVK9UnmOadOQ98Yv0OCdIiIiKWBmPcxsiplNN7Mb89j+ZzObZGbjzexzM9szZtt5ZjYtWs4r2chFREQK8OqrYaaR+IE6C6NiRbj2Wpg+Ha64Ap59Flq1gm++Kb44C0HJixTqnZ3FsrWb+GbqklSHIiIiUqaYWSbwJNATaAOcYWZt4nYbA+S4ewfCAOP3R2XrALcBBwBdgdvMTH1ARUQkffTrB+3bw4EH7npddevCo4/CxImhRUanTgWXSQIlL1LosNb1qVO1Am+r64iIiEhJ6wpMd/eZ7r4JGAScGLuDu3/p7uuij8PZPl37scCn7r7M3ZcDnwI9SihuERGR/I0aBaNHJzZQZ2G0bg3PPQfVqhVfnYWg5EUKlc/M4IQOjfls0iJWbdic6nBERETKkixgTsznudG6nbmI7TOcJVzWzPqa2SgzG7VkiVpaiohICejXD6pUgbPPTnUkxUrJixQ7KTuLjVu28fGPC1MdioiISFmS16uoPOcvN7OzgRzCFO+FKuvu/d09x91z6tevX6RARUREErZqVRjvok8fqFkz1dEUKyUvUqxj01q0qFeVweo6IiIiUpLmAk1jPjcB5sfvZGZHAzcBvdx9Y2HKioiIlLhXXoG1a3dtoM40peRFipkZJ3XMYvjPS5m/Yn2qwxERESkrRgKtzKyFmVUA+gDvxu5gZtlAP0LiYnHMpqFAdzOrHQ3U2T1aJyIikjruoctIx47QpUuqoyl2Sl6kgd7ZWbjDO2P10kZERKQkuPsW4ApC0mEy8Lq7TzSzO82sV7TbA0A14A0zG2tm70ZllwF3ERIgI4E7o3UiIiKpM2IEjBtX/AN1polyqQ5AoFndKnTeszaDx8zl0sP3wnbDC01ERCTduPuHwIdx626N+fnofMo+BzyXvOhEREQKqV8/qFoVzjwz1ZEkhVpepIne2VlMXbSGSQtWpToUERERERER2VVr1sBf/gIzZiT/WCtXwqBBcMYZUKNG8o+XAkpepInft29M+Uxj8GgN3CkiIiIiIlLqPfQQPPggnHoqbNxY8P674uWXYf363XKgzlxKXqSJ2lUrcOQ+DXhn3Hy2bN2W6nBERERERESkqBYtComLNm1gzBi46abkHSt3oM5OnSAnJ3nHSTElL9JI7+wslqzeyH9nLE11KCIiIiIiIlJUd98dWkIMHgyXXx5aYXzySXKONXw4/Pjjbt3qApS8SCtH7tuAGpXKMXiMuo6IiIiIiIiUSjNmwFNPwcUXQ+vW21tgnHsuLF5ccPnC6tcPqlUL413sxpKavDCzHmY2xcymm9mNeWw/zMxGm9kWMzslZv2R0ZRkucsGMzsp2jbAzH6O2dYxmedQkiqVz+T3HRrz8YSFrN24JdXhiIiIiIiISGHdfDNUqAC3RhNYVa4Mr74KK1bAhReGbh7FZflyeO01OOssqF69+OpNQ0lLXphZJvAk0BNoA5xhZm3idpsNnA8MjF3p7l+6e0d37wh0A9YBsW1s/pK73d3HJuscUqF3dhPWb97KJ5MWpjoUERERERERKYwffgizfvz5z9C48fb1HTrA/ffDBx/Ak08W3/Feegk2bNjtu4xAcltedAWmu/tMd98EDAJOjN3B3We5+3ggvxEqTwE+cvd1yQs1feTsWZusWpUZPGZ+qkMRERERERGRwrjxRqhbN0yRGu/KK6FnT7juOpgwYdePlTtQZ5cukJ296/WluWQmL7KAOTGf50brCqsP8GrcunvMbLyZPWxmFfMqZGZ9zWyUmY1asmRJEQ6bGhkZRu/sLIZNW8LiVRtSHY6IiIiIiIgk4pNP4LPP4JZboEaNHbebwYABUKtWGJ9i/fpdO96338KkSWWi1QUkN3lheawrVOceM2sMtAeGxqz+K7Av0AWoA9yQV1l37+/uOe6eU79+/cIcNuVOys5im8O749T6QkREREREJO1t2wY33ADNm8Oll+58vwYNQgJjwgS4/vpdO2a/fiFJ0qfPrtVTSiQzeTEXaBrzuQlQ2Kfx04DB7r45d4W7L/BgI/A8oXvKbmXvBtXo0KSmZh0REREREREpDQYNgrFjwxSpFfPsHLBdjx5wzTXwxBPw/vtFO97SpfDGG3D22VC1atHqKGWSmbwYCbQysxZmVoHQ/ePdQtZxBnFdRqLWGJiZAScBxdBZKP2c1DGLifNXMXXR6lSHIiIiIiIiIjuzcSPcdBPsv3/i05Xee2/Y/4ILYMGCwh/zxRfDcctIlxFIYvLC3bcAVxC6fEwGXnf3iWZ2p5n1AjCzLmY2FzgV6GdmE3PLm1lzQsuNr+OqfsXpkoy5AAAgAElEQVTMfgR+BOoBdyfrHFLphP33IDPD1PpCREREREQknfXrB7NmwX33QUaCj9gVK8LAgbB2LZx3Xuh2kqjcgToPPDDMYlJGJLPlBe7+obu3dveW7n5PtO5Wd383+nmkuzdx96ruXtfd28aUneXuWe6+La7Obu7e3t3bufvZ7r4mmeeQKvWrV+R3rerxzph5bNtWjPMAi4iIiIiISPFYtQruugu6dYPu3QtXtk0bePhh+PRTeOSRxMt98w1MmVKmWl1AkpMXsmt6Z2cxf+UGvv95WapDERERERERkXgPPgi//hpaXVhec1YUoG9fOOmkMMXqmDGJlenXD2rWhNNOK/zxSjElL9JY9zaNqFohkyHqOiIiIiIiIpJeFi6Ehx6C00+HnJyi1WEGzzwD9euH8TLWrs1//19/hbfegnPOgSpVinbMUkrJizRWuUImPdo15sMfF7Bh89akHOPnX9dy41vjmbFkt+x9IyIiIiIikhx33gmbNoUZRnZF3brw0kswdWqYhSQ/L7wQjlnGuoyAkhdpr3d2Fqs3buHzyYuLve6FKzdw9jPfM2jkHHo9Poz3xxd2JlsREREREZEyaOpU6N8/JBH23nvX6+vWDW64AZ5+OrSsyIt7OObBB0O7drt+zFJGyYs0d1DLujSsUZHBY+YWa70r1m3inGe/Z8W6TTx9bg77Nq7BFQPHcNs7E9i4JTmtPERERERERHYLN98MlSrBLbcUX5133BG6n1x8MczN4/nvq69C0qQMtroAJS/SXmaGcWLHLL6asoRlazcVS53rNm3hggEj+WXpOp4+L4dj2jRkUN8D+cOhLXjhu1847anvmLt8XbEcS0REREREZLcyYgS88QZcdx00bFh89VaoEKZP3bQpjGmxNe6lcr9+ULs2nHpq8R2zFFHyohTonZ3Flm1eLN06Nm3ZxqUvj2bcnBU8dkY2B7esB0D5zAxuPr4NT53dmZlL1vL7x4bxxU+Ldvl4IiIiIiIiuw330L2jfn249trir79VK3jiidDK4v77t69fvBjefhvOPRcqVy7+45YCSl6UAvs1rsG+jaozeBdnHdm2zbn2jXF8M3UJ957cnh7tGu2wT492jXj/T4fSpHZlLhwwivs+/oktW7ft0nFFRERERER2Cx9/HBILt94K1asn5xjnnRdmMLnlFvj++7BuwADYvLnMdhkBJS9Kjd7ZWYyZvYKffy1g6pydcHduf28i742bz4099+X0Ls12uu+edavy1mUHc0bXZvz7qxmc9cz3LF61oaihi4iIiIiIlH5bt4ZWF3vtBX37Ju84ZvDUU5CVBWeeCStXhoE6f/c72G+/5B03zSl5UUr06rgHZjCkiK0vHvlsGi9+9wuXHLYXlx7essD9K5XP5N6T2/Pw6fszfu5KjntsGP+d8WuRji0iIiIiIlLqDRwIP/4I99wTxqdIplq14JVXYNasMBPJjBllutUFKHlRajSuWZmDW9ZlyNh5uHuhyg749mce/Xwap3Zuwo099y1U2d7ZTXjnikOoWbkcZz/zPU9+OZ1t2wp3fBERERERkVJtw4Yww0inTnDaaSVzzEMPDcccPRrq1oX/+7+SOW6aUvKiFDmpYxa/LF3H6NkrEi7zzth53P7eJI5p05B7T26PmRX6uK0bVufdKw7lhP334IGhU7johZEsL6aZT0RERERERNLev/8Ns2fDffdBRgk+Rt9yS+g6cuedYWrWMkzJi1KkR7tGVCqfweAxecz5m4cvpyzm2tfHcUCLOjx+RjblMov+665asRyPnN6Ru09qx7fTl3L848MYM3t5kesTEREREREpFVauhLvvhmOOgaOPLtljlysXuo9cfnnJHjcNKXlRilSvVJ5j2jTi/fEL2LQl/xlAfvhlGZe9/AP7NKrOM+flUKl85i4f38w4+8A9eeuyg8nIgNP6fcfz3/5c6G4sIiIiIiIipcb998OyZfCPf6Q6kjJNyYtS5uTsLFas28xXUxbvdJ+fFq7igudH0rhmZV64sCvVK5Uv1hjaN6nJ+1f8jsNbN+CO9yZxxcAxrN6wuViPISIiIiIiknLz58PDD8MZZ4TxLiRlkpq8MLMeZjbFzKab2Y15bD/MzEab2RYzOyVu21YzGxst78asb2Fm35vZNDN7zcySPMxrejm0VT3qVq3AkLF5zzoyZ9k6zn12BJUrZPLihV2pV61iUuKoWaU8T5/bmb/23JePJy6k1xPf8uWUxWzVYJ4iIiIiIpJK7mFa0+Jwxx2wZUvoNiIpVS5ZFZtZJvAkcAwwFxhpZu+6+6SY3WYD5wPX5VHFenfvmMf6+4CH3X2QmT0FXAT8u1iDT2PlMzM4Yf89GDhiNivXb6Zm5e2tKpas3sjZz37Pxi3beOPSg2hap0pSYzEzLjm8JdnNanPVoDFc8PxIsmpV5tScJpyW05Q9alVO6vFFRERERER2cPLJ8MEH0KIF7L33jkvz5lA+gdbpP/0Ezz4bxpvYa6+khy35S1ryAugKTHf3mQBmNgg4Efhf8sLdZ0Xb8h/AIWJhqoxuwJnRqheA2ylDyQuA3tlZDPjvLD76cQF9ujYDYNWGzZz33AgWr9rIy384gNYNq5dYPF1b1OHrvxzJZ5MX8eqI2Tz6+TQe/Xwah7euT58uzThqvwaU34XBQkVERJLBzHoAjwKZwDPu/o+47YcBjwAdgD7u/mbMtq3Aj9HH2e7eq2SiFhGRfI0dC0OGQM+eULUqTJ8O33wDa9Zs3yczE/bcE1q23DGxsdde22f1uOkmqFIlzPghKZdQ8sLM2rn7hELWnQXMifk8FzigEOUrmdkoYAvwD3cfAtQFVrj7lpg6s3YSc1+gL0CzZs0KGXp669CkJnvVr8rbY+bRp2szNmzeyh9eGMW0xat55rwudN6zdonHVKFcBse1b8xx7RszZ9k6Xh81h9dHzeHSl3+gXrWKnNK5CX26NKV5vaolHpuIiOzeinKfksQWoiIikkoPPQTVqsHAgVCrVljnDosXh0TG9OkwY8b2n199FVas2F7eDJo0Ca0z/vOfMEVp/fopORX5rURbXjwVjS0xABjo7isK2B/A8lhXmAERmrn7fDPbC/jCzH4EViVap7v3B/oD5OTk7FYDMZgZvTtm8dCnU/ll6Vruen8yI2ct49E+2RzeOvV/WE3rVOHa7vtw1VGt+HrqEl4dMYen/zOTp76ewUF71aVP16Yc27ZRscyAIiIiQtHuU4q9haiIiKTYnDkwaBBceeX2xAWEhETDhmE55JAdyy1btj2ZEbsceihcc03JxS/5Sih54e6Hmlkr4EJglJmNAJ5390/zKTYXaBrzuQkwP9HA3H1+9O9MM/sKyAbeAmqZWbmo9UWh6tydnJQdkhd9+g9nwcoN3HViW3rtv0eqw/qNcpkZHLVfQ47aryGLVm3gzR/mMmjkbK4aNJZaVcrTOzuLM7o2K9EuLiIisvsp4n1KMlqI7mB3bgkqIpJ2HnkktLK46qrClatTB7p2DYukrYQHInD3acDNwA3A4cBjZvaTmZ28kyIjgVbR7CAVgD7AuzvZ9zfMrLaZVYx+rgccAkxydwe+BHJnJjkPeCfRc9idNK1ThS7Na7Ng5QauObo15xzUPNUh5athjUr88ci9+fq6I3n5ogM4ZO96vDz8F7o//A29//Utr4+cw7pNWwquSEREJA9FuE8pjhaiOYRxuB4xs5Y7iau/u+e4e059NTsWEUmeFSugf384/fQwnoXsdhId86IDcAHwe+BT4AR3H21mewDfAW/Hl3H3LWZ2BTCUMBDWc+4+0czuBEa5+7tm1gUYDNQGTjCzO9y9LbAf0C9qpplBeKOR24zzBmCQmd0NjAGeLfLZl3J3ntiO8XNXcFpO04J3ThMZGcahrepxaKt6LF2zkbdHz2PQyNlc/9Z47nx/En8+pjUXHtoi1WGKiEgpUpT7FJLTQnRGkU5ARER2Xf/+YVDO6/Iapkh2BxYaMxSwk9k3wNPAm+6+Pm7bOe7+UpLiKxY5OTk+atSoVIchO+HujPplOQ98PIXx81Yw6uZjqFYxmRPhiIgIgJn9ELUeKNWKcp9iZuWAqcBRwDxCi9Ez3X1iHvsOAN7PnW3EzGoD69x9Y9RC9DvgxLjBPneg+xERkSTZtClMi7rffvDZZ6mORgop0fuRRLuNHEcYAGt9VHmGmVUBSPfEhaQ/M6NL8zrc0HMfNmzexscTFqY6JBERKV0KfZ8SjZ2V20J0MvB6bgtRM+sV1dPFzOYCpxJahOYmNvYjjK0xjtCd9R8FJS5ERCSJXn0V5s+Hv/wl1ZFIEiX6evsz4Gggd3LcKsAnwMHJCErKpk7NatOsThUGj5nLKZ2bpDocEREpPYp0n+LuHwIfxq27NebnkYTuJPHl/gu037WQRUSkWLjDgw9C+/bQvXuqo5EkSrTlRSV3z70hIPq5SnJCkrLKzDgpO4v/zljKgpXrCy4gIiIS6D5FRKSsGjoUJkwIY11YXmMxy+4i0eTFWjPrlPvBzDoDerqUYtc7Owt3eGdsmZwBV0REikb3KSIiZdUDD0BWFvTpk+pIJMkS7TZyNfCGmeU+UTYGTk9OSFKWtahXlexmtRg8eh6XHLYXpuypiIgUTPcpIiJl0ejR8MUXcP/9UKFCqqORJEsoeeHuI81sX2AfwrzoP7n75qRGJmXWydlZ3PLORCYtWEXbPWqmOhwREUlzuk8RESmjHnwQqleHvn1THYmUgES7jQB0AToQ5jE/w8zOTU5IUtYd32EPymcaQ8bMS3UoIiJSeug+RUSkLPnlF3j99ZC4qKkXnmVBQi0vzOwloCUwFtgarXbgxSTFJWVY7aoVOGKfBrwzdj439tyPzAx1HRERkZ3TfYqISBn0yCNhgM6rrkp1JFJCEh3zIgdo4+6ezGBEcvXOzuLTSYv4dvqvHNa6fqrDERGR9Kb7FBGRsmT5cnj6aTjjDGjaNNXRSAlJtNvIBKBRMgMRidVt3wZUr1SOweo6IiIiBdN9iohIWfLUU7B2LVx7baojkRKUaMuLesAkMxsBbMxd6e69khKVlHmVymdyfIfGDBkzn7tP2kLVioleqiIiUgbpPkVEpKzYuBEeewy6d4f99091NFKCEn0ivD2ZQYjkpXd2E14dMYdPJi2kd3aTVIdTKNu2OY98NpW9G1an1/57pDocEZHd3e2pDkBERErIK6/AwoXw0kupjkRKWKJTpX5tZnsCrdz9MzOrAmQmNzQp63L2rE2T2pV5e/S8UpW8cHduGvIjr46YQ1atypzQoTFmGnRURCRZdJ8iIlJGbNsWpkft2BGOOirV0UgJS2jMCzO7GHgT6BetygKGJCsoEYCMDOOkjll8O/1XFq/akOpwEuLu3Pn+JF4dMYcOTWoyb8V6Js5fleqwRER2a7pPEREpIz76CCZPhuuuCzONSJmS6ICdfwQOAVYBuPs0oEGyghLJ1btTFtsc3hk7P9WhJOTBT6bw/LezuPCQFjx3fhcyDD6ZuDDVYYmI7O50nyIiUhY88ECYXeS001IdiaRAosmLje6+KfeDmZUjzJ+eLzPrYWZTzGy6md2Yx/bDzGy0mW0xs1Ni1nc0s+/MbKKZjTez02O2DTCzn81sbLR0TPAcpBRqWb8a+zepydulYNaRJ76YxpNfzuDMA5pxy/H7Ua9aRXKa12HoxEWpDk1EZHdXpPsUEREpRUaOhK+/hquvhvLlUx2NpECiyYuvzexvQGUzOwZ4A3gvvwJmlgk8CfQE2gBnmFmbuN1mA+cDA+PWrwPOdfe2QA/gETOrFbP9L+7eMVrGJngOUkr1zs5i8oJV/LQwfbtfPPOfmTz4yVROzs7i7hPb/W+Mi2PbNmLKotX8/OvaFEcoIrJbK/R9ioiIlDIPPgg1a8LFF6c6EkmRRJMXNwJLgB+BS4APgZsLKNMVmO7uM6O3IYOAE2N3cPdZ7j4e2Ba3fmrU5BN3nw8sBuonGKvsZk7Yfw/KZRiD07T1xcvDf+HuDyZzXPtG3H9KBzIytve/696mIQBD1XVERCSZinKfIiIipcXMmfDmm3DJJVC9eqqjkRRJKHnh7tvc/Wl3P9XdT4l+Lqg5ZhYwJ+bz3GhdoZhZV6ACMCNm9T1Rd5KHzaziTsr1NbNRZjZqyZIlhT2spJG61SpyeOv6vDNmPlu3pVcr4Dd/mMvNQybQbd8GPHJ6NuUyf/sn1bROFdruUUPJCxGRJCrifYqIiJQWjzwCmZnwpz+lOhJJoURnG/nZzGbGLwUVy2NdoW4kzKwx8BJwgbvnts74K7Av0AWoA9yQV1l37+/uOe6eU7++Gm2UdidlZ7Fw1QaGz1ya6lD+5/3x87n+zXEcunc9/nVWJyqUy/vPqUfbRoyZvYJFpWTGFBGR0qaI9ykiIlIaLF0Kzz4LZ54JWYV+Fy67kUS7jeQQkgVdgN8BjwEvF1BmLtA05nMTIOEpI8ysBvABcLO7D89d7+4LPNgIPE/oniK7uWPaNKR6xXK8PTo9uo58NmkRVw8aS+c9a9P/3M5UKp+5032PbdcIgE8maeBOEZEkKcp9ioiIlAb//jesWxemR5UyLdFuI0tjlnnu/gjQrYBiI4FWZtbCzCoAfYB3EzletP9g4EV3fyNuW+PoXwNOAiYkUqeUbpXKZ9KzfSM+nrCA9Zu2pjSW/0xbwuWvjKbtHjV47vwuVKlQLt/9WzWoRot6VRk6QV1HRESSoYj3KSIiku42bIDHH4eePaFdu1RHIymWaLeRTjFLjpldCuQ7Uoq7bwGuAIYCk4HX3X2imd1pZr2ieruY2VzgVKCfmU2Mip8GHAacn8eUqK+Y2Y+EQbnqAXcX7pSltOqd3YS1m7byyaTUJQG+n7mUi18cxV71q/LChV2pXqngaZrMjGPbNmL4zKWsXLe5BKIUESlbinKfIiIipcBLL8HixWp1IQDk/8p4u4dift4CzCIkGPLl7h8SRvyOXXdrzM8jCd1J4su9zE6ae7q73qSUUQe0qENWrcoMHjOPEzuWfH+3MbOXc+GAkWTVqszLfziAWlUqJFz22LYNeerrGXz+0yJO7rTDJS8iIrumSPcpIiKSxrZtg4cegk6d4MgjUx2NpIGEkhfurqtFUi4jwzix4x70+2YmS1ZvpH71PCeaSYqJ81dy3nMjqFutIq/84UDqVSvcsfdvUouGNSoydOJCJS9ERIqZ7lNERFJs6VL49FOYMAGOOAIOPxzKF9xCOV/vvw9TpsDAgWB5zQUhZU1CyQsz+3N+2939n8UTjkj+emdn8a+vZvDuuPlcdGiLEjnmtEWrOefZEVSrWI5X/nAAjWpWKnQdGRlG9zaNeOOHOazftJXKFXY+wKeIiBSO7lNERErY1q0wciR8/HFYRoyA3Bmq77kHateG44+Hk0+G7t2hSpXCH+OBB2DPPeHUU4s3dim1CjPbyGVAVrRcCrQh9CdVn1IpMa0aVqddVg0Gj5lbIseb9etaznrmezIzjFcuPpCmdYrwH2+kR7tGbNi8ja+nLinGCEVEBN2niIgk34IF8MIL0KcPNGgABx0Ed94ZWkXcdhsMHw6rV8OQIdCrV2g50bs31KsXkhgvvQTLlyd2rOHDYdgwuOYaKJfoSAeyu0v0SqgHdHL31QBmdjvwhrv/IVmBiexM7+wm3PX+JKYtWk2rhsm7J527fB1nPfM9m7du47VLDqJFvaq7VF/XFnWoWbk8n0xcSI9o+lQRESkWuk8RESlumzfDf/+7vXXF2LFhfaNGITnRowccfTTUrfvbcieeGJbNm+Gbb2Dw4JDQGDw4JCKOPDIkNU46CRo3zvvYDz4ItWrBRRcl9xylVEm05UUzYFPM501A82KPRiQBvfbfg8wMY/CYeUk7xqJVGzjrme9ZtWEzL110AK2LIUlSPjODo/ZrwGeTF7F567ZiiFJERCK6TxERKQ6//AL9+4fkQt26YfyKBx+EmjXh3ntDAmP+fHj+eTj99B0TF7HKl4ejjoInnoDZs0NrimuvhVmz4PLLISsLDj44dA+ZPn17uenT4e234bLLoFq1ZJ+xlCKJtrx4CRhhZoMBB3oDLyYtKpF81K9ekd+1qsc7Y+dzXfd9yMgo3gF8Fq8OiYslqzfy0kUH0C6rZrHVfWzbRrw9eh7fz1zGoa3qFVu9yTZ5wSqeHfYz7rBPo2q0blidfRpVp1GNSpgGUBKR1NN9iohIUa1eDY8/Di+/DJMnh3V77glnnhlaV3TrBjVq7NoxMjLggAPCcu+9MGlSaInx9ttw/fVhadcudC/56aeQ+Ljyyl0/N9mtJDrbyD1m9hHwu2jVBe4+JnlhieSvd3YWVw0ay/c/L+OglvlkfAtpwcr1nPX09yxYuYHnzu9C5z1rF1vdAIe1qk/l8pl8PHFBqUheTJi3ksc+n8YnkxZRrWI5qlTI5K3R28cbqV6pHPs0rE7rRtXDvw2r07phNeoWcjYWEZFdofsUEZEiWLsWnnwS7r8/zBbSrRv07RsSFvvsk7wZPsygbduw3HxzaIkxZEhIZNx1Vxj488ILd96lRMqswox+UgVY5e7Pm1l9M2vh7j8nKzCR/HRv04iqFTIZPGZusSUv5ixbx5nPDGf52s28eFFXujSvUyz1xqpcIZPDW9fnk4mLuLNXu2JvNVJcxs1ZwWOfT+PznxZTo1I5rj66FRcc3IKaVcqzfO0mpi5azdRFq5myaDVTF67hg/ELGLh+9v/K16tWIUpkhBYauUmN6pV2ccosEZGd032KiEgi1q2Dp56C++6DxYuhZ0+4/Xbo2jU18TRvDldfHZbFi+GLL8IMJSJxEp0q9TbCSN77AM8D5YGXgUOSF5rIzlWukEmPdo356MeF3HliOyqV37WpR2f9upYznx7Omo1beOmirmQ3K94WF7GObdeQjycuZOzcFXRK4nGKYvTs5Tz2+TS+mrKEmpXLc+0xrTnvkObUiEk61K5agQP2qssBe21PGrk7S1ZvZMqi1UxZmJvYWMPro+awbtPW/+2XVasyPds14qbf76fuJiJSbHSfIiKSgA0bwngW994LCxeGwTbvuCOMO5EuGjQIs5mI5CHRlhe9gWxgNIC7zzczTT0mKXVypyzeGj2XzyYv4vgOexS5numLV3Pm02FWkYEXH1isY1zkpds+DSmXYQyduDBtkhcjZy3jsc+n8Z9pv1K7Snmu77EP5xy4Z8ItJcyMBjUq0aBGJX7Xqv7/1m/b5sxbsf5/rTRG/7KCZ4b9TPVK5bnq6FbJOh0RKXt0nyIisjMbN8Jzz8E998C8eXD44fDaa3DYYamOTKRQEp1tZJO7O2EQLMxs1+aMFCkGB+5Vl0Y1KjF4dNFnHZm8YBWn9xvONodBfQ9KeuICoGaV8hzUsi5DJywk/FmlzvCZSzmj/3BOfeo7Js1fxV977suwG7px+RF7F0sXj4wMo2mdKhy1X0MuP2Jvnj63Myd3yuLhz6by8YQFxXAGIiJAEe9TzKyHmU0xs+lmdmMe2w8zs9FmtsXMTonbdp6ZTYuW84rlLEREitPmzfDMM9C6dZjdo3lz+Pxz+PJLJS6kVEo0efG6mfUDapnZxcBnwNPJC0ukYJkZxonZe/D11CUsXbOx0OV/nLuSM54eTvnMDF675ED2aVRyL+mObduIWUvXMXXRmhI7Zi5359vpv3Jav+/o038405es4ebf78ewG7pxyeEtqVqxMEPhFI6Z8ffe7cluVotrXhvHpPmrknYsESlTCn2fYmaZwJNAT6ANcIaZtYnbbTZwPjAwrmwd4DbgAKArcJuZpUdTOhGRLVtgwIAw6ObFF4eBL4cOhf/8JwzKqa67UkollLxw9weBN4G3CP1Jb3X3x5MZmEgiTs5uwpZtznvj5heq3A+/LOfMp4dTtUI5Xr/kIFrWL9k5pLu3aYgZDJ24sMSO6e58PXUJpzz1Hf/f3n2HR1VtDRz+rRRIgCT0hBY6CAEBCU0EQToKWK6KFXtFvZbrvehVsX4WxIrYe8GCBVQI6AVERSB0EoqhSYBA6CW0JPv7Yw86hsxkzmQmM4T1Ps88TDlnzZphZ2bPOvvsfdmbc9mw4wAPDWnN7Ht7c12PJsRWKN28Ib6KiY7ktcs7khAbzfXvp7Pdj8KTUkq587Of0hnIMsasNcYcASYAw4rEXW+MWQoUFtl3ADDdGLPTGLMLmA4MDMBLUUop/xUU2OVOW7WCq6+G6tXhu+9gzhw7AaYWLdQJrsRDrK4jE2nGmL7YL2elwkbLpDha1Ynnq0WbuKp7Y5/2+W3tDq55dz614yry0fVdqVc1NshZHq92fAwdGlQlLSOH2/sEf+6HGau28cIPv7N4427qJMTw6LAULkxtUOqJTv1VOz6G16/syIWvzuGWDxfy4XVdqBDl60AwpZT6Syn6KfWAjW63s7EjKfzdt56H/G4AbgBITk52kJ5SSvmosBA+/9yuGLJyJbRrZ5ceHTpUCxaqXCnx14IxpgDIE5HgTwaglB/O71CPJdl7WJNb8ikYs3/P5ap35lEnIYZPb+wWksLFMQPbJJGxeS8bd+YF9XkmL9nM1e/MJ3ffYZ44ry0z/9WLK7o1Clnh4phT61flmQvbMW/9Th78ZnnI5/9QSp2YStFPKa5H7+sHkc/7GmNeN8akGmNSa9WqVdwmSinlH2Pgyy9tsWL4cIiMhC++gIULYdgwLVyocsfXQ52HgGUi8paIvHjsUtJOwZgIS0Q6isgyV8wXRddbPOkNa1+XCIGvF3mfuPPHFVu59r10GtWozKc3diMxPqaMMizegJQkILinjuw5eJRHvs3k1PoJzLinF5d2SaZiVGiLFu6GtqvLrb2bMmH+Rt77dX2o01FKnbj86adkAw3cbtcHfD0HsTT7KqVU6RgD334LHTvCBRfYiTk/+QSWLrW3I3Q0qyqffG3Z3wEPAD8BC9wuHgVxIqzx2OGXzV0XPcf0JFc7PobuzWry1aJNFBYWf9Bs6vIt3PThAlomxjHhhn51Jw8AACAASURBVK7UrFKxjLM8XsMalTklKY5pGVuD9hxj0laxY78dcRGup2Xc3a8l/Von8uh3K/j59+2hTkcpdWJy3E8B5gPNRaSxiFQAhgOTfHy+NKC/iFRz9U/6u+5TSqngMQamTYNu3WDIENizB957D5YvtyMvtGihyjmvLVxEkgGMMe8VdykhdsAnwhKROkC8MWaOa0m094FzfX2xqvw6/7R6ZO86SPqGXcc99s3iTdz68SLa1kvgo+u7ULVShRBkWLz+KUnM37AzKJNWLt64mw/nbmDE6Y3KZAlYf0VECM9d3J5mtapw68cLWbf9QKhTUkqdIErTTzHG5AMjsUWHFcBnxpgMEXlERIa64ncSkWzgQuA1Eclw7bsTeBRbAJkPPOK6TymlgmPmTLu86YABsGULvPGGnd/iyishKngrxSkVTkoqz3197IqITHQY2+fJrBzsW891vcSYInKDiKSLSHpubq7PSasTU//WScRGR/LVouy/3f9Z+kb++eliUhtW4/1ruxAfEx2iDIs3MCUJY+CHzMCOvsgvKOS+L5dRO64id/VrEdDYwVClYhRvjkglQuD699PZe+hoqFNSSp0YStNPwRjzvTGmhTGmqTHmcdd9DxpjJrmuzzfG1DfGVDbG1DDGpLjt+7Yxppnr8k4gXoxSSh3n11+hTx/o3RvWrIFx42D1arjuOogOr36tUsFWUvHCfT6JJg5jB2MiLJ0gSxWrcsUoBrZJ4tulWzh0tACAD3/bwL1fLOWMZjV59+rOVKkYflXpVnXiaFA9lqkBnvfivTkbyNyyl9FDUogLs4KNJw2qV+KVyzqyfvsB7vhkEQUeTgFSSik3pemnKKVU+EpPh0GDoHt3e1rI2LG2eHHLLVAx9Kc/KxUKJRUvjIfrvgjGRFjZruv+xFTl3Hkd6rHvUD4zVm7jrZ/X8d+vl9PnlNq8cWUqsRXCZ5JKdyLCgNZJ/Jq1g30BGm2wZc9Bxk5bRe+WtRjYJikgMctKt6Y1GD00hRmrcnl66spQp6OUCn+l6acopVT4WbLErhTSqRPMmwdPPglr18Kdd0Js6FbJUyoclHQoup2I7MUe2Yh1Xcd12xhj4r3s++dEWMAm7ERYl/qYVxrwhNsknf2BUcaYnSKyT0S6AnOBK4GXfIypyrnuzWpSO64iD03KYNu+wwxqk8QLwzuE7USVxwxsk8SbP69jxqpchrarW+p4D0/KpMAYHhnWhhNxMZ7LuzZkVc4+XvtpLS2T4jj/tPol76SUOlmVpp+ilFL+McYWFhYuhOrVoWZNqFXL/luzJlTwY361zEwYPRo+/xwSEuCRR+COOyBeP8aUOsZr8cIY4/fhamNMvogcmwgrEnj72ERYQLoxZpKIdAK+AqoBQ0TkYWNMiqtIcWwiLPj7RFg3A+8CscAU10UpIiOEYe3r8sbsdQxrX5dnL2xHVGR4Fy4ATkuuRs0qFUnLyCl18eLHFVuZmpHDvQNb0qB6pQBlWPYeHNKarG37+c+Xy2hcszIdkquVvJNS6qRTmn6KUko5lp0NH3xgV/hYtcrzdvHxfxUzSvr30CE7uuLjj6FyZbj/frj7bqimfR+lihK7aEf5lpqaatLT00OdhioDew8dZcbKbZxzal0iI06cUQejvlzGpMWbWPBAP2Ki/euL5x3Jp9/Yn6hUIZLvbu8R9iNOSrLrwBGGjvuZQ0cLmTzyDJISYkKdklLljogsMMakhjqPk4X2R5Q6AeXlwZdf2oLFjz/aURdnnAEjRkD//rB/P+Tmwvbt3v/NzYXDHlaXi42FkSPh3nttQUOpk4yv/ZHwm8FQqVKIj4lmWHtfF7UJHwNSEvlk3h/8krWdPq0S/Yrxwo+/s2n3QT67sdsJX7gAqFa5Am+N6MR5437hhg/S+ezGbn4XdpRSSimlfFZYCD//bAsWn38O+/ZBo0bwwAN2adKmTZ3HNAYOHDi+qHHgAJx3HiSdWPOUKRUKWrxQKgyc3rQmcRWjSMvI8at4sTJnL2/NXsdFqfXp3Lh6EDIMjRaJcbwwvAPXf5DOvV8s5YXh7U/IeTyUCoSdB45QvbIf51ErpZTyzdq18P779rJuHVSpAhdeaEdZ9OgBEaU4OCRi41WpYgshSinHTvzDs0qVAxWiIjirVW1+WLGN/IJCR/sWFhru/2o5cTFRjBrUKkgZhk7f1onc078lk5Zs5pWZa0KdjlIhsXFnHqc/+SOfp28MdSpKKVW+7N0Lb70FPXvaERWPPGL//eADyMmBt9+GM88sXeFCKRUQ+leoVJgYkJLEzgNHSN+wy9F+n6VvZMGGXdw3uBXVyulR2Vt6NWVou7qMmbaK6ZlbQ52OUmVu7PTV9jTr5noutFLFys+Hjz6CQYNg+fJQZ6PCXUEBTJ8Ol11mT9e47jrYuhWeeAI2bLCPXX65nUBTKRU29LQRpcLEmS1qUSEqgqnLc+japIZP+2zff5j/m7KSLo2r84+O5XdJURHh6X+cyvodB/jnhEV8eUt3WibFhTotpcpExuY9fL14Ezf2bEqdhNhQp6NUeDl0yM5L8NRTdpg/2GUmJ0wIbV4qOA4fhm3bYPduOw9F0cvevcXfX/Sx/fvtHBRVq9pTQkaMgC5d7KkdSqmwpcULpcJE5YpR9Gxei+mZW3loSGuf5nZ44vsV5B3J5/Hz2pT7uSBioiN5/YpUhr78M9e9P5/vb+9BXEx0qNNSKuienLKShNhobu7lxwRxSpVX+/fD66/DmDGwZQt07gzPPw8zZ8JLL8HmzVC3dMuPqzKSn28nrszJsaMfcnL+uhS9vXt3yfEqVLBLlcbF/XWpUcPOM+F+X7t2cM45EKOrmSl1otDihVJhZEBKIj+s2MryTXtpWz/B67a/rtnOlws3MbJ3M5rVPjlGISQlxDD+8tO4YPwcXp6RVS7n+FDK3c+/b2f279v579mtSIjVYp1S7NoFL78ML7wAO3bAWWfZuQnOOsseNW/TxhYxXn3Vzl2gwsPhw3aZ0VmzbLHJvTiRm2tHQRQVF2dP6UhMtP+vffva27Vr2xETRQsUxy4VyucptEopLV4oFVb6tkokMkJIy8jxWrw4nF/Af79eTnL1Sow8q1kZZhh6HRtW58KO9Xn753UM75RM45p6PqoqnwoLDU9OXUG9qrFc3rVhqNNRKrS2boWxY+GVV+yoiyFD4L77oGvXv2/XpIk9mv7aa3D//VCxYmjyVfb0jClT4Kuv4Lvv7O0KFeyImMRE+391+ul/FSiSkv66JCZCpUqhfgVKqTCjxQulwki1yhXo3Kg6UzNyuGdAS4/bvTZrLWtzD/Du1Z2IiY4swwzDw78GtuT7ZVt4/LsVvDkiNdTpKBUUk5duZvmmvYy9qN1J+XeuFGAnT3zmGbsaxJEjcNFFMGoUnHqq531uvx369YPPPoMrrii7XBVs3w6TJtmCxfTpdsRFrVpw8cVw/vl2hIwWlJRSftLVRpQKMwPbJJG1bT9rcvcX+/j67Qd4eUYWZ59ah14ta5dxduGhdlwMt/Vpzg8rtvLT6txQp6NUwB3OL2DMtFW0qhPPue3rhTodpcreypVw1VXQrJmd2+Lyy+19n3zivXAB0KcPtGoFL75Y/OkIKrA2brTvde/edsTEtdfCsmVw883w00/2NJE33rArwWjhQilVClq8UCrM9E9JBCAtI+e4x4wxPPDNcipGRvDgOa3LOrWwcnX3RjSsUYlHv83kaEFhqNNRYWjJxt3kHckPdRp++XjuH2zceZD/DDqFiIjyPRmv8tOyZXDrrXaVjY8/htmz7SiFo0dDnVnpLFoEF14IrVvbkRO33gpr1tgfv82b+xZDBG67DdLTYe7c4OZ7slqxwi4r2qkTJCfDHXfYuSvuuw8WLrQrvzz3HPToAZE6ckwpFRh62ohSYaZOQizt6ieQlrGVW3r9fT6LyUu3MPv37Tw8NIXE+JN7duyKUZH89+zWXP9+Oh/9toGrujcOdUoqjMxYuY2r353P0HZ1efGSDqFOx5F9h47y0v+yOL1pDXo2rxnqdFS4Wr/ejkLYtevv94tAnTrQoIH9UdmgwV+XY7dr14aIMDp+tX8/pKXZU0OmTLETMY4aZX8Q1/ZzhOEVV9gYL754/LwYyjljYMEC+PJLe0rIypX2/i5d4Mkn4bzzoEWL0OaolCr3tHihVBjqn5LEM2mr2LLnIHUSYgHYc/Aoj0zO5NT6CTp5n0vfVrXp0bwmY6evZmj7elSvrDOMK9i69xB3f76E6Ehh8tLN3NyrKa3qxIc6LZ+9NmstOw8cYdSgVuV+CWRVCkOGwM6d9of/xo1/Xf7446/rS5bAt9/CwYN/37dCBahf/6+CRseOdsh/mzZlV9TYsgUmT4ZvvrGrUBybG+Hxx+1oiwTvK26VqEoVuPpquzLJmDG6bGppHDxoixNpaXYURa9eMHIknHsu1NPT2pRSZUeLF0qFoYFtbPFiWsZWRpzeCIAxaavYeeAw71zViUgdRg6AiPDAOa0Z9MJsnpu+mkfPbRPqlFSIFRQa7vx0MQePFDDhhm5c9c48np22ijdHdAp1aj7ZuvcQb/68liHt6pa4XLJSgP2R3qqVvRTHGFvkKFrYOHb9f/+zS40C1KwJZ55pJ1Xs3RtOOcWO5AgEY+ypBt98Yy/HTudo0gRuuQWGDYPu3SEqgF3TW2+1S6q+9ho8/HDg4p5MDh+2E21Om2YnTr36aqhRI9RZKaVOUlq8UCoMNa1VhWa1q5CWkcOI0xuxeONuPpy7gRHdGukPmiJaJMZxRdeGvD9nPZd1TeaUpBPnCLsKvPEzs/h1zQ6evuBUOjasxk1nNuWZtFUs2LCLjg2rhTq9Ej3/w+8UFBru6a/Dr1WAiNgfmzVqQPv2xW+zYQPMmPHXZeJEe39Skj3KfqyY0bSps2JGQQH8+utfBYusLHt/aio89pgtWKSkBK5AUlSzZjB4sC1e3HefThbp1JEjdv6RqVPhzTftRJxKKRVCQR0bKCIDRWSViGSJyH+KebyiiHzqenyuiDRy3X+ZiCx2uxSKSHvXYzNdMY89dnIut6DKvQEpicxdt5Pt+w9z35fLqB1Xkbv1B02x/tm3OfGx0Tw8KROjM8uftNLX7+S5H35naLu6XJhaH7ATu9asUoFn0laGfdvI2rafz9I3clmXhjSsUTnU6aiTScOGdmWP996zhYysLLvCR+/eMHMm3HCDnSwzORmuvBLefdduV5y8PPj6a3uEPikJeva08040bQqvvALZ2TB/Ptx/vz1NJdinRt1+O2zdCp9/HtznKW+OHoXhw+2pPePHa+FCKRUWgjbyQkQigXFAPyAbmC8ik4wxmW6bXQvsMsY0E5HhwFPAxcaYj4CPXHHaAt8YYxa77XeZMSY9WLkrFQ4GpCQxbsYabvxgAZlb9vLKZacRFxMd6rTCUtVKFbi7Xwse+CaDtIytDGyTFOqUVBnbk3eUOyYspl7VWB4/r82fc0VUqhDFyN7NGD05k1+ydnBGGE+A+fTUlcRGR3LbWc1K3lipYBGxhYamTeH66+3pHqtW2dNLZsywE2oeO82kcWNb4DjrLHt6wddfw/TpcOiQnbPi7LPt6IqBA+0knKHQty+0bAkvvWSXW1Uly8+3E55+9ZU97eamm0KdkVJKAcEdedEZyDLGrDXGHAEmAMOKbDMMeM91/Qugjxw/O9klwCdBzFOpsNS2XgJ1E2JYsGEXvVvWYpD+IPfqks7JtEyM4/HvMzl0tCDU6agyZIzh3xOXsnXvIV66pMNxRb5LuiRTr2psWI++WLBhJ9Myt3JjzybUqKJD21UYEbFzX9xyix29sHUrLF0Kzz8Pp55qV5+4/HJ7ZH7JElvw+OEHu2zmRx/BRReFrnABdgLS226DefN02VRfFBTYUTOffmonOr399lBnpJRSfwpm8aIesNHtdrbrvmK3McbkA3uAorMAXczxxYt3XKeMPFBMsQMAEblBRNJFJD03N9ff16BUyIgI57SrS2x0JI8Ma6OrDpQgKjKCB4e0ZuPOg7z187oyec5fsrbzzi/r+G3tDvYcPFomz6mO9+HcP5iakcO9A1vSrkHV4x6vGBXJHX2bsyR7D9Myt4YgQ++MMfzf9yupFVeRa3vokr8qzEVEQNu2dhnTr7+G7dth4UJbuFi/3p4i0qcPRIfRSMErr4S4ODv6QnlWWGiLTx9+CE88AXffHeqMlFLqb4I5YWdxv7SKHvLyuo2IdAHyjDHL3R6/zBizSUTigInAFcD7xwUx5nXgdYDU1NTwPNSmVAnu6teCa7o3JikhJtSpnBC6N6vJgJRExs3I4h8d65MYH7z37ZN5f3DfV8twP5Bfv1osrevE07pu/J//1qsaq4WnIFqxZS+PfpvJmS1qcd0ZTTxud36Herw6aw3PTltF31aJYbViz/TMraRv2MXj57WhUgWdR1udYCIjoUOHUGfhXVwcXHONnXNjzBg7F4f6O2Pg5pvhnXdg9GgYNSrUGSml1HGC2UvKBhq43a4PbPawTbaIRAEJwE63x4dTZNSFMWaT6999IvIx9vSU44oXSpUHMdGRJCVEhjqNE8r9g1vTd+wsnpq6krEXeZhZv5Te/WUdoydn0qtlLR47tw1rcg+QuXkvmVv2krl5D9NXbP2zqBEfE0WrIgWN5rXjqBAV1PmSTwp5R/K57ZNFJMRG8+xF7YjwUpCIiozg7n4tufXjhUxasonzOtQvw0w9yy8o5KmpK2lSqzIXpzYoeQellH/cl0196KFQZxNejLGnh7z+ul2V5cEHQ52RUkoVK5jFi/lAcxFpDGzCFiIuLbLNJGAEMAf4B/A/4zohWUQigAuBnsc2dhU4qhpjtotINHAO8EMQX4NS6gSTXKMS1/VozCsz13BF14Z0SA7s8pivzlrDk1NW0r91Ii9d2oGKUZHUr1aJM1vU+nObvCP5rMrZ5ypm2KLGhHkbOeiaiyM6UmhWO+7PYkbXJtVJqatL4Dr18KRM1uTu54NrulDTh3kiBrVJIqVuPGOnr+bstnXDooD0xYJs1uQe4NXLOxIVGfp8lCq3mje3y6a++qodVVChQqgzCg/G2NNDXn4Z7rnHLmGrowWVUmEqaMULY0y+iIwE0oBI4G1jTIaIPAKkG2MmAW8BH4hIFnbExXC3ED2BbGPMWrf7KgJprsJFJLZw8UawXoNS6sR0S+9mfLEgm4cnZ/Llzad7PSLvK2MML/6YxXM/rGZIu7qMvagd0R5+bFaqEEWH5Gp/K5wUFBrW73AfobGXn37PZeLCbCIE3hyRylmnJJY6z5PFpCWb+TR9I7f0aurzCiIREcI9A1py9Tvz+TR9I1d0bRjkLL07eKSA535YzWnJVRmQov/3SgXdbbfBoEHwxRdwadHjaSchY2wh57nn7MiLp5/WwoVSKqxJuM68HkipqakmPV1XVlXqZDJxQTZ3f76EsRe14/zTSneKgDGGp9NWMX7mGi44rT5P/+PUgM2ZsHXvIa59bz7rcg/wxc2n06pOCGflP0H8sSOPwS/OpkViFT69sZvHIlJxjDFc9NocNuzIY9a/ehNbIXSnZY2bkcUzaav4/KZudGpUPSQ5iMgCY0xqSJ78JKT9kRArLLQrp9SoAXPmhDqb0HvoIXjkEbsU6iuvaOFCKRUyvvZHdIyqUqpcOq9DPdo1qMqTU1Zy4HC+33GMMTzybSbjZ67h0i7JPBPAwgVAYnwMb17ZiSoxUVz77ny27T0UsNjl0ZH8Qm6bsAgReGF4B0eFC7Cr+PxrwCls23eY9+esD0qOvth54AivzlxD31aJIStcKHXSObZs6m+/wfz5oc4mtB57zBYurrkGxo3TwoVS6oSgxQulVLkUESE8NKQ12/Yd5pWZWX7FKCw03P/1ct75ZT1Xd2/E4+e2CcgpKEUlJcTw1ohO7Mo7yvXvp3PwSEHAn6O8eHbaKpZs3M1TF5xKg+qV/IrRuXF1erWsxfhZa9h7KDRL3L78vywOHMnn3wNbhuT5lTppjRgBVaqc3MumPv00PPAAXHGFnaQzQn8OKKVODPpppZQqt05Lrsb5Herxxux1/LEjz9G+BYWGeycu5eO5f3BLr6Y8eE7roC552qZeAi8Mb8/STXu4+/PFFBaW/1P6nJq1OpfXflrLpV2SGdy2Tqli3dO/JbvzjvLm7HUBys53G3fm8cFv67mwYwOaJ8aV+fOrv4jIQBFZJSJZIvKfYh6vKCKfuh6fKyKNXPc3EpGDIrLYdXm1rHNXfoqPh6uvhgkTYOvWUGdT9p5/Hv79bxg+3C6LGqkrmimlThxavFBKlWv3DjyFqAjhie9X+LzP0YJC/vnpYr5YkM1d/VrwrwEtg1q4OKZ/ShKjBp3C98tyeHb6qqA/34lk275D3P3ZYlomxvHgOa1LHa9NvQTObluHt2avZcf+wwHI0Hdjpq0iMkK4s1+LMn1e9XciEgmMAwYBrYFLRKRo47oW2GWMaQY8Bzzl9tgaY0x71+WmMklaBcbIkXD0qB11cDIZNw7uvBMuuADef18LF0qpE44WL5RS5VpSQgy39m7G1Iwcfs3aXuL2h/MLGPnxQiYv2cyoQadwe5/mZVK4OOb6Hk0Y3qkB42as4YsF2WX2vOGssNBw16dL2H84n5cu7UBMdGA63Hf2a8HBowWMn7kmIPF8sXzTHr5ZvJlrujcmKSGmzJ5XFaszkGWMWWuMOQJMAIYV2WYY8J7r+hdAHynLDwQVHC1awMCBMH48HDkSnOc4eBBWr7YreoSDN96wRZuhQ+HjjyE6OtQZKaWUY1q8UEqVe9ee0Zj61WJ5eHIm+QWFHrc7dLSAmz5YQFrGVkYPac2NZzYtwywtEeHRc9twetMajPpyKXPX7ijzHMLNqz+t4ees7Tw0JIUWATzNolntKlxwWn3e/20DW/YcDFhcb56aupKqlaK5qVfZty11nHrARrfb2a77it3GGJMP7AFquB5rLCKLRGSWiPTw9CQicoOIpItIem5ubuCyV6Vz222wZQt8+WXgY69bBx07QsuW0KQJ3HOPnSS00PP3T9Dk5dkRFzfeaJeJ/ewzqFCh7PNQSqkA0OKFUqrci4mO5L9nt2LV1n18Mu+PYrfJO5LPte/NZ+bqXP7v/LZc1b1xGWf5l+jICMZf1pEG1Stx44cLWL/9QMhycWp33hGytu2nIEBzdizYsItnp63m7LZ1GN6pQUBiurujb3OMMbz44+8Bj13U7N9zmf37dkb2bkZ8jB71DAPFjaAo2nA9bbMFSDbGdADuAj4WkWLXOTbGvG6MSTXGpNaqVatUCasAGjgQmjWDF18MbNy5c6FLF8jJgSefhFat7HN06wbJyXD77TBrFhQEaWLmwkJYtMhOytm3L1Svbkdc9O1rCzUVKwbneZVSqgxo8UIpdVIYkJJEtyY1eHb6anbn/X2Y8L5DRxnx9jzmrNnBsxe245LOySHK8i8JlaJ5e0QnAK55d/5xOYeTnQeOMGHeH1z59jxSH/uBvmNn0eahNC4Y/yujJ2UwcUE2q7fuc1zQ2HPwKLd/sog6CTE8cX7boJy+U79aJS7r0pDP0rNZF8QiUWGh4ckpK6lfLZYrujUM2vMoR7IB94pYfWCzp21EJApIAHYaYw4bY3YAGGMWAGsAncTkRBIRYX/Uz5kD6emBiTlxIvTqBXFxNu6//w3ffw/btsEHH0CnTvb0jV69oG5duOkmmD7dzr9RGtnZdvLNSy+FpCQ47TT73Fu3wq23wpQp8N13EKOnqimlTmxiwuVcvCBKTU016YH6YlJKnbBWbNnL2S/O5spujRg9NAWAPXlHufKdeWRs2sPzw9tzzql1Q5zl381du4PL35pLasPqvHdNZypEhUfNefv+w6Rl5PD9si38tnYnBYWG5OqVGNy2Dk1qVSZz816Wb9pDxua9HDxqjzDGRkfSum48besl2Ev9BJrWqkJkMcvPGmMY+fEipmbk8PlN3TgtuVrQXkvuvsP0fHoG/Von8uIlHYLyHN8s3sQdExbz/MXtObdD0TMTQkdEFhhjUkOdRyi4ihGrgT7AJmA+cKkxJsNtm1uBtsaYm0RkOHC+MeYiEamFLWIUiEgTYLZru53enlP7I2Fmzx6oXx/OPx/ee6/k7T0xBsaMsQWDrl3hm2/A0yib/fttQWPiRFtQOHDAjo4YOhT+8Q87QqKk0RH799vRG9Om2eLHCteE1ImJ0K8f9O9v49Qp3apMSilVVnztj2jxQil1Urn/q2VMmL+RKXf0oGaVilz+5lyytu3n5Us70D8lKdTpFWvigmzu/nwJF6c24MkLgjMCwRfb9h5iqqtgMW/dTgoNNK5ZmcFtkxjUpg4pdeOPy62g0LA2dz9Ls/ewbNMenwsan87fyH1fLePfA0/h5jKYH+KZtJWMm7GG72/vQeu6xY7+99vh/AL6PDuL+Jhovr3tDCKKKdaEyslcvAAQkcHA80Ak8LYx5nEReQRIN8ZMEpEY4AOgA7ATGG6MWSsiFwCPAPlAAfCQMWZySc+n/ZEwNHKkHQ2xcSPUru18//x8G+O11+Cii+DddyE21rd9Dx6EtDRbyJg82RZT4uPhnHPsiiADB0KlSvYUk4UL/ypW/PqrHa0REwM9e9piRb9+0LYt6HyySqkTkBYv3GhnQSl1zI79h+k9ZianJMWz++ARNuzI47UrOtKrpR+d1jI0Jm0VL8/IYtSgU8p0ItEtew4ydXkOU5blMH/DToyBprUqc3bbOgw+tQ4tE+McF1MKCg1rcvezzEtBI7+wkK5NavDe1Z3L5Mf+nryj9Hj6f3RuXJ03XafrBMLKnL08+m0mv2Tt4P1rOtOzRXjNeXCyFy/KmvZHwtDKlXZeikcfhf/+19m+e/fagkVaGowaBY89Zk9H8ceRI/Djj/DFF3bkxo4dtnDRpQssXgy7dtntOnSwhYp+/eCMM/RUEKVUuaDFCzfaWVBKuXv753U88m0msdGRvDUildOb1Qx1SiUqLDTc9skiVDCqzAAAFkJJREFUvl++hVcv78iAII4S2bT7IFOWbWHK8hwWbLAd5paJcQxuW4fBbZNoHsAVP44pWtDYtu8Qo4emUDuu7Drm42Zk8UzaKibefDodG5buNJVt+w7x3PTVfDp/I3Ex0dzTvwVXdGsUmEQDSIsXZUv7I2FqwABYvhzWr/d9CdGNG+HssyEzE159Fa67LnD55Ofb00ImToRffrFzWPTvD336+Dc6RCmlwpwWL9xoZ0Ep5e5oQSHPTltN/5TEoM6lEGiHjhZw8eu/sTpnH5/f1I029RICFnvjzjymLN/Cd8tyWLJxNwCt68QzuG0SA9vUoVntKgF7rnCVdySfnk/PoFntKnxyfVe/Ts85dLSAN2evZfzMNRzOL2TE6Y247axmVK0UnksTavGibGl/JEx9+y0MGQITJsDFF5e8/YIFdvsDB+xIiX79gp+jUkqVY1q8cKOdBaVUebFt3yHOG/cr+YWFfHPrGSQl+D8yYW3ufqYsz2HK8i0s37QXgDb14hnctg6D2tShcc3KgUr7hPHuL+sYPTmTD6/twhnNfR+RU1ho+GbJJp6ZuorNew4xICWR/wxqFfbvoRYvypb2R8JUYSG0aGFX6vj5Z+/bTp4Mw4dDzZp24s2UlLLJUSmlyjFf+yNRZZGMUkqpwKgdF8ObI1L5x/hfufa9+Xx2YzcqV/Tto9wYw+/b9vP9si1MWZbDqq37AOiQXJX7B7diYJskGlSvFMz0w94lXZJ5Y/Y6nklbSfdm3X0afTFv3U4e+y6Tpdl7aFMvnrEXt6drkxplkK1SKiAiIuySonfdZSfGPO204rd78UW48077+OTJttihlFKqzAR1zT0RGSgiq0QkS0T+U8zjFUXkU9fjc0Wkkev+RiJyUEQWuy6vuu3TUUSWufZ5UUI17b5SSoVIqzrxvHzpaazYspd/frqYgkLPI+iMMSzftIcxaavoM3YW/Z/7iRd+/J2E2GgeGtKaOaPO4qtbunN9zyYnfeECoGJUJHf0bc6S7D1My9zqddsNOw5w84cLuOi1OWzbe5ixF7Vj0q1naOFCqRPR1VdD5crw0kvHP1ZQALffDnfcYZc0nTlTCxdKKRUCQRt5ISKRwDigH5ANzBeRScaYTLfNrgV2GWOaudZPfwo4drLhGmNM+2JCjwduAH4DvgcGAlOC9DKUUios9T6lNg+c05qHJ2fy1NSV3De41Z+PGWNYkr3nz0k3/9iZR4RAt6Y1uLp7YwakJJbpRJgnmvM71OO1WWsYk7aKvq0SiSyy2smevKO8PON33v11PVEREdzVrwXX92hCbIXIEGWslCq1qlXhyivh7bfh6aehlmtloP374dJL7UiLu+6yj0Xq37pSSoVCME8b6QxkGWPWAojIBGAY4F68GAaMdl3/AnjZ20gKEakDxBtj5rhuvw+cixYvlFInoatOb8Ta3AO8/tNaGtaoRMvEOL5flkNaRg6bdh8kKkLo3qwmt/RqSr/WidSoUjHUKZ8QoiIjuLt/S275aCHfLN7E+afVB+xErx/9toHnf/ydPQePcmHH+tzdvyWJ8VoIUqpcGDkSxo+HN96A++6DzZvhnHNgyRIYNw5uuSXUGSql1EktmMWLesBGt9vZQBdP2xhj8kVkD3BsvG1jEVkE7AX+a4yZ7do+u0jMesU9uYjcgB2hQXJyculeiVJKhSER4aEhrdmwM4/7v1oOQIWoCHo2r8ld/VrQt1UiCZV8XPZP/c3AlCTa1IvnuR9Wc86pdflpdS5PfL+CtdsPcHrTGtx/ditS6gZutRelVBho3Rr69rUFjIEDYdgw2L3bjroYPDjU2Sml1EkvmMWL4kZQFD0x29M2W4BkY8wOEekIfC0iKT7GtHca8zrwOtjZvX3OWimlTiBRkRG8fGkHxs3IonWdeM46pTZxMVqwKK2ICOGe/i256p359H9uFut35NGkVmXeGpHKWafU9msZVaXUCeD22+28Fp0723ktZs+G9sWdxayUUqqsBbN4kQ00cLtdH9jsYZtsEYkCEoCdxq7fehjAGLNARNYALVzb1y8hplJKnVTiY6IZNahVyRsqR85sUYvuzWqQuXkvDw9N4dIuyURHBnWea6VUqA0ebJc/jY62Iy7q1y95H6WUUmUimMWL+UBzEWkMbAKGA5cW2WYSMAKYA/wD+J8xxohILWwRo0BEmgDNgbXGmJ0isk9EugJzgSuBYqaFVkoppUpHRHj7qk6AXYVEKXUSiIyE9HSoUMEuoaqUUipsBK144ZrDYiSQBkQCbxtjMkTkESDdGDMJeAv4QESygJ3YAgdAT+AREckHCoCbjDE7XY/dDLwLxGIn6tTJOpVSSgWFFi2UOgnF6CS8SikVjoI58gJjzPfY5Uzd73vQ7foh4MJi9psITPQQMx1oE9hMlVJKKaWUUkopFa50PJxSSimllFJKKaXCmhYvlFJKKaWUUkopFda0eKGUUkoppZRSSqmwpsULpZRSSimllFJKhTUxxoQ6h6ATkVxgQ4DD1gS2a4ywzEVfT3jnEi4xwikXfT3BiRFOuYTT63HX0BhTK4DxlBdB6I+EU7sKlxjhlEu4xAinXPT1hHcu4RIjnHLR1xOcGEX51B85KYoXwSAi6caYVI0Rfrno6wnvXMIlRjjloq8nODHCKZdwej2q/AindhUuMcIpl3CJEU656OsJ71zCJUY45aKvJzgx/KWnjSillFJKKaWUUiqsafFCKaWUUkoppZRSYU2LF/57XWMEJU64xAhUnHCJEag45SlGoOKES4xAxSlPMQIVJ1xiBDKOKh/CqV2FS4xAxSlPMQIVJ1xiBCpOuMQIVJzyFCNQccIlRqDilKcYftE5L5RSSimllFJKKRXWdOSFUkoppZRSSimlwpoWLxwSkbdFZJuILPdz/wYiMkNEVohIhojc4WecGBGZJyJLXHEe9ieOK1akiCwSkW/93H+9iCwTkcUikl6KPKqKyBcistL1/nRzuH9LVw7HLntF5J9+5HGn6z1dLiKfiEiMHzHucO2f4SSH4tqXiFQXkeki8rvr32p+xLjQlUuhiPg0O7CHOM+4/n+WishXIlLVjxiPuvZfLCLTRKSu0xhuj90jIkZEavqRx2gR2eTWXgZ7i+EtFxG5TURWud7jp/3I5VO3PNaLyGI/YrQXkd+O/R2KSGd/Xo+ItBOROa6/6ckiEl9CjGI/05y0Wy8xfG63XmL43Ga9xHDaZr1+zvvSbr3k4rjdqvLHU7sWkUYictCtfbzqNIbb48kisl9E7vEzl85ueSwRkfP8iNFPRBa4Po8WiMhZfsSo4fpb2i8iL3t7LSW9LyIySkSyxH7eD/ASo9jPLhGpICLvuF7PEhHp5UeMaBF5zxVjhYiM8iPGZfL3/lKhiLR3Gsf12KlivzMyXDkV22fykouTNuv1O8FBm/WUi5M26ymGkzbrKYbPbbaE/xuf2msxMR31AzzEcNwnKSaGo75RCbF87qd52D9g373iY9/Vy/6O+iQeYjjqz3uI4fi3RUAYY/Ti4AL0BE4Dlvu5fx3gNNf1OGA10NqPOAJUcV2PBuYCXf3M6S7gY+BbP/dfD9QMwHv7HnCd63oFoGopYkUCOdg1g53sVw9YB8S6bn8GXOUwRhtgOVAJiAJ+AJr7276Ap4H/uK7/B3jKjxitgJbATCC1FLn0B6Jc15/yM5d4t+u3A686jeG6vwGQBmwoqf15yGM0cI/D/9vi4vR2/R9XdN2u7c/rcXv8WeBBP/KYBgxyXR8MzPTz9cwHznRdvwZ4tIQYxX6mOWm3XmL43G69xPC5zXqJ4bTNevyc97XdesnFcbvVS/m7eGrXQCNPny2+xnB7fCLweUntzUsuldzurwNsO3bbQYwOQF3X9TbAJj/yqAycAdwEvFyK97Y1sASoCDQG1gCRHmIU+9kF3Aq847peG1gARDiMcSkwwe09Xg80chKjyDZtgbUlvCeecokClgLtXLdr+PGeOGmzXl+PgzbrKRcnbdZTDCdt1lMMn9uslxg+t9diYjrqB3iI4bhPUkK8EvtGXvZ11E/zEGN0Se3Kxzg+9129xHDUJ/EQw1F/3knbC/ZFR144ZIz5CdhZiv23GGMWuq7vA1ZgfzA7jWOMMftdN6NdF8cTmIhIfeBs4E2n+waSq6rbE3gLwBhzxBizuxQh+wBrjDEb/Ng3CogVkSjsF9lmh/u3An4zxuQZY/KBWYDH6r07D+1rGLawg+vfc53GMMasMMas8iWHEuJMc70mgN+A+n7E2Ot2szIltFsvf3PPAfeWtH8JMRzxEOdm4EljzGHXNtv8zUVEBLgI+MSPGAY4dnQkAR/arYc4LYGfXNenAxeUEMPTZ5rP7dZTDCft1ksMn9uslxhO26y3z3mf2m2gvitU+eT0s9hpDBE5F1gLZPgbx+07ECAGL23eS4xFxphjn2UZQIyIVHQY44Ax5mfgUEmvxVsc7GfaBGPMYWPMOiALKPZospfPrtbAj65ttgG7gWKPWHqJYYDKrj5KLHAE2FvMdr5+919Cyd85nuL0B5YaY5a4ttthjCkoRS5eeYvhsM0WG8dhm/UUw0mb9RTD5zbr5T3xub0Ww1E/wFNqOOyTeOJr38gLR/20IPO57+qJ0z6JhxiB+A4p9d+0P7R4EUIi0ghboZ3r5/6RriFU24Dpxhh/4jyP/SMq9CcHFwNMcw2Pu8HPGE2AXOAdsaewvCkilUuR03D8+JAzxmwCxgB/AFuAPcaYaQ7DLAd6uob9VcJWnBs4zcVNojFmiyu/LdijNeHgGmCKPzuKyOMishG4DHjQj/2HYo9mLPHn+d2MdA2Ze1tKOB3HixZADxGZKyKzRKRTKfLpAWw1xvzux77/BJ5xva9jAI9DiUuwHBjqun4hDtpukc80v9ptaT8XS4jhc5stGsPfNusex992W8zrCUS7VeVH0Xbd2PU9OktEejiN4fru/Tfgz+mof8tFRLqISAawDLjJrbPscww3FwCLjv0A8TOGU+5x6gEb3R7LxnlBcQkwTESiRKQx0BHn/YMvgAPYPsofwBhjTGmK8xfj/4/CFoARkTQRWSgi9/oZx582+6dSttmisfxps544abOBVpr26nc/wE2g+iRQur4RBK6fVqrv3gD2XUvdjy4iUJ+XZSIq1AmcrESkCnZ42z+LVNB85qput3edp/SViLQxxvg8F4eInANsM8YsEC/nXfqguzFms4jUBqaLyErXEV0norDD128zxswVkRewQ80fcJqMiFTAfug6/qB0fRgNww6x2w18LiKXG2M+9DWGMWaFiDyFrVbvx3ZWSvPlF3ZE5H7sa/rIn/2NMfcD94s9V3ck8JCD564E3I894lMa44FHscW3R7FDEq/xI04UUA3oCnQCPhORJsYYf6rqJR4B8+Jm4E5jzEQRuQg7iqmvH3GuAV4UkQeBSdijeiUq+plmD5Q4E4jPRU8xnLTZ4mL402bd47ie23G7LeZ9DVS7VWFORH4Akop56H5jzDeubYq26y1AsjFmh4h0BH4RkbUcf4DCW4yHgeeMMfuP/R37mQuugyopItIK+E1EHuP4o4ReY7juT8EObc6R4uccKzFGkXj+vJ6iH2oDgX4iMtpTjGK8jR2dmY4dNp4PjJXj5y3zFqMzUADUxX73rBORu4GjDmIA9oc6kGeMWe7Le1KMKOzpDZ2APGCziNyMLa74GsNxmy2GX222OE7brCdO2mxJ/HwtxX0J//k6vMXEx35ACTH64EOfxMfXVmLfqIRcfOqnlRDDp+/eEmLch499gJLeF1/6JP5+bjuNUeZMGZ2fUp4uODg/z8P+0djzne4KYE4P4fz8/f/DVmLXY+eHyAM+LGUeo53m4dovCVjvdrsH8J2fOQwDpvm574XAW263rwReKeV78gRwi7/tC1gF1HFdrwOschrD7f6ZODgvrbg4wAhgDlDJ3xhujzX05W/JPQb2/Nxtrna7Hvuh+weQVIo8fP6bLub/ZyrQy+32GqCWH+9rFLAVqO9nHnvgz+WvBdgbgP+fFsA8H2Ic95nmtN0WF8Npu/UUw0mb9ZaHwzb7tzj+tFsfcvG53eql/F18adcl/e0UFwOY7dZOd2NPLRsZgFxmOM3FdX997Jwv3UvzngBX4cOcF17el1HAKLfbaUC3EuKU9P7/SgnznhWNAYwDrnC7/TZwkT95YIew3+egzRXNZTjwrtvtB4B/lfI98fq4hzwct1kfc/HaZj3FcNJmS/j/cdJmi74njturh7g+9QOK2c+vPkkxcRz1jTzEcNxPKyFeIxx+9+Jn39WHuD71STzs66g/72vbC/ZFTxspY2JLwm8BK4wxY0sRp5b8NZN2LLaaudJJDGPMKGNMfWNMI+wX0P+MMZc7zKOyiMQdu46tKDpeicUYkwNsFJGWrrv6AJlO47iU5uj1H0BXEank+r/qgz3X3BHXKBREJBk4vxT5gK16j3BdHwGEptIJiMhA7NDMocaYPD9jNHe7ORTn7XaZMaa2MaaRq+1mYyc2zHGYRx23m+fhR7t1+Ro4yxWzBXay2e1+xOkLrDTGZPuZx2bgTNf1swC/hle6td0I4L+Ax5nfXdt5+kzzud0G4nPRUwwnbdZLDEdttrg4Ttutl1wC1W7VCcxTu3b1DSJd15sAzbHzAPgcwxjTw62dPg88YYzxuOKBl1wai52XARFpiD2Pfr3DGFWB77A/wn7x/I4E5vuphDiTgOEiUlHsKR/NgXkOY1dy9ZUQkX5AvjHGaV/nD+AssSpjjyY7+h51PX8E9oDNBKf7ukkDTnW9rijsd5Cj1+OkzXritM16ycXnNuslhs9tNsj8bq9O+wEeBKRPQun7RhCAflppv3sD1Xd15VKqfrQrRkA+L0OirKok5eWC/RG6BTs8Lxu41uH+Z2CHHC0FFrsug/3I41RgkSvOcvycgdctXi/8WG0EO1fFEtclAzuMyN8c2mOHUi7FftBU8yNGJWAHkFCKPB7GfhAsBz7ANTuxwxizsV/gS4A+pWlf2Nm7f8R+8P8IVPcjxnmu64exFew0P3PJwp5DeaztlrTqQnExJrre26XAZOyEiH7/zeHDajce8vgAe07rUuyXfB0/35MKwIeu17QQOMuf1wO8iz2/1t92cgZ25vol2LkROvoZ5w7sEaPVwJO4jpx4iVHsZ5qTduslhs/t1ksMn9uslxhO22yJn/MltVsvuThut3opfxdP7Rp7jn2G63NgITDEaYwi24ym5JUbPOVyhSuXxa5czvUjxn+xpyAsdrsUu1KAt9fj+nvbiT2VMxsvox1KiHM/9qjtKlwrKXiIUexnF/aI7SrsQZEf8LIimpcYVbAramRg+xkeRzp4iuF6rBd2cnFf2pu3OJe7clkOPO3H63HSZkv8TvCxzXrKxUmb9RTDSZv19r761GZLiOFTey0mpqN+gIcYjvskHuK8i499Iy8xHPXTPMQI6HcvpVipEYd9Eg8xHPXnnba9YF6ODedRSimllFJKKaWUCkt62ohSSimllFJKKaXCmhYvlFJKKaWUUkopFda0eKGUUkoppZRSSqmwpsULpZRSSimllFJKhTUtXiillFJKKaWUUiqsafFCKaWUUkoppZRSYU2LF0oppZRSSimllAprWrxQSimllFJKKaVUWPt/cbkRk+ade6EAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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JKaXOhWmzurwgIr4B/BH4A9AP2AA4F9iv8HyXiOiwpm2klH5Z9b7ACcB/inJsuTYfSJIkrTMuAAamlLoC+wM/j4jta3tRROwCPAn8G9g8pdQdGAksB7YurFPr8Ug1hwOzCj8llYnFCEkrRcQREfHviLgyIuZGxBsRsWfhuQAuBc5PKd2YUpqbUlqRUnoqpXRsYRNbAdMi4rqI2DmvzyFJkpq2lNK4lNKSqtnCNKTQSmFqoSXlhxHxTkQcUvTSi4BbUkoXpJQ+KGzr3ZTST1JKTxbWqfPxSER0BL4BnAgMjYgRjfgxJa2BxQip5bqg8J/8vyNij6LlOwGTgJ7AT4D7ImI9YDOgP3Dv6jaYUvoPsB0wDbg9Il6PiDMiok+pPoQkSVo3RcRvI2Ih8AbwPvBw4akNyY5D+pK1Vrg+IjaLiE7AZ4E/rWm79Twe+Town6zl56PAt9f+k0mqC4sRUst0JjCY7D/564EHI2JI4bkZwGUppWUppbuB8cCXgfULz7+/pg2nlN5OKf0M2AQ4HtgceK3Qt3NA438USZK0LkopfRfoAnweuA9YUvT0/6WUlqSUngL+CnwT6EF2/jK9aqWIuKgwbsSCiDinaNt1PR45HLg7pVQJ3AGMiog2pfi8kj7JYoTUAqWUnkspzSv8J/97sn6XXyo8/V5KKRWtPhnYCPioMF+nVg6FbbwOjAWmAlsCnRojvyRJah5SSpUppWfIxqL6TmHx7JTSgqLVqo5FZgMrKDoWSSmdURg34s9A6xq2v9rjkYjoD/wPcHth9b8A7ckuwkgqMYsRkiDrpxmFx30L40NUGUDWzHE8MIWsOeNqRUS7iPhGRDwAvAVsD5wMDE4pvd7oySVJUnPQGqhqpdmj0CWjygBgWqFA8Rzwtdo2VsfjkcPIzocejIjpZN1U22NXDaksLEZILUxEdC/cnrN9RLQuDAq1G1k/SYDewMkR0SYiDgS2AB4uXFk4Dfi/iDgyIrpGREVE7BoR1xe2PZysG8cpZFcX+qeUvp1S+me11haSJKmFiojeEXFQRHSOiFYRsQ8wCvhH0Wo/i4i2EfF54CtkYzoAnAEcFRFnRUTvwvb6AYOKtl/X45FvAz8Dtimavg58OSLWR1JJfaopk6Rmrw3wc7K+k5Vkg0Z9NaU0PiI+S3bFYSjwIfAB8I2U0kcAKaV7I2I+8GPgSmARMA64uLDtGcCOKaUJZfw8kiRp3ZLIumRcS3ZxdDJwakrpL4VBtaeTdcmYBiwETkgpvQGQUnomIr5ANsj2WYXGnFPJig5XFrZf6/FI4S4bA4GrU0ozi556ICImkBVHrmqUTyupRuHFSklVIuII4JiU0q55Z5EkSS1PoRhxW0qpX95ZJJWW3TQkSZIkSVJZWYyQJEmSJEllZTcNSZIkSZJUViVtGRERIyNifERMiIizanj+iIiYGREvF6ZjSplHkiRJkiTlr2R304iIVsDVwN5kI9yOjogHUkqvVVv17pTSSaXKIUmSJEmSmpZS3tpzR2BCSmkSQETcBRwAVC9G1EvPnj3TwIED1z6dJEnNzAsvvPBhSqlX3jlaAo9HJEn6tPoci5SyGNEXmFI0PxXYqYb1vh4RuwFvAt9PKU2pYZ2VBg4cyJgxYxovpSRJzURETM47Q0vh8YgkSZ9Wn2ORUo4ZETUsqz5a5oPAwJTScOBx4Pc1bijiuIgYExFjZs6c2cgxJUmSJElSOZWyGDEV6F803w+YVrxCSumjlNKSwuwNwPY1bSildH1KaURKaUSvXrY+lSRJkiRpXVbKYsRoYGhEDIqItsBBwAPFK0REn6LZ/YHXS5hHkiRJkiQ1ASUbMyKltDwiTgIeBVoBN6eUxkXEecCYlNIDwMkRsT+wHJgFHFGqPJIkSZIkqWko5QCWpJQeBh6utuzcosc/An5UygxrsnhZJb9+bDzb9O/Bl4f3qf0FkiRJkiSt61KCww+HQw6BffbJJUIpu2k0eW1bVfDUmzO58h9vkVL1sTUlSZIkSWqGHn8cbr0Vpk2rfd0SadHFiIqK4PjdhvDG9Hk8+aZ36ZAkqTmKiJERMT4iJkTEWTU8f0REzIyIlwvTMXnklCSpbC6+GPr0gYMPzi1Ciy5GAOy39Ub06daea5+cmHcUSZLUyCKiFXA1sC8wDBgVEcNqWPXulNI2henGsoaUJKmcxo6Fv/8dTj4Z2rXLLUaLL0a0bV3B0bsO4rm3Z/HSu7PzjiNJkhrXjsCElNKklNJS4C7ggJwzSZKUn0sugc6d4YQTco3R4osRAAftOICu7Vtz3VOT8o4iSZIaV19gStH81MKy6r4eEa9ExL0R0b+mDUXEcRExJiLGzJxp905J0jpoyhS46y445hjo3j3XKBYjgM7tWvPtzw7k0demM2nm/LzjSJKkxhM1LKs+avWDwMCU0nDgceD3NW0opXR9SmlESmlEr169GjmmJEllcPnl2Z00Tj017yQWI6ocvstA2rSq4IZ/2TpCkqRmZCpQ3NKhH/CJocNTSh+llJYUZm8Ati9TNkmSymfuXLj+evjWt2DjjfNOYzGiSq8u7Thw+3786YX3mDFvcd5xJElS4xgNDI2IQRHRFjgIeKB4hYjoUzS7P/B6GfNJklQe118P8+bBD36QdxLAYsQnHPv5wSxfsYJb/v1O3lEkSVIjSCktB04CHiUrMtyTUhoXEedFxP6F1U6OiHERMRY4GTgin7SSJJXI0qVZF40994Rtt807DQCt8w7QlAzs2Yl9t+rDbc9O5rt7DKFL+zZ5R5IkSWsppfQw8HC1ZecWPf4R8KNy55IkqWzuugveew9ubDp3r7ZlRDXH7z6YeYuXc+fz7+YdRZIkSZKktZNSdjvPrbaCffbJO81KFiOqGd6vO7sMWZ+bnnmbJcsr844jSZIkSVLDPfYYvPpqNlZE1HSTqXxYjKjBCbsP4YOPl/CXl6fVvrIkSZIkSU3VxRfDRhvBqFF5J/kEixE1+PzQngzr05XrnprIihXVb0UuSZIkSdI64KWX4Ikn4JRToG3bvNN8gsWIGkQEx+8+mIkzF/DEGzPyjiNJkiRJUv1dcgl06QLHH593kk+xGLEaX/5MH/r16MC1T03MO4okSZIkSfXz7rtw991w7LHQrVveaT7FYsRqtG5VwbGfH8wLk2cz5p1ZeceRJEmSJKnuLrssG7Dy1FPzTlIjixFrcOCIfvTo2MbWEZIkSZKkdcecOXDDDXDQQdC/f95pamQxYg06tm3N4bsM5PHXZ/DWB/PyjiNJkiRJUu2uuw7mz89u59lEWYyoxbc/O5D2bSq47ulJeUeRJEmSJGnNliyByy+HvfeGrbfOO81qWYyoxXqd2nLQDgP4y8vv8f7cRXnHkSRJkiRp9e68E95/v0m3igCLEXVy9K6DWJHg5mfezjuKJEmSJEk1Sym7nefw4VnLiCaspMWIiBgZEeMjYkJEnLWG9b4RESkiRpQyT0P1X68jXxnehzuee5e5C5flHUeSJEmSpE/7299g3LisVURE3mnWqGTFiIhoBVwN7AsMA0ZFxLAa1usCnAw8V6osjeH43YawYGkltz03Oe8okiRJkiR92sUXQ9++2V00mrhStozYEZiQUpqUUloK3AUcUMN65wMXAYtLmGWtDduoK7tt2otb/v0Oi5dV5h1HkiRJkqRVXngB/vlPOPVUaNMm7zS1KmUxoi8wpWh+amHZShGxLdA/pfRQCXM0mhN2H8yH85dw34vv5R1FkiRJkqRVLrkEunaF447LO0mdlLIYUVMHlbTyyYgK4DfA6bVuKOK4iBgTEWNmzpzZiBHr57OD12d4v25c//REKlek2l8gSZIkSVKpvfMO/PGPWSGia9e809RJKYsRU4H+RfP9gGlF812ArYAnI+IdYGfggZoGsUwpXZ9SGpFSGtGrV68SRl6ziOCE3YfwzkcLeWzc9NxySJIkSZK00mWXZQNWnnJK3knqrJTFiNHA0IgYFBFtgYOAB6qeTCnNTSn1TCkNTCkNBJ4F9k8pjSlhprW2z5YbMnD9jlz71ERSsnWEJEmSJClHs2fDjTfCwQdDv355p6mzkhUjUkrLgZOAR4HXgXtSSuMi4ryI2L9U71tqrSqCY3cbzNipc3l20qy840iSJEmSWrJrr4UFC+D0WkdAaFJK2TKClNLDKaVNU0pDUkq/KCw7N6X0QA3r7tHUW0VU+fp2/ejZuS3XPjUx7yiSJKkWETEyIsZHxISIOGsN630jIlJNXUYlSWqSliyBK66AffaB4cPzTlMvJS1GNFft27TiyM8N4qk3Z/LatI/zjiNJklYjIloBVwP7AsOAURExrIb1ugAnA8+VN6EkSWvh9tth+nT4wQ/yTlJvFiMa6NCdNqZT21Zc/7StIyRJasJ2BCaklCallJYCdwEH1LDe+cBFwOJyhpMkqcFWrMhu57nNNrDnnnmnqTeLEQ3UrWMbRu04gAdfeZ8psxbmHUeSJNWsLzClaH5qYdlKEbEt0D+l9NCaNtRUbjUuSRIAjzwCr7+etYqIyDtNvVmMWAtH7TqIAG565u28o0iSpJrVdHS28nZYEVEB/AaoddSvpnKrcUmSALj4YujfH775zbyTNIjFiLWwUfcOHLBNX+4ePYXZC5bmHUeSJH3aVKB/0Xw/YFrRfBdgK+DJiHgH2Bl4wEEsJUlN2ujR8NRTcOqp0KZN3mkaxGLEWjp+98EsWlbJH/4zOe8okiTp00YDQyNiUES0BQ4CVt7VK6U0N6XUM6U0MKU0EHgW2H9ducOXJKmFuuQS6NYNjj027yQNZjFiLW26QRf22qI3N/5rEpM/WpB3HEmSVCSltBw4CXgUeB24J6U0LiLOi4j9800nSVIDvP023HsvHH88dOmSd5oGsxjRCH6y35ZEwEl3vMSS5ZV5x5EkSUVSSg+nlDZNKQ1JKf2isOzclNIDNay7h60iJElN2q9/Da1awckn551krViMaAT91+vIJQduzavvzeWCh9/IO44kSZIkqTmaORNuvhkOOwz69q19/SbMYkQj+eKWG3L0roP43f97h0defT/vOJIkSZKk5uaqq2DRIvjhD/NOstYsRjSiM0duztb9u3PGva/w7kcL844jSZIkSWouFizIihEHHACbb553mrVmMaIRtW1dwVWjtiUCTrzjRcePkCRJkiQ1jhtvhFmz4Mwz807SKCxGNDLHj5AkSZIkNaply+DSS+Hzn4fPfjbvNI3CYkQJOH6EJEmSJKnR3H03vPtus2kVARYjSsbxIyRJkiRJay0luOgi2Gor+NKX8k7TaCxGlIjjR0iSJEmS1tojj8Crr2Z30IjIO02jsRhRQo4fIUmSJElaKxdeCP37w6hReSdpVBYjSszxIyRJkiRJDfLss/D003DaadCmTd5pGpXFiDJw/AhJkiRJUr1deCH06AHHHJN3kkZXp2JERGxV6iDNmeNHSJK09jwekSS1KG+8AX/5C5x0EnTunHeaRlfXlhHXRsTzEfHdiOhe0kTNlONHSJK01jwekSS1HJdcAu3aZcWIZqhOxYiU0q7AIUB/YExE3BERe5c0WTPk+BGSJDWcxyOSpBZj2jS49VY46ijo3TvvNCVR5zEjUkpvAecAZwK7A1dExBsR8bXVvSYiRkbE+IiYEBFn1fD8CRHxakS8HBHPRMSwhnyIdYnjR0iS1HANOR6RJGmdc9llsHw5nH563klKpq5jRgyPiN8ArwNfAPZLKW1RePyb1bymFXA1sC8wDBhVQ7HhjpTSZ1JK2wAXAZc27GOsOxw/QpKkhmnI8YgkSeucOXPg2mvhm9+EwYPzTlMydW0ZcRXwIrB1SunElNKLACmlaWRXJ2qyIzAhpTQppbQUuAs4oHiFlNLHRbOdgFSf8Osqx4+QJKlBGnI8IknSuuXaa2HePDjjjLyTlFTrOq73JWBRSqkSICIqgPYppYUppVtX85q+wJSi+anATtVXiogTgdOAtmRXNlqEqvEjbnrmbXYatB77fqZP3pEkSWrqGnI8IknSumPxYrj8cvjiF2HbbfNOU1J1bRnxONChaL5jYdmaRA3LPtXyIaV0dUppCFnfzxqvakTEcRExJiLGzJw5s46Rmz7Hj5AkqV4acjwiSdK649ZbYfr0Zt8qAupejGifUppfNVN43LEagZMXAAAgAElEQVSW10wlG+26Sj9g2hrWvwv4ak1PpJSuTymNSCmN6NWrVx0jN32OHyFJUr005HhEkqR1Q2UlXHwxbL89fKH5dxqoazFiQURsVzUTEdsDi2p5zWhgaEQMioi2wEHAA8UrRMTQotkvA2/VMU+z4fgRkiTVWUOOR7y7lyRp3XD//fDWW3DmmRA1dTRoXuo6ZsSpwB8joqplQx/gW2t6QUppeUScBDwKtAJuTimNi4jzgDEppQeAkyJiL2AZMBs4vCEfYl1XPH7Edhv3YP+tN8o7kiRJTVG9j0eK7u61N1mrzdER8UBK6bWi1e5IKV1bWH9/srt7jWzs8JIkrVZKcOGFsMkm8LWWcbfqOhUjUkqjI2JzYDOysSDeSCktq8PrHgYerrbs3KLHp9QvbvN15sjNGTtlDmfe+wpDe3dmiz5d844kSVKT0sDjkZV39wKIiKq7e60sRrTUu3tJkpqQJ5+E0aOzO2m0apV3mrKoazcNgB2A4cC2wKiI+HZpIrVMbVtX8NtDt6NL+9Ycf+sLzF1Ya61HkqSWqL7HIzXd3atv9ZUi4sSImAhcBJxc04aa64DakqQm4KKLoHdv+HbLOc2uUzEiIm4FLgF2JTsI2AEYUcJcLVLvLu255tDteX/uIk65+yUqV3hhRpKkKg08Hmm0u3s11wG1JUk5GzsW/vY3OOUU6NCh9vWbibqOGTECGJZS8uy4xLbfuAc/2W9Lzrn/v1z2+Juc/sXN8o4kSVJT0ZDjkYbc3euaBmSTJKlhLroIOneG73wn7yRlVdduGv8FNixlEK1yyE4D+OaIflz5jwk8Om563nEkSWoqGnI84t29JElN1zvvwN13w/HHQ48eeacpq7q2jOgJvBYRzwNLqhamlPYvSaoWLiI474CtGD99HqffM5YhJ3Zmk96d844lSVLe6n084t29JElN2q9/DRUVcOqpeScpu7oWI35ayhD6tPZtWnHNoduz35XPcNytY/jLiZ+jS/s2eceSJClPP23Ii7y7lySpSfrwQ7jpJjjkEOjXL+80ZVenbhoppaeAd4A2hcejgRdLmEvARt07cPUh2zH5o4Wcfs9YVjigpSSpBfN4RJLUrFx1FSxaBGeckXeSXNT1bhrHAvcC1xUW9QXuL1UorbLz4PU5+0tb8NhrH/DbJyfkHUeSpNx4PCJJajYWLIArr4T994cttsg7TS7qOoDlicDngI8BUkpvAb1LFUqfdNTnBnLANhvx67+/yT/Hz8g7jiRJefF4RJLUPNx0E8yaBWeemXeS3NS1GLEkpbS0aiYiWlPDPbpVGhHBr742nM037Mopd77E5I8W5B1JkqQ8eDwiSVr3LVuWDVy5666wyy55p8lNXYsRT0XE2UCHiNgb+CPwYOliqboObVtx3aHbExEcf+sLLFy6PO9IkiSVm8cjkqR13913w7vvttixIqrUtRhxFjATeBU4nmxE6nNKFUo1G7B+R64YtS3jP5jHGfe+QkpeDJIktSgej0iS1m0pwUUXwbBh8OUv550mV3W6tWdKaQVwQ2FSjnbftBc/3GczLvrbeLbu151jdxucdyRJksrC4xFJ0jpr+XKYPBkeeQRefRV+9zuoqGvbgOapTsWIiHibGvpkppQ8E87Bd3YfwqtT53LBI6+z5UZd2WWTnnlHkiSp5DwekSQ1aSnB9Onw5pufniZOzMaKANh0Uxg1Kt+sTUCdihHAiKLH7YEDgfUaP47qIiK4+MCtmTBjPifd+RIPfm9X+nbvkHcsSZJKzeMRSVL+5s6tueDw5pswf/6q9dq1g6FDsy4ZX/1qVoTYdFPYemto2za//E1EXbtpfFRt0WUR8QxwbuNHUl10btea6w7bngOu+jcn3PoCfzzhs7Rv0yrvWJIklYzHI5Kk3KQEJ54If/oTzJixanlFBQwcmBUZdt11VcFh002hf/8W3xVjTeraTWO7otkKsisTXUqSSHU2uFdnLv3WNhz7hzGcc/9/ufgbw4mIvGNJklQSHo9IknLzxBNwzTWw335Z0WGzzbKCw+DBWQsI1Vtdu2n8uujxcuAd4JuNnkb1tvewDTh5z6Fc8cRbbN2vG4d9dmDekSRJKhWPRyRJ+Tj/fOjbF/74R4sPjaSu3TT+p9RB1HCn7jmU/743l589+Bqb9+nKDgPtPitJan48HpEk5eKpp+Dpp+Hyyy1ENKK6dtM4bU3Pp5QubZw4aoiKiuA339qGA656hu/e/iIPfW9XNujaPu9YkiQ1Ko9HJEm5OP982GADOPbYvJM0K3UdTWME8B2gb2E6ARhG1k/TvppNQLcObbj+2yNYsGQ5J97+IssqV+QdSZKkxubxiCSpvP7zn2y8iB/+EDp4B8PGVNcxI3oC26WU5gFExE+BP6aUjilVMNXfpht04VdfH87Jd77Erx55g//7yrC8I0mS1Jg8HpEkldf550PPnnDCCXknaXbq2jJiALC0aH4pMLDR02it7b/1Rhyxy0BueuZt/vrK+3nHkSSpMXk8Ikkqn9Gj4ZFH4LTToFOnvNM0O3VtGXEr8HxE/BlIwP8Cf6jtRRExErgcaAXcmFL6VbXnTwOOIRsReyZwVEppct3jqyZnf2kLxk6dwxn3jmWzDbuwSe/OeUeSJKkxNOh4RJKkBvn5z6FHDzjxxLyTNEt1ahmRUvoFcCQwG5gDHJlS+uWaXhMRrYCrgX3J+nOOiojq/QZeAkaklIYD9wIX1S++atK2dQW/PWQ72rVpxXdue4EFS5bnHUmSpLXWkOMRSZIaZOxYeOABOPVU6No17zTNUl27aQB0BD5OKV0OTI2IQbWsvyMwIaU0KaW0FLgLOKB4hZTSP1NKCwuzzwL96pFHa9CnWweuHLUtE2fO50f3vUpKKe9IkiQ1hvoej0iSVH8//3lWhDj55LyTNFt1KkZExE+AM4EfFRa1AW6r5WV9gSlF81MLy1bnaOCR1bz/cRExJiLGzJw5sy6RBXxuk56c/sXNeGDsNG591t4vkqR1WwOPR4iIkRExPiImRMRZNTx/WkS8FhGvRMQTEbFx4yaXJK1Txo2De++F730PunfPO02zVdeWEf8L7A8sAEgpTaP2W2hFDctqvDwfEYeS3a7r4pqeTyldn1IakVIa0atXrzpGFsB3dh/Cnpv35vyHXuPFd2fnHUeSpLVR7+MRu41KkurtF7/IBqz8/vfzTtKs1bUYsTRl7fwTQETUZSjRqUD/ovl+wLTqK0XEXsCPgf1TSkvqmEd1VFERXPrNbdiga3tOvP1FPprvLpYkrbMacjxit1FJUt29+SbcfXc2aOX66+edplmrazHinoi4DugeEccCjwM31PKa0cDQiBgUEW2Bg4AHileIiG2B68gKETPqF1111a1jG649dHs+WrCUU+56mcoVjh8hSVonNeR4xG6jkqS6++UvoV07OP30vJM0e3W9m8YlZM0W/wRsBpybUrqyltcsB04CHgVeB+5JKY2LiPMiYv/CahcDnYE/RsTLEfHAajantbRV326cf8CWPDPhQy5//M2840iSVG8NOR7BbqOSpLqaNAluuw2OPx569847TbPXurYVCn0tH00p7QX8vT4bTyk9DDxcbdm5RY/3qs/2tHa+tcMAXpg8myv+MYFtBnTnC5tvkHckSZLqZC2OR+rbbXR3u41KUgt1wQXQujX88Id5J2kRam0ZkVKqBBZGRLcy5FGJnXfAVgzr05Xv3z2WKbMW1v4CSZKagLU4HrHbqCSpdu++C7//PRxzDGy0Ud5pWoS6jhmxGHg1Im6KiCuqplIGU2m0b9OKaw7djhUp8Z3bX2Dxssq8I0mSVFf1Ph6x26gkqU4uvDD7eeaZ+eZoQWrtplHw18KkZmDj9Ttx6Te34dg/jOFnD47jgq8NzzuSJEl10aDjEbuNSpLW6L334MYb4cgjoX//2tdXo1hjMSIiBqSU3k0p/b5cgVQeew/bgO/uMYTfPjmR7Qb04MAR/tFJkpomj0ckSSV18cVQWQlnnZV3khaltm4a91c9iIg/lTiLyuy0vTdllyHrc879/+W1aR/nHUeSpNXxeESSVBrTp8N118Fhh8GgQXmnaVFqK0YU3w5rcCmDqPxat6rgilHb0r1jG75z+wvMXbQs70iSJNXE4xFJUmn8+tewdCmcfXbeSVqc2ooRaTWP1Uz07NyO3x6yHe/NXsTp94xlxQq/ZklSk+PxiCSp8X34IVxzDYwaBUOH5p2mxamtGLF1RHwcEfOA4YXHH0fEvIiwXX8zsf3G63H2l7bg8dc/4LqnJ+UdR5Kk6jwekSQ1vt/8BhYuhB//OO8kLdIaB7BMKbUqVxDl68jPDeSFd2dz8aNvsHX/buwypGfekSRJAjwekSSVwOzZcOWV8I1vwBZb5J2mRaqtZYRaiIjgwq8PZ1DPTpx850tMn7s470iSJEmSVBqXXw7z5sE55+SdpMWyGKGVOrdrzbWHbs/CpZWccNsLLF5WmXckSZIkSWpcH3+cFSO++lUYPjzvNC2WxQh9wtANunDpN7dm7NQ5fP/ulx3QUpIkSVLzctVVMGeOrSJyZjFCnzJyqz78+Etb8Mh/p/PLh1/PO44kSZIkNY758+HSS+FLX4Ltt887TYu2xgEs1XIdvesgps5exI3PvE3/9Tpy+C4D844kSZIkSWvnmmvgo4/g//4v7yQtnsUI1Sgi+L+vDOO9OYv42YPj2Kh7B/YetkHesSRJkiSpYRYuhEsugb33hp13zjtNi2c3Da1Wq4rgioO25TN9u/G9O19k7JQ5eUeSJEmSpIa54QaYMcNWEU2ExQitUYe2rbjx8B3o1aUdR/9+NFNmLcw7kiRJkiTVz+LFcNFFsMce8PnP551GWIxQHfTq0o5bjtiRZZWJI255nrkLl+UdSZIkSZLq7uabYdo0W0U0IRYjVCeb9O7M9Ydtz5RZizju1jEsWV6ZdyRJkiRJqt306fCrX8Euu8D//E/eaVRgMUJ1ttPg9bn4wOE89/Yszrj3FVasSHlHkiRJkqSajR0LRxwBAwbAe+/B+edDRN6pVGAxQvVywDZ9+eE+m/GXl6fx67+PzzuOJEmSJK2yYgX89a+w556wzTZw771w/PEwfjx84Qt5p1MRb+2pevvuHkOYOnshV/9zIv16dGTUjgPyjiRJkiSpJVu4EP7wB7jssqzw0K8fXHghHHss9OiRdzrVoKQtIyJiZESMj4gJEXFWDc/vFhEvRsTyiPhGKbOo8UQE5x+wFbtv2otz7v8vT46fkXckSZIkSS3R++/DOedA//7wne9Aly5wxx0waRKccYaFiCasZMWIiGgFXA3sCwwDRkXEsGqrvQscAdxRqhwqjdatKrj6kO3YbIMunHj7i4ybNjfvSJIk1ciLI5LUDL38Mhx+OGy8Mfzyl7D77vCvf8Hzz8OoUdCmTd4JVYtStozYEZiQUpqUUloK3AUcULxCSumdlNIrwIoS5lCJdG7XmluO3IGuHdpw1O9GM23OorwjSZL0CV4ckaRmZMUKeOihbOyHbbeFP/0paw3x1ltw332w664OULkOKWUxoi8wpWh+amFZvUXEcRExJiLGzJw5s1HCqXFs0LU9txy5AwuXVHLU70bz8eJleUeSJKmYF0ckaV23cCFccw1ssQXstx9MmAAXXwxTp8Lll8OQIXknVAOUshhRU0mqQfeCTCldn1IakVIa0atXr7WMpca2+YZduebQ7ZkwYz4n3v4iyyo9lpMkNRleHJGkddk992TjQXz3u9CtG9x5J0ycCD/4AXTvnnc6rYVSFiOmAv2L5vsB00r4fsrRrkN7csHXPsO/3vqQs+97lZQaVHeSJKmxeXFEktZVf/4zHHwwbLYZPPMMPPccHHSQ40E0E6W8tedoYGhEDALeAw4CDi7h+ylnB47oz5TZi7jiibfov15HTt5zaN6RJEny4ogkrYv+9jf41rdghx3g0Uezu2SoWSlZy4iU0nLgJOBR4HXgnpTSuIg4LyL2B4iIHSJiKnAgcF1EjCtVHpXH9/cayte268ulf3+T+16cmnccSZJWXhyJiLZkF0ceyDmTJGlNnnwS/vd/Yaut4JFHLEQ0U6VsGUFK6WHg4WrLzi16PJrsCoWaiYjgV18bzvS5iznj3lfo2r4New3bIO9YkqQWKqW0PCKqLo60Am6uujgCjEkpPRAROwB/BnoA+0XEz1JKW+YYW5Jarmefha98BQYPhscec1yIZqyUY0aohWrbuoJrD9ueLTfqyndvf5F/jp+RdyRJUguWUno4pbRpSmlISukXhWXnppQeKDwenVLql1LqlFJa30KEJOXkpZdg5Ejo0wcefxx69sw7kUrIYoRKomv7NvzhqJ0YukFnjr/1BZ5568O8I0mSJElqqsaNgy9+MbtjxhNPZAUJNWsWI1Qy3Tq24bajd2Jwz04c84fR/GfiR3lHkiRJktTUvPUW7LVXdpeMJ56AAQPyTqQysBihkurRqS23HbMT/Xt05Ojfj2b0O7PyjiRJkiSpqZg8GfbcE5Yvz7pmbLJJ3olUJhYjVHI9O7fj9mN3YsOu7TnyltG8+O7svCNJkiRJytu0aVkhYt68bLDKYcPyTqQyshihsujdpT13HLsz63duy+E3P88rU+fkHUmSJElSXmbOzLpmfPBBdvvObbfNO5HKzGKEymbDbllBoluHNhx20/OMmzY370iSJEmSym327Gywyrffhocegp13zjuRcmAxQmXVt3sH7jx2Zzq1bcWhNz7H+Onz8o4kSZIkqVzmzYN9983unvHnP8Puu+edSDmxGKGy679eR+44dmfatq7gkBufZcIMCxKSJElSs7dwIey3H4wZA/fcAyNH5p1IObIYoVwM7NmJO47dGQhG3fAck2bOzzuSJEmSpFJZsgS+9jV4+mm49Vb46lfzTqScWYxQbob06sydx+7EihWJg294jskfLcg7kiRJkqTGtmwZHHQQPPoo3HADjBqVdyI1ARYjlKuhG3ThtmN2YvHySg6+4Tmmzl6YdyRJkiRJjaWyEg4/HO6/H664Ao4+Ou9EaiIsRih3W/Tpym1H78S8xcsYdcOzvD93Ud6RJEmSJK2tFSvg+OPhzjvhggvge9/LO5GakNZ5B5AAturbjT8cvROH3vgcB9/wHHcdtzMbdG2fdyxJkiSpZVm0CN59F6ZOhQULsrEeli7NpqrH1X+ubtkHH8D/+39wzjlw1ll5fzI1MRYj1GRs0787vz9qBw676XkOvuFZ7jrus/Tq0i7vWJIkSVJ5LFuW/WzTpjTbTwnmzIHJkz85vfvuqsczZtRvm23bQrt22c/ix1U/f/5zOPvs0nwerdMsRqhJ2X7j9bjliB04/JbnOeTGZ7nz2J1Zv7MFCZVeSokVCVpVRN5RJElq/pYuheefh06dYOutoWId6z1eWQmzZ8NHH2XTrFnZz/nzs9tXLlr0yZ91ebxoESxfnm2/bVvo3Bm6dMl+Fj+uy7JWrbKWDdWLDpMnZxmLtW8PAwbAxhtn38XGG2fzAwZk26peXCh+3KYNhMdOahiLEWpydhq8PjcdvgNH/W40h970PEfvOoh+PTrQf72ObNClHa1bNe3/rJZXrmDBkko+XryMeYuXM3/JcuYtXsb8JctZsmwF/dbrwCa9O9OrczvCf7zLbvGySt75aAGTZi5g0sz5TPpw1eP5S5bTp1uHlb9v/Xt0pP96HehX+LlBl/ZUWKyQJKn+UoLXXoO//z2bnnoq6wIA0Ls3fPGLMHIk7L13Nl/ubNOmZS0CqooL1YsM1ac5c7LXrUnbttCxYzZ16PDJn716fXK++HFK2b6ZNy8rHFT9nD8fpk//5LIlS2r/fD16ZIWFIUPgC1/Iig1V04AB2f72mFQ5iFTbH1ETM2LEiDRmzJi8Y6gMnnpzJt+97QUWLK1cuax1RdCne3v6de9Ivx7ZSWL2swP91uvIhl3br9WV7RUrEvOXLmfuwmV8vHgZcxct4+NFy/l48TI+XvTp4sK8xcv5ePFy5hcVHhYW5V2TLu1bM6RXZzbp3ZkhvTozpFcnNundmQHrdWzyBZemLqXE+3MXM2nmAt7+cD4TZy4oFB3m896cRZ84dtiwa3sG9+rEoJ6d6N6xDdPmLGbKrIVMmb2QDz7+5H/wbVtV0LfHp4sV2c+O9OjYxgKTchURL6SURuSdoyXweKQFSAleeCG7A8CYMbDjjrDPPrDTTtDa63l18sEH8PjjqwoQ06ZlyzfdNCs67LVXdlL9t7/BY4/Bhx9mz2+/fbavR46EnXdu/C4LM2bA6NFZy4zRo7Op6r2r69IF1l8f1lsv+7mmab31oGvXrKjQoUPWOqHUli1bVagoLlosXQr9+mXFhq5dS59DKqjPsYjFCDVpS5ZXMm3OYqbOXsjU2YuKfmaPq58srq5Y0aoiVhYUqgoMcxcVFRwWZ8vmLV7Gilr+JDq1bUXn9q3p0r4Nndu1pkv7wtSuDV3at175XJfCcyvn27emTUUFk2ctYOKM7AR5woz5TJw5nxnzVn2ONq2CjdfvxCa9OjOkd6eVBYvBvTrTud26efCzdPkK5ixaypyFy5izcBmzFy5lbtXPRdk+rwioiKAiICJWPq5qibByPoIoWreiIghg5vylWUuHmQt4+8MFLFq2qijUsW2rQsGhM4N7dmJwr2y/DurZiU5r2KeLl1Xy3pxFheLEIqYWihRTZmW/f7MXLvvE+p3atqL/eh1XFpeG9C58dz0706FtGQ5I1OJZjCgfj0dqUVkJixdnzc4XL655Wt1zxcsHDYLddoPPfKY8zfiXLcuu2N9/P/zlL1kz91atYLPN4I03sjsDdOuWnUSPHJmdMPfvX9pMS5fCyy/Df/4Dzz6bXTEfPPiT08CB2RX1vC1aBP/6V1Z4eOwxeOWVbPn668Oee2YFiL33zq7IV7diBbz4YlaYePTR7PNWVmYn0nvume3rffbJPmt9zJuXFZWKiw+TJ2fPVVTAllvCDjvAdtvBRht9urjQtu1a7RKppbEYoRZj8bJK3p9b92IFQIc2rejaoTXdOrSha/s2dO3QpvC4ddHjNnTtkM13bb9qWef2rUsypsDHi5etLFBMnDl/ZZFi8kcLqSyqjmzYtT1DeneiZ+d2dGrXmi7tWtOpMK163Iou7bPHnQtTp3ataVOP1hYpJZZVJpYsr2Tp8hUsWb7iEz+XVlayZNkKFi2rXFVcWJT9LC44ZI+XfqJ1S3WtK4KKilg5ZsOKlGpt9ViTioB+PTqubOUwuFdnhhR+btC1NF1i5i1extTZq4oVU2YtZMqshUycOZ93Zy1cWdiKgL7ds+45WZGp88rHPTp5kNPSfDh/CW9On0eX9m34TL9ujbptixHlk8vxyKJF8P772dXl6lPV8rZtYZNNYOjQbKp63KdP4zfDTgneew/Gj4c338x+Vj1+553s5LKhWrfOPsvChdl89+6w665ZYWK33bITx8a6Wj5/fnby++c/w1//mjW/79AhKzZ89avw5S9nJ6azZ8MTT2Qny3/7W/bZAYYNW1WY2G23rP/92pg6dVXh4dlnsxPpqqb4/ftn+2LSpFVdHKr06fPJAsWgQase9+lTmmLOihVZoaSq5cMzz2RZ27aFz30u63qx996w7bb1f/85c/5/e2ceJEd13/HPb2f2lFYHILQSp7AllS8Og4QhBhxxmDiJBHEwOLZjF0lRtoPjoxzHGBvLIfEdHykncbkMxrETEx9lDLYTgQELxwFsDklIFrpASAKtFqSspJV2V3v88sd7s9Oane7p1zM7Gq1+n6qu6e7p/s6ve3793q9//d5reOCBYnJi2za3fuHC4vm+5JLDkzCDgy4BEk08rF9f7E4xb55r4bJokfs85xw3xoJhGDXDkhGG4SkkK1SVae2udUJr/uh5Qn1oeJRtew6wucclKbb09LHlpQP0HjxEn+8WMjicLthrzTeNJSamtubJ56Qk0TDCYGQ5lCaBGR0tzGhvZkZHs5vvaGZmYd0U9znTry9819GSG5coUJ+QGC1JULhlty6avBhVZVpbM23NjfPfFsam2NLjWsBs9v/fMy/1MTBUPL/HTWk5LEFR6K4ze1pbUAKpGlSV3oND7Nw7QPe+frr3DtK9t5+dewfo7R9iVmcrc6a1MXt6G3P81DW9/ahtqVMv9h4cYmPPfjZ072fjrsLUx54DhwC4+pyT+PK1Z9f0Ny0ZUT9qGo+MjMD27eWTC9Gpt3f8vq2t7mnu3LnuhnNgADZtcjerQ5HWW1OmuMREuURFV1dyomLfvmKyoTTpUEgWgLspXLDA3Sy+/OWuBUFb2/ipvT15fWtrsRvEc8+5J+0PPeSmDRuKx3PhhcXkxOLFYUmAnh645x7XAuK++9xN7PHHw9KlLgFx2WXJLQ0K4x8UbpRXrnQtGNrb3Q3ylVe6acGC5HM7MOCSDYXEw8MPF5Mcra1w3nmum8IFF7jPk04q/v5LL7n/udy0ffvh4xm0th6enJg7160fGak8jY6WX3/gAPz618XuDa95TbHlw0UXuf+oVqi6likrVrhzvnKlO3etre7/P+MM16pi9Wr3P4Abk2Hx4mLyYdEiOOGE2tlkGEZZLBlhGMcQQyOjHBh0iYkDgyP0DQ7RNzji1g0U1rvP6PzwqNKab6Iln/OfTZFPt66w3JJrorW5iZbc4du2NedcYqG9hc62vA3umILRUeX53v6x1i+be/rGkhW9Jd0+OlvzTPeJmxntLW6+vZjImd5eTPrMaG/237fQki8mMUZGld19g+zcO+CSDXv76d5XTDZ07xuge+/AuKRWk8CszlZmtLfwUt8gu/0NdKl9hQRF17RikqJreitd09qZM72NGQ00jsboqDI8qjTnpKY29Q0Os2nXfjbt6mPDrmLiIdoya2prnvmzp7JwdifzZ3eycHYnC7s6a/76YktGlEdErgS+CuSAb6rqZ0u+bwX+DTgX2A1cq6pbkzRrGo/09MDs2Yeva252yYVCoiE6RdfPnNNiqXsAABO2SURBVFn+ZndkxD1J3rQJNm92n4XpmWeKI/aDezIcTVTMnOm2KyQeuruL2zY1uWbyhaTDwoXF+blzJ74rxa5dLjmxcqVLTjz1lLtRbWlxYzkUkhMXXjj+ifeWLS75cNdd7iZa1R3L1Ve7BMSFF2YfC+LAAWdT4WZ540a3/rTTik/xlyxxgyFGEw+rVhWTRvPmuYRDIflw1lnZuwgMDrr/v1yiYssW13WhlFwubGpudsmSwtgPc+ZkszUL/f3u/y+c7x073DgThRYPixa5sRIapP4xjGOJhklGNHzlbxiG0UDs7hv0SYoDvLh/kN5+N7ZGb7/r7tLbPzS2PJIwuMmUlhwzOlpQVXbtHxy3bUuuidnTW5kzrX1cMqGwPGvq4W+uGRgaoWffIN37Bti5t5/useRGMaHRs39g3Jgrrfkmuqa30dGS94ktoSXfRHPOJbma80205vxyvvh52Hb+U1UZ8N2DBoZG/OcoA3653LrCcv/QyFiLn1yT0JZvor0lR1tzjvbm3OHzpcstTbTli+vyTcLW3QfHkg47/q9/7HjbmpuYf2LnWOJhwexOFnR1Mnd6W12SMpaMGI+I5ICNwOXADuC3wFtV9XeRbd4LnKmq7xaR64CrVfXaJN2axiOjo3DHHYcnHI47buJu7IeHi4mK0mTFs8+67084oZhkiCYeXvYy9zS6UdizxyUWCi0nHn/cJWJyOXdzevHFzt6f/ATWrnX7nH22Sz5cdRWceebE3LA++2zxRvn++8e/SrGjw90wFxIP55/vWqjUA1V3M9/UVEwsHG2v1TQMo2FpiGTEUVH5G4ZhHIWoKvsH3Vtfeg8OFQcH7R9irx+rozC45liCYVobXT7RcNyUlgm5MR4eGeXFvkGXoCgkK3yiopAMGBoZHfscLCyPjDI0rP5zlMGRyl2FmnNCWz5HW0uOtuZIsqCwrpBs8Otbm5to94mE8QmNEfoPufn+oVEGxuZHGDg0wsDwCEMj4xM6Z8yawgLfwmH+iVNZ2NXJyTM7JmRcmbRYMmI8InIBsFxV3+iXbwJQ1c9Etlnht3lYRPJANzBLE4KkSRuPDA+7G+cZM460JdnYv9+1OCgkJx591B3TxRe75MOyZeEDIFbLoUPOpgcfdK9QvOAC16XB3shhGMYkJCQWmchScDGwWVWf8UbdCSwDfhfZZhmw3M//EPiaiEhS5W8YhnGsIyJukNW2Zk457khbUySfa2LO9HbmTG+vWkvVdamIJjBExCcYmur++tuhkdGx5MWh4VG6prXZK3iPHk4CtkeWdwDnx22jqsMishc4HjjsXX8icgNwA8Cpp546UfYeWfL5ozcRAe41jFdc4SZwLQAOHXLjVxwpWlrcOBKXXHLkbDAMw2hAJjIZYZW/YRiGkQkRoTkndRvEsxLNvitJZ1uN33Vv1INyTVVKH3qk2QZV/QbwDXAtI6o3zZhw2tvdZBiGYTQcExnl1bTyV9XzVPW8WbNm1cQ4wzAMwzCOCXYAp0SWTwZeiNvGd9OYDuypi3WGYRiGcYwykckIq/wNwzAMwzjS/BaYLyLzRKQFuA64u2Sbu4F3+vk/BR6wLqOGYRiGMbFMZDLCKn/DMAzDMI4oqjoM3AisANYD31fVdSLydyKy1G92G3C8iGwGPgR89MhYaxiGYRjHDhM2ZoQfA6JQ+eeA2wuVP/CYqt6Nq/y/4yv/PbiEhWEYhmEYRs1Q1Z8DPy9Zd0tkfgC4pt52GYZhGMaxzIS+U8gqf8MwDMMwDMMwDMMwSpGjrVeEiLwIPFdj2RMoeYPHUazRSLbY8UyMRiPZYsfT2LY0ikYj2TLZjqeU01TVRnquAxaPHFW2NIpGI9lixzMxGo1kix1PY9vSKBq11CmQOhY56pIRE4GIPKaq500GjUayxY5nYjQayRY7nsa2pVE0GsmWyXY8xuSiUfxqsl0njaLRSLbY8UyMRiPZYsfT2LY0ikYtdbLQGC9wNwzDMAzDMAzDMAzjmMGSEYZhGIZhGIZhGIZh1BVLRji+MYk0aqXTKBq10plMGrXSaRSNWuk0ikatdCaTRq10GkWjVjq1ssWYPDSKX02266RRNGql0ygatdKZTBq10mkUjVrpNIpGrXQmk0YtdYKxMSMMwzAMwzAMwzAMw6gr1jLCMAzDMAzDMAzDMIy6ckwnI0TkdhHpEZG1VWicIiIPish6EVknIu/PoNEmIr8RkdVe41NV2JMTkSdF5KdVaGwVkadEZJWIPJZRY4aI/FBEnvbn5oLA/Rf63y9M+0TkAxns+KA/p2tF5Hsi0haq4XXe7zXWpbWjnH+JyHEicp+IbPKfMzPqXONtGRWRiqPfxmh8wf8/a0TkxyIyI4PGrX7/VSJyr4jMDdWIfPdhEVEROSHj8SwXkecjPvOmLLaIyPtEZIM/v5/PYMd/RmzYKiKrMh7P2SLySOE6FJHFGTTOEpGH/fV8j4hMq6BRtjwL8dsEjdQ+m6AR6rNxOqn9Nk4j8n1Fv02wI8hnjclJnF+LyOki0h/xj69n0Yl8f6qI9InIhzPYsjhix2oRuTqDxuUi8rgvjx4XkSUZNI7311KfiHwt6XxUOicicpOIbBZX3r8xQaNs2SUiLSLyLX88q0XkDRk0mkXk215jvYjcVOF44nTeJofHTKMicnaIhv/uTHF1xjpvU9mYKcGOUJ9NrBdS+mycLSE+G6cR4rNxGqE+m/T/pPLZkn2C4oAYjaB4JEEnOD6K0Ukdp8XsX9O6VwLi1zL7BsXRMRpBsVGMRtB9RU1R1WN2Ai4GXgusrUJjDvBaP98JbAReGaghwFQ/3ww8Crwuoz0fAv4D+GkVx7QVOKHKc/tt4C/9fAswowqtHNCNe2dtyH4nAc8C7X75+8C7Mvz+q4G1QAeQB34BzM/iX8DngY/6+Y8Cn8uo8wpgIfBL4LyMGlcAeT//uUq2xGhMi8z/NfD1UA2//hRgBfBcGt+LsWU58OGA/7Wcxu/7/7fVL5+Y5Xgi3/8jcEtGW+4F/sDPvwn4ZQaN3wKX+PnrgVsraJQtz0L8NkEjtc8maIT6bJxOar+N0wjx2wQ7gnzWpsk5xfk1cHpc2RKiE/n+R8APknwuwZaOyPo5QE9hOUDjHGCun3818HwGO6YArwfeDXytinP7SmA10ArMA7YAuRiNsmUX8FfAt/z8icDjQFOgxp8Bd0bO8Vbg9ITjqViOAq8BngnVwMU3a4Cz/PLxGc5JqM8mHk9Kn42zJcRn4zRCfDZOI9Rn43RS+2yJXlAcEKMRFI+k1EwVH5XZLyhOi9FYnuRTgVpB8WuZ/YPi6BiNoNgoxO/qMR3TLSNU9SFgT5UaO1X1CT+/H1iPuwkO0VBV7fOLzX4KHsxDRE4G/hD4Zui+tcRnXS8GbgNQ1UOq2luF5KXAFlV9LsO+eaBdRPK4iumFDBqvAB5R1YOqOgysBGIz7AVi/GsZLlGD/7wqi46qrlfVDSlsT9K41x8PwCPAyRk09kUWp1DBbxOuuS8DH6m0fwqd1MRovAf4rKoO+m16stohIgK8BfheRlsUKDzBmE4F343RWAg85OfvA95cQSOuPEvtt3EaIT6boBHqs3E6qf22Qhmfym9rUU8Yk5dQv86iIyJXAc8A67JoROo/gDaSr5k4jSdVtVCOrQPaRKQ1UOOAqv4PMJB0HJV0cGXanao6qKrPApuBsk97E8quVwL3+216gF6g7BPFBA0FpvgYpR04BOwrs10lnShvJaHeSdC4Alijqqv9drtVdaQKOyqSpBPgs2U1An02TiPEZ+M0Qn027pyk9tkSguKAOLMIiEcqERIflSEoTqsDQfFrKaFxdIxG1XVIra7pLBzTyYhaIyKn47Koj2bYN+ebK/UA96lqsAbwFdwFMZph3ygK3OubpN2QYf8zgBeBb4nrMvJNEZlShT3XkaHAUtXngS8C24CdwF5VvTfD768FLvZN7TpwWeFTMugAzFbVnd6+nbinKY3A9cB/ZdlRRP5BRLYDbwNuybD/UtzThtVZfr+EG30ztdslRReYMiwALhKRR0VkpYgsqsKWi4Bdqrop4/4fAL7gz+0XgcTmuzGsBZb6+WsI8NuS8iyT31ZTJqbQCPLZUp0sfhvVyOq3ZY6nWp81Jhelfj3P16MrReSiLDq+/v1bILQL6GG2iMj5IrIOeAp4dyT4Ta0R4c3Ak4UbiowaoUR1TgK2R77bQXiCcDWwTETyIjIPOJfw2OCHwAFcjLIN+KKqVpVoB64l203eAkBFZIWIPCEiH8n4+1l9dowqfLZUJ4vPxhHisxNBVp/NHAdEqEU8EqWa+KhWcVrVdW+t4tdq4+gSalVe1o38kTZgsiAiU3HNyT5QkuVKhc8+n+37+fxYRF6tqqnHshCRPwJ6VPVxSei3mJLfU9UXRORE4D4Redo/dU1LHtdc/H2q+qiIfBXXtPsToYaISAuuEA0u+HzhsgzXnK0X+IGIvF1Vvxuio6rrReRzuIxyHy4AqaZCayhE5Gbc8fx7lv1V9WbgZnF9XW8EPhnw2x3AzbgnMtXyr8CtuGTarbgmgNcHauSBmcDrgEXA90XkDFXNkvFOfDqVgvcAH1TVH4nIW3AtjS4L1Lge+CcRuQW4G/fUrSKl5Zl7iBFGtWVikkaoz5bTCfXbqIb/7WC/LXNea+GzxlGAiPwC6Crz1c2q+hO/Talf7wROVdXdInIucJeIbAZmBep8Cviyqvb5a/kGEXlXoAb+IcmrROQVwCMi8veMf4qXqOHXvwrXlLhbyo/ZVVGjRC/LuS0t1K4ELheR5XEaZbgd13LyMVwT7WHgSzJ+3K8kjcXACDAXV/f8SkTeiXv6HHs8cYjI+cBB4CsiknhOypDHdSdY5DVeEJH34JIlaTUy+WwZgn22HKE+G0eIz6Yhjc+W263MOq2kR8o4oILGpaSMR1IeW2J8VMGWVHFaBY3UdW8FnY+RIg6odE7SxCMZy7lgjSOC1rFPSCNOBPZvi9FoxvUX+lCNbPokgX2ZgM/gsqRbceMrHAS+WwNblmewpQvYGlm+CPhZxt9fBtybcd9rgNsiy38O/EsNzsmngfdm8S9gAzDHz88BNmTRiaz/JSn7dpXTAN4JPAx0VGOH/+60NNdSVAPXt7XH++1WXCG6Deiq0pZU13WZ/+e/gTdElrcAszKc1zywCzg5wK9KbdkLY69fFmBfledkAfCbFBrjyrNQvy2nEeqzcRoZfDaxfE7jt6UaWfw2hR2pfNamyTml8es01045HeBXEV/txXXnurFKWx5MsiVOA9d8eCPuoUfmcwK8ixT97xPOyU3ATZHlFcAFFXQSzz/wv1QYM6xUA/hn4B2R5duBt6Q4prK24JqMfyzleSm15TrgjsjyJ4C/qfKcVPTZGFuCfDalLYk+G6cR4rMV/pvUPhtzToJ9toxmqjigzH7B8UiCVnB8VLJ/cJxWQe90MtS9VBG/JmimiqNj9g2KjdL6XT0m66ZRJeJStrcB61X1Sxk1ZklxpOh2XLbx6RANVb1JVU9W1dNxFcoDqvr2DLZMEZHOwjwu4xf0thFV7Qa2i8hCv+pS4Hehtniqebq8DXidiHT4/+lSXF/tYHwrEUTkVOBPqrDpblyBgf88YplIEbkS1wxyqaoezKgxP7K4lHC/fUpVT1TV073v7sAN9NedwZY5kcWrCfRbz13AEq+3ADf46ksZdC4DnlbVHRn2LfACcImfXwIEN2eM+G0T8HGg0sjmceVZar+tUZlYViPUZxN0UvttOY1Qv02woxY+axzlxPm1jw1yfv4MYD6uD32QjqpeFPHVrwCfVtWyo/on2DJP3LgGiMhpuH7oWwM1ZgA/w91Q/Tr+jNSmfqqgczdwnYi0iutiMR/4TaB2h4+TEJHLgWFVDY11tgFLxDEF97Q3qB6N2NOEewhzZ5b9cTe3Z/rjyuPqn6DjCfXZOEJ8NsGW1D6boJHaZ+tAJp8NjQNiqDoeiVBtfFR1nFaLurdW8Wu1cbTXqEl5ecSoZ+aj0SbcDeVOYAjnRH+RQeP1uGY+a4BVfnpToMaZwJNeYy0ZRpct0XsDGd+mgRvvYbWf1uGa7mTRORvXdHENruCYmUGjA9gNTK/iXHwKd2GvBb6DH303g86vcJXyauDSrP6FG536flxBfj9wXEadq/38IC7DvCKDxmZc/8OC31Z6E0Y5jR/5c7sGuAc3OGDma46Ub3KJseU7uH6ha3CV9pwMGi3Ad/0xPQEsyXI8wB24/qlp/aucLa/Hjc6+Gje+wLkZNN6Pe6KzEfgs/slGgkbZ8izEbxM0Uvtsgkaoz8bppPbbOI0Qv02wI8hnbZqcU5xf4/qor/NlwBPAH2fRKdlmOclvJoiz5R3ellXelqsyaHwc1+R/VWQqOxJ+0rH4620PrtvkDhJaI1TQuRn3VHUD/k0BMRplyy7cE9UNuIccvyDhjV8JGlNxb4tYh4sxKrVEiC1HcbHfIyn8LUnj7d6WtcDnMxxPqM9WrBdS+GycLSE+G6cR4rNJ5zXEZ5N0UvlsiV5QHBCjERSPVNC6g4D4qMz+QXFajEbN614yvomQwDg6RiMoNgr1u4meCk1uDMMwDMMwDMMwDMMw6oJ10zAMwzAMwzAMwzAMo65YMsIwDMMwDMMwDMMwjLpiyQjDMAzDMAzDMAzDMOqKJSMMwzAMwzAMwzAMw6grlowwDMMwDMMwDMMwDKOuWDLCMAzDMAzDMAzDMIy6YskIwzAMwzAMwzAMwzDqiiUjDMMwDMMwDMMwDMOoK/8PwyaVqq+ylDoAAAAASUVORK5CYII=\n", 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\n", 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ftG5PQi2RcqBbBeUOjM69RzRfSKim/z7hl/IB0b6Oid6f5+O2rU3Z6YSH6n5R+bMJNZAqG0EjqXOM5nsTRo34lFCbZgDhb+DnhJoLg2tRbkn0/vyc8DfeN/osTQXmReVqc30rvQ7AeYTvkJYV7O/MaPtWwM2xGCood31l62r4eZ5I6Nx3JOGz/CPCCBGbgf5RmVjHma8R/m53j67PTP7bceYBhJpjNxG+J3ckNI+5D2gRlTkrKvO76JoMiq5FZR1nnhbF8buobH/C98ffovX9Cd91P4pi35PwnXZf3D6S6ZPiTUJCIfb/wAGEUZxWxsU2ivBZPDv6jPyK8N0Y3yfFnVG5vlG8jwHFSbwH7Ql9suwXfSZ3i2J6NVrfjFDTL9ZxZgHhu+f0aH2F/Urw/Y4zBxG+Oz+O7beybYFbCf/H/Jjwvf8Hwud1QV0/b5o0adKU7injAWjSpElTXSfgkuhmcBmhhsAX0Y1y/AgcQ6IbxI2EhME5wEukLknxG8KoERuiG8P/Sdjme/smVC+/KNrXFkJTjNeJGxaTakb3iG4+vZrp1LiysSEA5xEeVr93bkmexzaEB/viKO6vCQ9D1XYAGN24VxbnlVGZJwntryvaPic63rXVHSvJz81JhAe21YSHrq8IiZxDKilf2yTFHdFnsrKhTWdG7/UzwJuVlGlO6B8l/v3qTegccGH0XqwnJCKuIkrO1bJsAXAPobO+LYRfw58FDq3ivJM6x7j5roSH9/mEB9hl0XtxIt8f3jPZcl0I/QHMJfyNbyT8ynwz0CsqU9PrW+V1IDwoPlDJ/jpFn6lfEf62r6mk3C7EdVxZh8/yrwjfZ19H1+lLQuJiRFyZ2BCkp0TntCnapiBhXz8GXiE8GK8ndDR7S8L1PpnQV8Xm6PyepuohSI8ijD6ykdAfzkz+25FwtyjWL+Niv4u4oWdJbnSPy6Jyy6P9fE54sK+og+Ivo/N7kJBgiU9S/D36vG0kJDieBgYk8R60ItTIWRQd/5tovltcmXaEv5WvojILib5jqaLzS0Li5q3oPVtF3BCklW1LGOr2seh6ryA0t7oeJSk0adLUAKZY5lxERGrJzJwwrNuDmY6lLhrLeYjID5nZtYRaUTtnOhYREZGqqE8KEREREREREckKSlKIiEhKRMPdfVvZlMLj7F3VccysrIp1v0tVHCLSdJjZvtV87+yR6RhFRBoLNfcQEZGUMLN8IL+y9R56l0/FcbYhtGOvzDaE9uQVKXH3xGEpRUSqlMT3zhJ3T9cQxiIiTYqSFCIiIiIiIiKSFdTcQ0RERERERESygpIUIiIiIiIiIpIVlKQQERERERERkaygJIWIiIiIiIiIZAUlKUREREREREQkKyhJISIiIiIiIiJZQUkKEREREREREckKSlKIiIiIiIiISFZQkkJEREREREREsoKSFCIiIiIiIiKSFZSkEBEREREREZGsoCSFiIiIiIiIiGQFJSlEREREREREJCsoSSEiIiIiIiIiWUFJChERERERERHJCkpSiIiIiIiIiEhWUJJCRERERERERLKCkhQiIiIiIiIikhWUpBARERERERGRrKAkhYiIiIiIiIhkBSUpRERERERERCQrKEkhIiIiIiIiIllBSQoRERERERERyQpKUoiIiIiIiIhIVlCSQkRERERERESygpIUIiIiIiIiIpIVlKQQERERERERkaygJIWIiIiIiIiIZAUlKUREREREREQkKyhJISIiIiIiIiJZQUkKEREREREREckKSlKIiIiIiIiISFZQkkJEREREREREsoKSFCIiIiIiIiKSFZSkEBEREREREZGsoCSFiIiIiIiIiGQFJSlEREREREREJCsoSSEiIiIiIiIiWUFJChH5jpm9ZmabzOzbaJpXg20PMrM3zGydmS03s9fNbHQNtv9d3HE3mVlZ3Pzs2p2RiIiINCRm9qCZfWVma83sUzM7owbbFprZ02a2ysxWm9kcM7vOzDrWMpZ9zczN7KLabC8itaMkhYgkGufubaKpfzIbmNkxwATg/4DuwLbAFcBh0fq2ZrZNVftw9+tjxwXOAt6Ni2NQXU5IREREGow/AL3dvR0wGrjWzIZXt5GZ7Qm8BrwN7OzuHYBRQCmwS1Sm2vuRBKcAJdG/IlJPlKQQkWqZ2alm9raZ/cXM1pjZXDP7SbTOgJuBa9z9H+6+xt3L3f11d/9ltIvBwFIzu8vMRmbqPERERCS7uftsd98cm42mvlGthiVRzcsVZrbIzE6M2/RPwH3u/gd3/yba1xfu/nt3fy0qk/T9iJm1Ao4BzgX6mVlhCk9TRKqgJIWIJPpD9J//22a2b9zyEcBCoDPwe+BxM8sH+gM9gEcr26G7vwvsBiwF/m1mn5jZRWa2fbpOQkRERBomM7vTzDYAc4GvgMnRqu0I9yHdCLUb7jaz/mbWGtgDeKyq/dbwfuRo4FtCTdHngZPrfmYikgwlKUQk3sVAH8J//ncDT5lZ32jdMuBWd9/q7g8D84BDgE7R+q+q2rG7F7v7VcCOwK+AnYE5UdvRnqk/FREREWmI3P0coC2wN/A4sDlu9f+6+2Z3fx14BjgO6Eh4rvk6VsjM/hT1S7HezC6P23ey9yOnAA+7exnwH2CsmeWm43xF5PuUpBCR77j7FHdfF/3n/y9Cu86fRau/dHePK/45sAOwMppPqlZEtI9PgFnAEmAQ0DoV8YuIiEjj4O5l7v4Woa+rs6PFq9x9fVyx2L3IKqCcuHsRd78o6pdiItC8gv1Xej9iZj2A/YB/R8WfBPIIP86ISJopSSEiVXHAotfdov4nYnoSqkvOAxYTqkVWysxamtkxZjYJmA8MB84D+rj7JymPXERERBqD5kCsVmfHqGlHTE9gaZS4mAIcVd3Okrwf+TnhOekpM/ua0Nw1DzX5EKkXSlKICABm1iEaRjTPzJpHnVHtQ2iHCdAVOM/Mcs3sWGAAMDn6JeIC4H/N7DQza2dmzcxsLzO7O9r3UEJzkPMJv0b0cPeT3f3VhNoZIiIi0kSZWVczG2Nmbcwsx8wOAsYCr8QVu8rMWpjZ3sChhD4jAC4CTjezS8ysa7S/7kBB3P6TvR85GbgKGBY3HQ0cYmadEJG0+kHVJxFpsnKBawltM8sInVUd4e7zzGwPwi8U/YAVwDfAMe6+EsDdHzWzb4HLgL8AG4HZwI3RvpcBRe6+oB7PR0RERBoWJzTt+Dvhx9TPgd+4+5NRZ95fE5p2LAU2AGe5+1wAd3/LzPYndO59SVT5cwkhGfGXaP/V3o9Eo370Bu5w9+VxqyaZ2QJC0uSvKTlbEamQ6UdMEamOmZ0KnOHue2U6FhEREWl6oiTFg+7ePdOxiEh6qbmHiIiIiIiIiGQFJSlEREREREREJCuouYeIiIiIiIiIZAXVpBARERERERGRrKAkhYiIiIiIiIhkhUYzBGnnzp29d+/emQ5DREQk68yYMWOFu3fJdByZYmajgNuAHOAf7n5DwvqzgHMJwy9/C5zp7nPMrDfwCTAvKvqeu59V1bF0PyIiIlKxZO9HGk2Sonfv3kyfPj3TYYiIiGQdM/s80zFkipnlAHcAPwWWANPMbJK7z4kr9h93/3tUfjRwMzAqWveZuw9L9ni6HxEREalYsvcjau4hIiIijVkRsMDdF7r7FmA8cHh8AXdfGzfbGlCv4iIiIhmiJIWIiIg0Zt2AxXHzS6Jl32Nm55rZZ8CfgPPiVhWY2Uwze93M9k5vqCIiIqIkhYiIiDRmVsGyH9SUcPc73L0vcDFwebT4K6Cnu+8KXAD8x8za/eAAZmea2XQzm758+fIUhi4iItL0KEkhIiIijdkSoEfcfHdgaRXlxwNHALj7ZndfGb2eAXwG7JS4gbvf7e6F7l7YpUuT7Z9UREQkJZSkSKP1m0u5+YV5bNhSmulQREREmqppQD8zKzCzFsAYYFJ8ATPrFzd7CDA/Wt4l6ngTM+sD9AMW1kvUIiIitXHnnTBjRqajqJNGM7pHNnr246+5/ZUF9O3ahsOH/aD5q4iIiKSZu5ea2TjgecIQpPe6+2wzuxqY7u6TgHFmdgCwFVgFnBJtvg9wtZmVEoYnPcvdS+r/LERERJKweDGcey4ceCA8/3ymo6k1JSnSaMrCleHf4hIlKURERDLE3ScDkxOWXRH3+vxKtnsMeCy90YmIiKTIo4+Gf19+GZYtg65dMxtPLam5RxpNXRR+bJlarB9dREREREREJI0mTIBtt4WysvC6gVKSIk2+XrOJz1duYIf2eSxY9i0rvt2c6ZBERERERESkMVq8GN59F847DwYPhoceynREtaYkRZpMKQ5NPc7aty8A01SbQkRERERERNLhsah14rHHwtix8Pbb8MUXmY2plpSkSJMpxSW0admc4wp7sE1uDlOUpBAREREREZF0mDABdtkF+vWDMWPCsvHjMxtTLSlJkSZTFq6ksHdH8nJz2K1XB/VLISIiIiIiIqm3ZAm8806oRQHQpw+MGNFgm3woSZEGK77dzGfL1zOioBMARb078cnXa1mzcWuGIxMREREREZFGJb6pR8zYsfDBBzB3bmZiqgMlKdIgVmtiRJ98AIoK8nGHGZ+rNoWIiIiIiIik0KOPwpAhsNNO/1123HHQrFmDrE2hJEUaTC0uYZvcHIZ0aw/Arj07kJtj6pdCREREREREUmfp0tBJZnwtCoDtt4d99w1JCveMhFZbSlKkwXsLVzK8V0dyc8LlzcvNYZfu6pdCREREREREUuixx0ISIjFJAaHJx/z58P779R9XHaQ1SWFmo8xsnpktMLNLKlh/lpl9ZGYfmNlbZjYwbt2l0XbzzOygdMaZSqs3bGHeN+soKsj/3vIRffL5aMkaNmwpzVBkIiIiIiIi0qhMmACDB8POO/9w3VFHQW5ug2vykbYkhZnlAHcABwMDgbHxSYjIf9x9iLsPA/4E3BxtOxAYAwwCRgF3RvvLetMWrcIdRiQkKYoKOlFa7sz8YnWGIhMREREREZFG46uv4K23Kq5FAZCfDwcdBA8/DOXl9RtbHaSzJkURsMDdF7r7FmA8cHh8AXdfGzfbGog1ljkcGO/um929GFgQ7S/rTVm4khbNm7FLjw7fWz68V0eaWVgvIiIiIiIiUidVNfWIGTs2DFH69tv1F1cdpTNJ0Q1YHDe/JFr2PWZ2rpl9RqhJcV4Ntz3TzKab2fTly5enLPC6mLqohGE9OpCX+/2KH21aNmdwt/bqPFNERERERETqbsIEGDQIBgyovMzo0bDNNg2qyUc6kxRWwbIfdCvq7ne4e1/gYuDyGm57t7sXunthly5d6hRsKqzbtJWPv1zDyISmHjFFvfOZuXg1m0vL6jkyERERERERaTS++grefLPqWhQAbdqERMWECbB1a/3EVkfpTFIsAXrEzXcHllZRfjxwRC23zQozPl9FuYf+JypSVJDPltJyPlyypp4jExERERERkUbj8cerb+oRM3YsrFgBL7+c/rhSIJ1JimlAPzMrMLMWhI4wJ8UXMLN+cbOHAPOj15OAMWbW0swKgH7A1DTGmhJTikto3szYrVeHCtfv3jvUsNBQpCIiIiIiIlJrEybAwIFhqs6oUdChQ4Np8pG2JIW7lwLjgOeBT4BH3H22mV1tZqOjYuPMbLaZfQBcAJwSbTsbeASYAzwHnOvuWd9GYsrClQzt3p5WLZpXuL5j6xb037at+qUQERGpR01xSHQREWnEvv4a3ngDjjkmufItW4bhSCdOhI0b0xtbCqSzJgXuPtndd3L3vu5+XbTsCnefFL0+390Hufswd98vSk7Etr0u2q6/uz+bzjhTYeOWMj5csqbSph4xRQX5zFhUQmlZwxkCRkREpKFqqkOii4hII1aTph4xY8fCunUweXL64kqRtCYpmpL3v1hFabkzok/FnWbGFBXks35LGXO+WltlOREREUmJJjkkuoiINGITJsDOO4eRPZK1336w7bYNosmHkhQpMqW4hGYGhb06VlmuqED9UoiIiNSjtA+JLiIiUm+++SY09Tj2WLCKBsWsRE4OHHccPP00rM3uH8yVpEiRKQtXMmiH9rTNy62y3Lbt8ujdqZX6pRAREakfaR8S3czONLPpZjZ9+fLldQpWRESkShMnQnl5zZp6xIwdC5tMGmLvAAAgAElEQVQ3wxNPpD6uFFKSIgU2bS1j5uLVjCiouqlHzIiCTkxbVEJ5+Q/uc0RERCS10j4kurvf7e6F7l7YpUuXOoYrIiJShQkToH9/GDy45tuOHAm9e2d9kw8lKVLgwyVr2FJa/l1TjuoUFeSzesNWPl22Ls2RiYiINHlNbkh0ERFppJYtg9deq3lTjxgzGDMGXnwRsrjmn5IUKTBl4UqAGiUpQP1SiIiIpFtTHBJdREQaqbo09YgZMwbKyuCxx1IXV4o1z3QAjcHURSXsvF1bOrRqkVT57h23YYf2eUwpLuHkPXqnNzgREZEmzt0nA5MTll0R9/r8Kra9DrgufdGJiIgkacIE2GknGDKk9vsYOhQGDAhNPs46K3WxpZBqUtTR1rJyZny+Kun+KADMjKKCfKYWl+CufilERERERESkCsuXw6uv1r6pR4xZ6EDzzTdhyZLUxZdCSlLU0UdfrmHDljJG9OlUo+2KCjqxfN1mFq3ckKbIREREREREpFGINfU45pi672vsWHCHhx+u+77SQEmKOor1K7F77+RrUkB8vxQrUx6TiIiIiIiINCITJsCOO8Iuu9R9XzvuCIWFWTvKh5IUdTRl4Ur6dmlNl7Yta7Rd3y6t6dS6BVPUeaaIiIiIiIhUZsWK1DT1iDd2LMyYAfPnV1+2nilJUQdl5c70RasoKqhZUw/4fr8UIiIiIiIiIhWaODGMyFGXUT0SHX98SHhkYW0KJSnq4JOv1rJucykj+9SsqUdMUUE+S1Zt5MvVG1McmYiIiIiIiDQKEyZA374wbFjq9tmtG+yzT0hSZNlgDkpS1EGsqUZRDUb2iBfbbppqU4iIiIiIiEiiFSvglVdS29QjZuxYmDsXZs1K7X7rSEmKOpiycCU981uxffttarX9ztu1o21ec/VLISIiIiIiIj/05JOpb+oRc8wx0Lx51jX5UJKilsrLnamLShhRy1oUADnNjKLe+UzRCB8iIiIiIiKSaMIE6NMHdt019fvu1AkOPBDGjw/Dm2aJtCYpzGyUmc0zswVmdkkF6y8wszlm9qGZvWxmveLWlZnZB9E0KZ1x1sb8Zd+yesPWWjf1iCkqyGfh8vUsX7c5RZGJiIiIiIhIg1dSAi+/nJ6mHjFjx8IXX8C776Zn/7WQtiSFmeUAdwAHAwOBsWY2MKHYTKDQ3YcCjwJ/ilu30d2HRdPodMVZW7HaDyP71Hxkj3jf9UuxSE0+REREREREJPLEE1Bamp6mHjGHHw55eaE2RZZIZ02KImCBuy909y3AeODw+ALu/qq7b4hm3wO6pzGelJpSXML27fPo3rF2/VHEDO7Wnm1yczQUqYiIiIiIiPzXhAnQuzfstlv6jtG2LRx2GDzySEiIZIF0Jim6AYvj5pdEyyrzC+DZuPk8M5tuZu+Z2RHpCLC23J0pC0N/FFbHaje5Oc0Y3qujOs8UERERERGRoKQEXnopvU09YsaMgWXL4NVX03ucJKUzSVHRlaxwAFYzOwkoBG6MW9zT3QuBE4BbzaxvBdudGSUypi9fvjwVMSdl4Yr1rPh2MyPq2NQjpqggn7lfr2XNhq0p2Z+IiIiIiIg0YE8+mf6mHjE/+xm0a5c1o3ykM0mxBOgRN98dWJpYyMwOAC4DRrv7d71HuvvS6N+FwGvAD7ozdfe73b3Q3Qu7dOmS2uirEGuaUddOM2OKCvJxh+mfqzaFiIiIiIhIkxdr6lFYmP5j5eXBkUfC44/D5swP6JDOJMU0oJ+ZFZhZC2AM8L1ROsxsV+AuQoJiWdzyjmbWMnrdGfgRMCeNsdbIlIUr6dymJX06t07J/ob16ECLnGbql0JERERERKSpW7UqNPU45pj0N/WIGTsW1qyBZ5+tvmyapS1J4e6lwDjgeeAT4BF3n21mV5tZbLSOG4E2wISEoUYHANPNbBbwKnCDu2dFksLdmVKcmv4oYvJyc9ilR3v1SyEiIiIiItLUPfkkbN1aP009Yn7yE+jSJSuafDRP587dfTIwOWHZFXGvD6hku3eAIemMrbaWrNrIV2s2MaJPapp6xBQV5HPX6wtZv7mU1i3T+raIiIg0KWY2CrgNyAH+4e43JKy/ADgDKAWWA6e7++fRujLgo6joF9k4LLqIiDQyEyZAr16w++71d8zmzUNS5L774NtvoU2b+jt2gnQ292iU3lu4EoARBanpNDOmqKATpeXO+1+sSul+RUREmjIzywHuAA4GBgJjzWxgQrGZQKG7DwUeBf4Ut26juw+LJiUoREQkvVavhhdfrN+mHjFjx8LGjaEmRwYpSVFDU4tL6NAql35dU5tZGt6rIznNTP1SiIiIpFYRsMDdF7r7FmA8cHh8AXd/1d03RLPvETr7FhERqX+ZaOoRs+ee0KNHxpt8KElRQ1OKSyjqnU+zZqnNarVp2ZzBO7RTvxQiIiKp1Q1YHDe/JFpWmV8A8b2G5UXDnb9nZkdUtEGmhkQXEZFG6NFHQ6KgqKj+j92sGYwZA88/DytX1v/xY2Fk7MgN0FdrNvJFyYaUDT2aqKggnw8Wr2bT1rK07F9ERKQJquhXBa+woNlJQCGhY++Ynu5eCJwA3GpmfX+wswwNiS4iIo3MmjXwwguZaeoRM3YslJbCY49l5vgoSVEjsaYYI/uktj+KmKKCTmwpLefDJWvSsn8REZEmaAnQI26+O7A0sZCZHQBcRhgW/btB4t19afTvQuA1YNd0BisiIk3YpEmwZUtmmnrEDBsG/fvD+PEZCyGpJIWZDU53IA3BewtLaNuyOQO2b5eW/e/euyMAU4szV7VGREQkW9XyfmQa0M/MCsysBTAGmBRfwMx2Be4iJCiWxS3vaGYto9edgR8BWTEkuoiINEITJkD37jBiROZiMINzzoGhQ6G8PCMhJDvW5d+j/9jvB/7j7qvTF1L2mlq8ksLeoYPLdOjQqgU7b9eWKcUljEvLEURERBq0Gt+PuHupmY0DnicMQXqvu882s6uB6e4+idC8ow0wwUL12thQowOAu8ysnPDDzg3uriSFiIik3po1oS+Ic84JfUNk0nnnZfTwSSUp3H0vM+sHnA5MN7OpwH3u/mJao8siy9dt5rPl6zm2sEf1heugqCCfR2csobSsnOY5ao0jIiISU9v7EXefDExOWHZF3OsDKtnuHWBInQMXERGpzkUXhaYeJ56Y6UgyLumnYHefD1wOXAz8GLjdzOaa2VHpCi6bTFsU+qNIV6eZMUUF+WzYUsbspWvTehwREZGGqKnfj4iISCP00ENw991w8cVQWJjpaDIu2T4phprZLcAnwP7AYe4+IHp9SxrjyxpTFq6kVYschnRrn9bjFPUOSZCpGopURETke3Q/IiIijc6nn8KZZ8Kee8I112Q6mqyQbE2KvwLvA7u4+7nu/j581+P15ekKLptMKS5heK+O5Ka5CUbXdnkUdG7NFCUpREREEjX5+xEREWlENm2C446DFi3CaBq5uZmOKCsk23Hmz4CN7l4GYGbNgDx33+DuD6QtuiyxesMW5n2zjkOGbF8vxyvqnc9zs7+mvNxplqZOOkVERBqgJn0/IiIijcwFF8CsWfDUU9AjvX0fNiTJVgt4Cdgmbr5VtKxJmFpcgjuM6NOpXo5XVJDPmo1bmffNuno5noiISAPRpO9HRESkEXnkEfjb3+DCC+HQQzMdTVZJNkmR5+7fxmai163SE1L2mVpcQovmzRjaPb39UcSM6JPafik2l5bxp+fmcs3Tc3D3lOxTREQkA5r0/YiIiDQSCxbAGWfAyJFw/fWZjibrJJukWG9mu8VmzGw4sDE9IWWfKcUl7NqjA3m5OfVyvO4dW9GtwzYpSVJ8tvxbjrrzHe587TP++VYxf399YQoiFBERyYgmfT8iIiKNwObNcPzx0Ly5+qGoRLJ9UvwGmGBmS6P57YHj0xNSdlm3aSuzl65h3P796vW4RQX5vDl/Be6OWc37pXB3Hp62mKuemkNebjPuObmQJz/4khufn8uQbu3Zq1/nNEQtIiKSVk32fkRERBqJCy+E99+HJ56AXr0yHU1WSipJ4e7TzGxnoD9gwFx335rWyLLE9M9XUe4woiC/Xo9bVJDPxJlfUrxiPX26tKnRtms2bOXSiR8y+aOv+dGOnbj5uGFs2y6PPft24tNv1vHrh97nqV/vRfeOqiErIiINR1O+HxERkUbg0Ufhr3+F3/4WDj8809FkrZqMp7k7MBTYFRhrZidXt4GZjTKzeWa2wMwuqWD9BWY2x8w+NLOXzaxX3LpTzGx+NJ1SgzhTasrCEpo3M3br2bFej1tUULt+KaYtKuHg297ghdnfcPGonXng9BFs2y4PgNYtm3PXzwspLXPOenAGm7aWpTxuERGRNKvx/YiIiEjGLVwIv/gFFBXBDTdkOpqsllSSwsweAG4C9iLcHOwOFFazTQ5wB3AwMJBwIzEwodhMoNDdhwKPAn+Kts0Hfg+MAIqA35tZ/WYJIlOLVzK0e3u2aVE//VHE9Oncms5tWiSdpCgtK+eWFz/l+LveJbd5Mx47e0/O3rfvD4YwLejcmluOH8bHX67l8ic+VkeaIiLSYNTmfkRERCTjYv1QmIV+KFq0yHREWS3ZPikKgYFesyfaImCBuy8EMLPxwOHAnFgBd381rvx7wEnR64OAF929JNr2RWAU8FANjl9nG7aU8uGSNfxynz71eVgAzIyignymJJGkWLJqA78Z/wHTP1/FUbt14+rDB9OmZeVv7QEDt+W8/Xfk9lcWMKxHB04aqbZQIiLSINTmfkRERCSzLr4Ypk+Hxx+HgoJMR5P1km3u8TGwXQ333Q1YHDe/JFpWmV8Az9Zy27SY+cVqSsu93vujiCnqnc+XqzeyZNWGSss8NWspB9/2JvO+XsdtY4Zx83HDqkxQxJx/wE7s278LVz01m/e/WJXKsEVERNKlNvcjIiIimTNxItx2G5x3Hhx5ZKajaRCSrUnRGZhjZlOBzbGF7j66im0qGpKiwl8+zOwkwq8jP67JtmZ2JnAmQM+ePasIpXamLFxJM4PhvTLS0oSigk5A6JcisZPL9ZtLuXLSbCbMWMKuPTtw+5hd6ZGffEeYOc2MW48fxui/vs3ZD87g6V/vTZe2LVMav4iISIrV5n5EREQkMxYtgtNPh+HD4U9/ynQ0DUaySYora7HvJUCPuPnuwNLEQmZ2AHAZ8GN33xy37b4J276WuK273w3cDVBYWJjyqp/vFZcwuFt72uZlZuza/tu1pV1ec6YWl3DUbt2/W/7RkjWcN34mi1auZ9x+O3L+Af3IzalJH6hBh1Yt+PtJwznqb29z7n/e599njKjVfkREROrJlZkOQEREJClbtoR+KMrL4ZFHoKV+EE5WUk+k7v46sAjIjV5PA96vZrNpQD8zKzCzFsAYYFJ8ATPbFbgLGO3uy+JWPQ8caGYdow4zD4yW1ZtNW8v4YPFqinpnpqkHhNoOu/fO/67zzPJy5+43PuOov73Npq1lPPTLkVx4UP86JRYG7tCOG44aytTiEm54dm6qQhcREUm5Wt6PiIiI1L9LL4WpU+Gf/4Q+9d/HYUOW7OgevySMvnFXtKgb8ERV27h7KTCOkFz4BHjE3Web2dVmFquWeSPQBphgZh+Y2aRo2xLgGsLNxzTg6lgnmvVl1uLVbCktZ0SfTvV52B8oKshn4Yr1zF66hlPum8r1k+ey/85defb8vRmZotiO2LUbp+7Zm3++VcyTH3yZkn2KiIikWm3uR6LtGvyQ6CIi0oBMmgQ33wznngvHHJPpaBqcZJt7nEsYrWMKgLvPN7Ou1W3k7pOByQnLroh7fUAV294L3JtkfCk3tbgEM9i9d2b6o4iJJUmOuONtcpoZ1x05mBOKemJWUbcdtXfZIQOYs3QtFz/2ITtt25YB27dL6f5FRERSoMb3I3FDov+U0Jx0mplNcvc5ccViQ6JvMLOzCUOiHx83JHohoW+sGdG26nFaREQq9vnncOqpsOuucNNNmY6mQUq2ncBmd98SmzGz5lTSCWZjsWPXNpyyR286tMrsGLaDdmhH5zYt6dulDU+N24sTR/RKeYICIDenGX89cVfa5eVy1oMzWLNxa8qPISIiUke1uR/5bkj0aNvYkOjfcfdX3T02lNZ7hL6wIG5I9CgxERsSXURE5Ie2boUxY6C0NPRDkZeX6YgapGSTFK+b2e+Abczsp8AE4Kn0hZV5Bw/ZnitHD8p0GOTmNOOVC3/M07/ei37btk3rsbq2zeNvJ+3G0tUb+e3DH1Be3qjzUCIi0vDU5n4k7UOim9mZZjbdzKYvX768mnBERKTRuuwyeO89uOce2HHHTEfTYCWbpLgEWA58BPyK0ITj8nQFJd/XLi+X5vU06sbwXvlccehAXpm7jNtfmV8vxxQREUlSbe5HajMk+o012dbd73b3Qncv7NKlSzXhiIhIo/TMM3DjjXDWWWFUD6m1pPqkcPdy4J5okkbupJG9+GDxGm59aT5Du7dn/523zXRIIiIitb0fSfuQ6CIi0sQVF8PJJ8Muu8Att2Q6mgYv2dE9is1sYeKU7uAkM8xCB52DdmjHb8Z/wKIV6zMdkoiISG3vRxr0kOgiIpLFVq6Eiy+GQYNCfxTqhyIlkm1DUAjsHk17A7cDD6YrKMm8vNwc/n7ScJo1M856cAYbtpRmOiQREZEa34809CHRRUQkC61dC1ddBX36hCYeRx0FM2fCTjtlOrJGwdxr1zmimb3l7nulOJ5aKyws9OnTp2c6jEbnjU+Xc8p9Uzls6A7cNmZYWkYWERGR9DKzGe5emOk40kH3IyIiUm82bIA77oA//jHUojjySLj6ahg8ONORNQjJ3o8k1SeFme0WN9uM8EtGeoeakKywz05duPDA/tz4/Dx26dGBX+xVkOmQRESkidL9iIiIZMSWLWHEjuuug6++goMOgmuvhcJGmf/PuKSSFMCf416XAouA41IejWSlc/bty6zFq7l+8icM2qEdI/t0ynRIIiLSNOl+RERE6k9pKTzwQGja8fnnsPfeMH487LNPpiNr1JId3WO/dAci2cvM+PNxu3D4X9/m3H+/z3VHDuGgQduq6YeIiNQr3Y+IiEi9KC+HRx+FK66AefNg+HC46y448EDQM1DaJdvc44Kq1rv7zakJR7JV27xc7jmlkLMfnMFZD85gjz6duOKwgQzYvl2mQxMRkSZC9yMiIpJW7vDMM3D55TBrVhi14/HH4YgjlJyoRzUZ3eNsoFs0nQUMJLQDVVvQJqJvlzZMPm9vrjl8EJ98vZZDbn+TyyZ+xMpvN1e/sYiISN3pfkRERNLjlVdgzz3hsMNg3Tp48MGQqDjySCUo6lmyfVJ0BnZz93UAZnYlMMHdz0hXYJKdmuc04+d79OawXXbg1pfm88B7nzNp1lJ+c8BOnLxHL3Jzks17iYiI1JjuR0REJLXefx/+539CkqJ799Cs47TTIDc305E1Wck+UfYEtsTNbwF6pzwaaTA6tGrBlaMH8dz5ezOsRweueXoOo259g1fnLct0aCIi0njpfkRERFLn009h333ho4/glltg/nw480wlKDIs2ZoUDwBTzWwi4MCRwP+lLSppMPpt25b/O72IV+Yu49pnPuG0+6axb/8uXH7IQHbs2ibT4YmISOOi+xEREUmNjRvh2GNDQmL6dOjZM9MRSSTZ0T2uM7Nngb2jRae5+8z0hSUNiZnxkwHbsne/Lvzfu4u47aX5jLr1DU7eozfnH9CP9tsoEykiInWn+xEREUmZcePgww9h8mQlKLJMTToQaAWsdffbgCVmVpCmmKSBatG8GWfs3YdX/2dfji3szn3vFLPfTa/x7ymfU1bumQ5PREQaB92PiIhI3dx/P9x7L1x2GRx8cKajkQRJJSnM7PfAxcCl0aJc4MEkthtlZvPMbIGZXVLB+n3M7H0zKzWzYxLWlZnZB9E0KZk4JTt0btOSPxw1lKfG7cWOXdtw2cSPOeT2N3nnsxWZDk1ERBqw2t6PiIiIfOejj+Ccc2C//eCqqzIdjVQg2ZoURwKjgfUA7r6Uaob6MrMc4A7gYMLwYGPNbGBCsS+AU4H/VLCLje4+LJpGJxmnZJHB3drz8JkjufPE3Vi3qZQT7pnCWQ/M4IuVGzIdmoiINEw1vh8RERH5zrp1oR+K9u3hP/+BnJxMRyQVSDZJscXdndBJFWbWOoltioAF7r7Q3bcA44HD4wu4+yJ3/xAor0HM0oCYGT8bsj0v/78fc+GBO/H6p8s54ObXeWnON5kOTUREGp7a3I+IiIiAO/zyl2EEj/HjYbvtMh2RVCLZJMUjZnYX0MHMfgm8BNxTzTbdgMVx80uiZcnKM7PpZvaemR1RUQEzOzMqM3358uU12LXUt7zcHMbt349XL9yXHbu24ZLHP2L1hi3VbygiIvJftbkfERERgTvvhIcfhuuugx//ONPRSBWSSlK4+03Ao8BjQH/gCnf/SzWbWUW7qkFsPd29EDgBuNXM+lYQ193uXujuhV26dKnBriVTtmufx03H7sLqDVu4+uk5mQ5HREQakFrej4iISFM3bRr89rdw6KFw0UWZjkaqUe0QpFHfEs+7+wHAizXY9xKgR9x8d2BpshtH7Uxx94Vm9hqwK/BZDY4vWWrgDu04e9++/OWVBRy2yw7s179rpkMSEZEsV4f7ERERacpKSkI/FDvsAP/6FzSryQCXkgnVvkPuXgZsMLP2Ndz3NKCfmRWYWQtgDJDUKB1m1tHMWkavOwM/AvSzeyMybv8d6de1DZc9/hHrNm3NdDgiIpLl6nA/otHGRESaqvJyOOUUWLoUHnkE8vMzHZEkodqaFJFNwEdm9iJRj9oA7n5eZRu4e6mZjQOeB3KAe919tpldDUx390lmtjswEegIHGZmV7n7IGAAcJeZlRMSKTe4u5IUjUjL5jn88ZihHP23d/jjc3O59oghmQ5JRESyX43vR+JGG/spoZbnNDOblHBfERtt7MIKdrHR3YelIHYREalvN90ETz8Nt98ORUWZjkaSlGyS4ploqhF3nwxMTlh2RdzraYRmIInbvQPoqbWR261nR07/UQH/fKuYQ4fuwMg+nTIdkoiIZLfa3I98N9oYgJnFRhv7Lknh7ouidRptTESksXjjDfjd70JTj3HjMh2N1ECVSQoz6+nuX7j7v+orIGlaLjywPy/O+YZLHvuQ536zD3m5GqtYRES+r473IxWNNjaiBtvnmdl0oJRQs/OJCuI7EzgToGfPnrUIUUREUuqbb2DMGOjTB/7xD7CKxnSQbFVdnxTf/UdsZo+lORZpgrZpkcMNRw9h0coN3PLip5kOR0REslNd7kc02piISFNSVgYnnACrVsGjj0K7dpmOSGqouiRF/H/sfdIZiDRde/btzNiiHtzz5kJmLV6d6XBERCT71OV+JGWjjQGvEUYbExGRbHXVVfDKK3DnnTB0aKajkVqoLknhlbwWSalLfzaArm3zuPixD9lSqibBIiLyPXW5H9FoYyIiTcXzz8O118Jpp4VJGqTqkhS7mNlaM1sHDI1erzWzdWa2tj4ClKahXV4u1x05mLlfr+PO1xZkOhwREckutb4fcfdSIDba2CfAI7HRxsxsNICZ7W5mS4BjCaOLzY42HwBMN7NZwKtotDERkey1eDGceCIMHgx//Wumo5E6qLLjTHdXL4ZSb34yYFsOH7YDd7y6gIMHb0//7dpmOiQREckCdb0f0WhjIiKN3NatcPzxsGVL6IeiVatMRyR1UF1NCpF69fvDBtEuL5eLHp1FaZmafYiIiIiISDUuuQTefTeM5LHTTpmORupISQrJKvmtW3Dl6EHMWrKGe98uznQ4IiIiIiKSzSZOhJtvhnHj4LjjMh2NpICSFJJ1Dh26PT8duC1/fuFTilesz3Q4IiIiIiKSjT77DE49FXbfHW66KdPRSIooSSFZx8y49ojBtGjejIsf+5Dycg0sIyIiIiIicYqL4eijIScHJkyAli0zHZGkiJIUkpW2bZfH5YcMYGpxCf+Z+kWmwxERERERkWywdi1ceikMGADz58O//w29emU6KkkhJSkkax1X2IO9duzMDc/OZenqjZkOR0REREREMqWsDO65B/r1gxtugDFj4NNP4eCDMx2ZpJiSFJK1zIw/HDWEsnLndxM/wj21zT42bS1j2dpNKd2niIiIiIik2CuvwG67wZlnhtE7pk2D+++Hbt0yHZmkgZIUktV65LfiolH9eW3ecibO/DIl+5z/zTqufmoOI//wMj/64ytMmrU0JfsVEREREWkSysvr5zjz58Phh8NPfhKaeTzyCLzxBhQW1s/xJSOaZzoAkeqcskdvnv7wK65+eg579+tCl7Y17xRn45YynvnoKx6a+gUzPl9Fbo5x4KDt+GbNJs57aCYr1m3m9L0K0hC9iIiIiEgj8eWXcMwxMGdOaGYxenT4t2PH1B5n1Sq45hr4618hLw/+8Af4zW/Ca2n0lKSQrNesmfHHo4fys9vf5PeTPubOE4cnve3spWsYP3UxT3zwJes2ldKnc2t+97OdOXq37nRq05JNW8s4f/xMrn56DsvWbebiUf0xszSejYiIiIhIA/T++3DYYaFGwxFHwAsvwMMPh9E19t47JCxGj4a+fWt/jK1b4a674MoroaQEzjgjJCu23TZlpyHZT0kKaRB27NqG83/Sjxufn8dzH3/FqMHbV1r2282lPDVrKQ9N/YIPl6yhRfNmHDJke8bs3oOigvzvJSHycnO488Th/O+TH/P31z9j2bpN/PHooeTmqCWUiIiIiAgAEyfCSSfx/9s78/ioqvP/vx+yhyTsS4CETUAWcWERQXDBBfcFEdRWES2ltVZb/bUVa7FaWm2ttXX3q7jVpViruKGICrJDWGVfAwSCLAGykf38/jg3MMTMZO4sySR53q/XfWVm7r2fee7NM3fOfO45z6F1a1iwAPr3t0M+li6Fjz+Gjz6CX//aLn36nDAsBg+2JoY/zLH3qekAACAASURBVJwJ990HGzbAhRfCk0/C6aeH97iUiCSsv8REZJSIbBKRrSLyu2rWjxCRFSJSJiI3VFl3m4hscZbbwhmnUj+YOKIbfTuk8PsP13GksOSkdcYYVu8+wu/eX8PgqbN54H/fUVxawcNX9WHZ5Iv4x9gzOLtbq2p7SUQ1EaZe249fX9yT/63Yw52vZ1BQXFZbh6UoiqIoiqIo1VNaCqtW2VktpkyBrKzafX9j4PHH4frr4bTTrCnRv79d16QJDBkCU6fCd9/Btm3w1FOQmgpPPAFDh9rHEybAhx9CQUH177FuHYwaBZdfbo93xgyYPVsNikaMhHrGhOPCIlHAZuBiIAtYBtxkjFnvsU0XIAW4H/jIGPNf5/WWQAYwEDDAcmCAMeawt/cbOHCgycjICMuxKJHD2j1HuebZBVx7Rkf+fuPpHD1WyoxVe3hn6W42ZOeSEBPFVaenMm5wOmemNXc9dOOdpbt48IPvOK1jM6aNH0SrJPf1LxRFUSINEVlujNEqY7WAtkcURQmYigrYutXOXLFsmTUEVq6EIo/Z6JKTrWnw059akyCclJTY93ntNTvd57RpkJDg375HjsDnn9seFjNn2udxcXDRRbaHxZVXQkyMNV5eegmSkuzju+6C2NiwHpZSd/jbHgnncI/BwFZjzHYnoHeBa4DjJoUxJtNZV7U87KXAl8aYHGf9l8Ao4J0wxqvUA/p1bMak87rx7DfbOHqslPlbD1BUWkG/jilMva4fV5/egeT4mID1bxqcTqumsdz9zkpueGERb0wYTFrLxBAegaIoiqIoitLoMcYWoaw0JCqXo0ft+oQEO+Xmz34GgwbZBWDSJPj5z+Htt23vilNPDU98Bw/C6NF2Jo2HH4Y//AHc3Pxr3twaG+PG2d4R8+dbw2LGDPj0U7tNfLxdN2mSfY/WrcNxJEo9JJwmRUdgt8fzLODsIPb9wSS4IjIRmAiQnp4eWJRKvePuC3swe/1+Fm8/xOizOnHT4HT6dWwWMv1L+rbnrTvP5o7XM7j++YW8dvsg+nYInb6iKIqiKIrSyDh0CDIyTvSQWLYM9u2z66Kj7VCKceNOGBJ9+tjXq/Lll7Znw3332eEQDz0Ev/lNaHsfbNxoezpkZVkz5KabgtOLiYELLrDLk0/amUE+/hh27IB77rHHqigehNOkqM5q83dsiV/7GmNeAl4C273S/9CU+kx8TBQf3jUMEfs4HAzs0pL/TjqHW6ctZeyLi3npxwMYeoq6u4qiKIqiKIoLysttT4GXXz7xWq9edtjD4MHWkDj9dP+HUYjA7bfbaT/vuceaFNOnW/3Bg4OPd/ZsO8VoXBzMmWNrToQSEejb1y6K4oVwDmTKAtI8nncC9tbCvkojICE2KmwGRSU92iXzv58PpUPzeMa/uoxP1mgKKoqi1Ee0kLeiKHVCWRnceqs1EO66C776ytZm2LgR3nwT7r7bmgD+GhSetG9vp/+cMcNO1TlkCPzqV5CfH3i8L7xgC1impdneHqE2KBTFT8JpUiwDeohIVxGJBcYBH/m57xfAJSLSQkRaAJc4rylKrZLaLIH3fjqU09Oacfc7K3l1wY66DklRFEVxgVPI+1ngMqAPcJOIVO1bvAsYD7xdZd+WwBTscNXBwBSnXaIoiuKb0lK45RY7XOLPf4ZnnrHTajYL8RDiq6+2s2NMmmRn1ujXD75w+bOpvBzuvdfWvxg1ChYuhM6dQxunorggbCaFMaYM+AXWXNgATDfGrBORR0TkagARGSQiWcAY4EURWefsmwM8ijU6lgGPVBbRVJTaplliDG/ecTYX927HHz9ez+OfbyRcs+IoiqIoIed4IW9jTAlQWcj7OMaYTGPMGsBrIW9nhrHKQt6KoijeKSmx9SWmT7dTcT7wQHjfr1kzeO45mDfPFqMcNcr24Dh4sOZ9c3Ot0fHPf9qeGDNm2BlEFKUOCWdNCowxnwGfVXntDx6Pl2GHclS37zRgWjjjUxR/iY+J4vkfDeChGWt5fs429ucW89jo04iJCsznKyotZ9O+PDZk57IhO5eN+/IorzAkx0eTkhBj/8bHkBwfQ0pCtP0bb/82O/48hviYJq6nWVUURWlkhL2Qt6IoynGKi2HMGFsY8qmnbN2I2uLcc2HVKpg6FR57zE79+c9/2sKX1bUXMzPhqqtgwwY71OOnP629WBXFB2E1KRSlIRHVRJh6bT/aJsfx1OwtHCoo5rlbziIx1vvHyBjD/rxi1jtmxIbsPNbvPcqOgwVUOJ0xmsZG0at9MgmxURzML2HHwQJyi8rIPVZKWYXvHhvRTeQkU6NNchw92iXRs20yvdonc0rbpLDX7lAURYlwwl7IW2cbUxQFgKIiO23nZ5/Bs8/aqUJrm/h4ePRRuPFGuPNOO+Tkrbfg+efB8/q0aBFce601VT7/3BbyVJQIQU0KRXGBiHDvRT1pmxzP7z/8jpv+bwmvjh9Ey6axlJRVsO1APuv3OobEPmtK5BSUHN+/Y/MEeqemcMVpqfTpkELv1BTSWiTSpMkP28HGGIpKK8grKiW3qPS4cZFXVEZukfO3yvO9R44xf8tBSsptj+UmAp1bNaVnuyR6tUumRztrXnRt3TTgXiCKoij1jGALeZ9fZd85VTfS2cYURaGw0P7onz0bXnoJfvKTuo3ntNNsbYlnnoHJk+00n3/5izVO/vMfmDABOnWCuXPh1FPrNlZFqYI0lLH1AwcONBkZGXUdhtKI+GLdPn75zkpaJ8WRkhDD1v15lJbbz1NsdBN6tUumT2oKvVOT6Z2awqmpKTRLiAl7XGXlFWQeKmTz93ls2pfH5u/t4tl7IyZK6NY6iZ7tk+nVLsmaF+2SSWuZSFQ1homiKPUbEVlujBlY13HUBSISDWwGRgJ7sLWubjbGrKtm29eAT4wx/3WetwSWA2c5m6wABviqk6XtEUVphBQU2GETc+bAtGkwfnxdR3QymZm2sOYXX0DPnrB5M4wYAf/7H7RqVdfRKY0If9sjalIoShAsy8zh0U/W0yIxlt6OIdEnNYWurZsSHWE9FYpKy9l+oMCaF9/nsXlfHpv357E759jxbeJjmtCjbfLx4+idmkLvDimkxIffXFEUJXw0ZpMCQEQuB54CooBpxpipIvIIkGGM+UhEBgEfAC2AImCfMaavs+8EYLIjNdUY86qv99L2iKI0MvLy4MorYf58eOMNO7wiEjHGDvu4/34b73PPQWxsXUelNDLUpFAUxS8KisvYsj+fzfusebFpXx7rs3NPGqaS1jKB3u1Tjg9R6ZOaQqcWCSEp2llQXMaunEK7HLJ/d+YUsjunkLbJcdw5vBsjT21b7ZAYpX6TffQYGZmHaRoXRdPYaJrGRZMUd+KvFoYNHY3dpKhNtD2iKI2I3Fy47DJYssQaAGPH1nVENWNM9UU0FaUW8Lc9ojUpFKWR0zQumjPSmnNGWvPjr3kW/Fy/N/d44c8vN3xPpa+ZHB993LDok2oNjB7tkoiLPrlQZ6XWrpxCdh6qNCMKjhsTB/NLTtq+WUIM6S0T6Z2azOrdR/nJGxn0aJvExBHduOaMjsRGR1YPFSUwsg4Xct1zCzmQV+x1m6gmQmJs1HHjwpoX1tDwfK19Shw3DEwjKU6/0hSlUVBWBjk50LZtXUeiNGaOHIFLL4UVK2yNh9Gj6zoi/1CDQqkHaE8KRVH8prCk7HhPiw2OgbFxXx6FJeWAnW2ke5skerVPprCkjJ2HCtl9uJCi0orjGk0EUpsl0LlVIuktE0l3/nZu2ZT0lok0SzwxtKS0vIJP12TzwtxtbNyXR2qzeO44tyvjBqfrD9J6TG5RKTc8v5Dso0W8+KMBJMZFU1BcRl5RGQXFZRSUlJFf7DwuLj/++AevlZSRX1RGWYWhdVIsv7q4J2MHpkXcUKtIQHtS1B7aHgkzhYV27P/8+fD00zBxYl1H1LDYuNHOAJGYWNeRRDY5OXDJJbBmDbz3HlxzTV1HpCj1Ah3uoShKrVBRYdiZU3h8VpP12bls2pdHcny0NR+OmxHWhOjYPMF1bwhjDHM2H+CFOdtYsiOHlPhobj2nC+OHdaF1UlyYjkwJB6XlFdz+6jIWbz/EGxMGM/SU1kHpGWNYnXWUqZ+uZ1nmYU5pm8Tky0/lgl5tdaiIB2pS1B7aHgkjlcUJ586Fs86CjAw7xeLTT9tpF5Xg+Pvfbb2Cdu3gt7+1hRYTEuo6qsjj4EE7XeeGDbbw5BVX1HVEilJvUJNCUZQGycpdh3lh7jZmrf+e2KgmjBnYiYnDu5PeSu/6RDrGGH77/hqmZ2TxxJjTuWFAp5Bqz1r/PY/N3MiOgwUM7d6KyZf3pl/HZiF7j/qMmhS1h7ZHwoSnQfHGGzBuHEyZAlOnwqBB8P77kJZWs47yQyoq4L774KmnbI+A/Hz46ito3x5+9zvbW0XNCsv+/dag2LIFPvzQDvdQFMVv/G2PaJ9YRVHqFWemt+DFHw9k9q/P49ozOjJ9WRbnP/ENv3h7BWv3HK3r8BQfPPvNVqZnZPHLC08JqUEBICJc2rc9s341gj9e3ZcN2blc9cx8fj19FXuPHKtZQFGUyKWgwM5GUGlQ3HILREXBn/4EH3xghygMGADffFPXkdY/iorgppusQXHvvbZnwOzZ9lz37m1f694d/vUvu21jJjsbzj8ftm6FTz5Rg0JRwoiaFIqi1Eu6t0ni8Rv6M++3F/CT4d2Ys+kAVz49nx+/soQFWw/SUHqJNRRmrNrDE7M2c92ZHfnVxT3D9j4xUU24bWgX5vy/C5g4ohufrMnmgifm8LcvNpJXVBq29w0FFRWGhdsO8vRXW+o6FEWJHCoNim+/hTff/OH0jtdeC8uWQevWcPHF8OSToNd//zh82P7Qnj4dnngC/vEPaOL8NBgxAr7+GubMgZ494Z57rFnxzDON06zYs8caFLt2wcyZMHJkXUekKA0aHe6hKEqD4OixUt5aspNp8zM5mF9M/07N+OmI7ozq154onb60Tlm6I4cfvbyEM9Kb8+Ydg38wA0w42Z1TyBOzNjFj1V5aJ8Vy70U9GTcosopr7j1yjPeXZ/He8ix25RSSEh/NvN9ceFIR2WDR4R61h7ZHQkhBgR3vP2+eNShuvtn7tnl5cPvtdtjH2LHwyivQtGntxVrf2L3bTp25eTO8/rrtTeGLb76xw2vmzYOOHWHyZLjjDohrBHWhdu+GCy6wQz1mzoRhw+o6IkWpt2hNCkVRGiVFpeX8b8UeXvp2G5mHCunSKpGJI7pz/VkdiY+pvR/HnuQVlfLx6mzaJscxsnfjKui4/UA+1z+/kJZNY/nfz4bSPDG2TuJYvfsIUz/bwNIdOXRv05TJl/fmwlPr7n9RXFbO7PX7mZ6xm2+3HMAYGHZKK24cmMalfduHPFfVpKg9Iq49Yoztnr5rl/1xVV8KTHoaFP/+d80/osEe61//an9A9+ljh4Kcckr4Y61vfPedNSjy8uw5uvBC//YzxvaumDIFFiywNUAmT7bmUEMyK7KzYfFiWLTI/s3IgJgY+OILGDKkrqNTlHqNmhSKojRqyisMX6zbxwtzt7Em6yitk+KYcG4XfjSkMynxobtD7Ys9R47x2oIdvLt0N3nFZQAM6NyCyZefyoDOLWslhrrkUH4x1z+/kPyiMj74+bA6L25qjOFLp7jm9oMFnNOtFQ9eUbvFNTdk5zI9YzcfrtzD4cJSOjSL54aBaYwZ0Im0luE7P2pS1B513h6pqID16+3wiMolO9uuS06G666zRScvusj+8IpE8vOtQTF/vv8GhSdffmmPsbwc3npLZ1/wZM4cO0SmaVPbK6B/f/caxtjCmlOmwMKF1qx48EFrVsTWjREdMCUlsHLlyabEzp12XWysnUVmyBDba6Rfv7qNVVEaAGpSKIqiYH+YLtp2iOfnbmPeloMkxUVzy9npTDi3K+1SwnNHcdXuI7w8bzsz1+4D4IrTUrl9WBc27cvjyS83sz+vmMv6tec3o06la+uG2R25qLScm/9vMev25vLOxCGcld6irkM6Tml5Be8s3cVTs7eQU1DC9Wd25P5Le9GheXiq1x89VspHq/fyXsZu1mQdJTaqCZf0bceNA9MYdkrrWhmOpCZF7VHr7ZHycli92hY6/PZb2/Pg0CG7rmNHOO88u3ToYGcjeP99OHIEWrWC0aOtATB8uC1EGQl4GhRvvWXNhkDIzITrr7c/QB9+GB566ES9hcbKf/4Dt95qe5fMnAnp6cHpGWMNoSlT7I/7zp2tWTF+fOQaYFlZJ8yIRYtgxQooLrbr0tLgnHOsKXHOOXDmmQ2rh4iiRABqUiiKolRh7Z6jvPjtdj5ds5foJk247syOTDyvG93bJAWtXV5h79K/Mn87yzIPkxwfzc2D07ltaJeTfvwWlpTx8rwdvDh3G8VlFdxydjq/HNmDVkkNpyFUUWG4+52VfLY2m+duPovLTkut65CqJbeolOfnbOOV+TsA6N0+mQ7NE+jQPIGOHn87tkigRWKMq6EhFRWGxdsPMT1jNzPX7qO4rILeqSmMHdiJa87oSIumtXu3UU2K2iPs7ZGSEli+3BoSc+fabve5uXZd9+624OGIEdaY6NIFquZtcTHMmgXvvgszZthhFampcOON1hA4++wf7lNb5OfD5Zfbu/NvvWVrSwTDsWMwaZKdEeTKK21di+bNQxNrfePJJ+00o8OHW7OqZQh78xljh0JMmQJLl9q8Gz0aEhPtj/z4eLt4Pq76vLp1MTG2Z1B5+Q8Xb69XXVdYaI2ISlNizx4bc3y8nRGm0pQYMsSaeoqihJWIMClEZBTwTyAKeNkY81iV9XHAG8AA4BAw1hiTKSJdgA3AJmfTxcaYSb7eS00KRVH8ZdehQv5v3namZ+ympLyCS/q0Y9J53TkzgLv9BcVlvJexm2kLMtmVU0inFglMGNaVGwelkRQX7XW/A3nF/POrzbyzdDcJMVH87PzuTBjWlYTYCLmbGQSPzdzIC3O3MfnyU5k4ontdh1MjWYcLeWX+Drbuz2fvkWPsOXKMotKKk7aJj2lywrRwDAzP5+2bxRMb3YS9R47x3+VZvLd8N7tzjpEcH821Z3Rk7KA0+nZIqbMaGGpS1B4hb48cOwZLlpwYurFwoX0N7BSR5513wphw+yOroAA+/dQaFp99Zg2MLl2sWTFunB0KUFs5G2qDohJj4Pnn7ewUnTvbGgynnRYa7fpARQXcf7+dueOGG6xRE666JMbA55/DI4/Y3j1FRZEz00rXrid6SAwZAqefXv+GpihKA6DOTQoRiQI2AxcDWcAy4CZjzHqPbX4O9DfGTBKRccB1xpixjknxiTHG78FfalIoiuKWg/nFvLYgkzcWZZJbVMaQbi2ZdF53zuvZpsYfk9lHj/HawkzeWbKL3KIyzkpvzk+Gd+PiPu1czRyx7UA+j8/cyKz139MuJY77Lu7F6AGdQj4EoKy8gtVZR5m/5SCbv89jZO+2XNE/NeQzbby9ZBeTP/iOW85O50/X9quXRUKNMRwuLD1uWOw5fIy9R46x96h9vOdIEQfzi0/aRwRaJ8VxML847EUwA0FNitoj5O2Riy6y4/9F7A+rSlPi3HOhbdvQvc/Ro7ZnxTvv2C785eVw6qknDItevUL3XlXJz7eFHBctgrfftr06Qs2CBTBmjD3OV14JfBhJfaK42A7vmD4dfvlL25uiNof1GANlZdasKCqy8VQ+9ud5aamNt0kT+9fX4m2b2Fjo2xfat6+941YUxSuRYFKcAzxsjLnUef4AgDHmLx7bfOFss0hEooF9QBugM2pSKIpSS+QXl/Hu0l28PG8H+3KL6J2awqTzunHFaak/MBzW7jnKy/O288mabCqM4bJ+qUw4tysDOgdXc2FZZg5//mwDK3cdoVe7ZH53+amc74dZ4g1jDDsPFTJv60HmbznAwm2HyCsqQwRaNbU/plsnxXLT4HRuObsz7ZsFf2dtzqb93PF6BsN7tOblWwdG1DSfoaaotJx9R4usiXHEmhh7Dh+jQ/MEbghzEcxAUJOi9gh5e2TWLDvE49xza2+owsGDtnbFu+/aISXG2PH5Y8fa3g59+4auvkNentVctMgaJGPGhEa3OrKzrf6CBXbow2OPQbT3Hm/1miNHbJHUOXPgb3+zx1sPTWNFURoWkWBS3ACMMsbc6Tz/MXC2MeYXHtusdbbJcp5vA84GkoB12J4YucDvjTHzfL2fmhSKogRLSVkFM1bt4cVvt7N1fz6dWiTwk+HduGFAJxZuO8TL87azZEcOSXHRjB2UxvihXUL6Y9QYw8y1+3j8843sPFTIsFNa8cBl/s8+caSwhAVbDzF/6wHmbTlI1mHbJbxj8wSG92jNuT1aM7R7a5onxDB/60FeX5jJ15v2EyXCpf3aM35oFwZ2bhGQMbJ+by43vriI9JaJTJ90js+hLkrtoyZF7dHg2iN799o78e++a4edALRpA+efb6euvOAC6NkzsB/AeXm2B8XixeE3KCopKbE/2J95xvZKeeABeywNqUBiVpY9r5s2wWuvwc0313VEiqIoQGSYFGOAS6uYFIONMXd7bLPO2cbTpBgM5ANJxphDIjIA+BDoa4zJrfIeE4GJAOnp6QN2Vk4ZpCiKEgQVFYavNu7nhbnbWL7zMNFNhLIKQ4dm8dw+rCtjB6eFdRrTkrIK3l6yk399vZWcghKuO7Mj913Sk04tTjZEisvKWbHzCPO3HmD+loOs2XMUYyA5LppzurdyjIk2dGmV6NV42HmogDcX7eQ/GbvJKyqjT2oK44d24eozOvg9TGHf0SKufXYBAB/eNSwkvTKU0KImRe3R4EwKT3btgq+/hm++sX+zsuzrHTpYs6LStOjatWatujAoPHnjDbjrLjvUpGlTuOQSuOoq26ujXbvajSWUrF1rz+vRo7b+xsiRdR2RoijKcSLBpAh4uIepEpSIzAHuN8Z4/dZv0I0CRVHqjGWZOcxYtYezu7bisn7ta3UIQ25RKS84s08YA+OHdeHK/qks3ZHD/K0HWbI9h2Ol5UQ1Ec5Ma87wHm04t0drTu/UzHWchSVlfLByD68vzGTz9/m0SIxh7KB0fnxOZzr6mJozv7iMG19YxM5DBbw3aSh9OqQEe9hKGGjsJoUW8g4DxsC2bSebFvv323WdO58wLC64ADp1OnlfT4Pi3XdtQce64NgxG/vHH8Mnn1jTRQQGD7aGxZVX1m7x0GCZOxeuucbOqjFzpq1hoiiKEkFEgkkRjR2uMRLYgy2cebMxZp3HNncBp3kUzrzeGHOjiLQBcowx5SLSDZjnbJfj7f0aTaNAUZRGR/bRY/x91mbeX5F1vFB6tzZNGX6K7SkxpFtLkkPUs8MYw6Lth3h9YSZfrv8egEv6tOe2oV0Y0q3lST0yysoruPONDOZtOcgrtw3k/F4hLOKnhJTGbFJoIe9awhjYsOGEaTFnDuQ4zbYePU6YFgMH2mKOS5dag2L06DoN+zjG2BkpPvnEmhZLl9rX09KsWXHVVTb+YGbGqKiww2c2b/7hcuCA3Uak+sXXusolOxtOOcUaFJ07B3c+FEVRwkCdmxROEJcDT2HvXEwzxkwVkUeADGPMRyISD7wJnAnkAOOMMdtFZDTwCFAGlANTjDEf+3qvRtsoUBSl0bBxXy4bs/MY3LUlHXz0bggVWYcL+ffiXby7bBdHCkvp1S6Z24Z24dozO5AQE8VDM9by78W7+PN1p3Hz2elhj0cJnEZuUmgh77qgogLWrDlhWsyda3tQgC1WGUkGRXXs22enZf34YzvbSUGB7aFw8cXWtLjiCkhNrX7fnJzqjYgtW6Cw8MR2CQm2nkfPnidmnzDm5KW617ytb9ECfv97aNkyfOdFURQlCCLCpKhNtFGgKIoSHopKy/lo1V5eW5jJ+uxcUuKjGdy1JbM37Oen53Xjgct613WISg00cpNCC3lHAmVlsGIFzJsHgwbZaVTrC0VFtmdIZS+LXbvs6wMHWsMiNtYWqaw0Iw4dOrFvVBR063bCjPBcOnQI3SwpiqIo9QB/2yNafl1RFEXxSXxMFDcOSmPMwE5k7DzMawsz+XztPq7on8pvLz21rsNTlJqorqBA1Ts03rbJBtI9C3mLSE2FvEMQcgMkOtrWehg8uK4jcU98PIwaZZenn4bvvjthWPzxj7YXQ8eO1ni44YaTjYiuXSEmfIWWFUVRGiJqUiiKoih+ISIM6tKSQV1akltUSlJsNE2a1JOCckpjJgtI83jeCdjrZZssZ7hHM2xtLAMUAxhjljs9LHoCJ3WVMMa8BLwEtidFOA5CiRBEbDHN/v1h8mQ4fNiaEElJdR2ZoihKg0H7mCmKoiiuSYmPUYNCqS8sA3qISFcRiQXGAR9V2eYj4Dbn8Q3A18YYIyJtnMKbOIW8ewDbaylupT7QooUaFIqiKCFGe1IoiqIoitJgMcaUicgvgC84Uch7nWchb+AV4E0R2YpTyNvZfQTwiIhUFvKe5GumMUVRFEVRgkdNCkVRFEVRGjTGmM+Az6q89gePx0XAmGr2ex94P+wBKoqiKIpyHB3uoSiKoiiKoiiKoihKRKAmhaIoiqIoiqIoiqIoEYGaFIqiKIqiKIqiKIqiRARiZ9eq/4jIAWBniGVbAwcjQCOSYtHjiexYIkUjkmLR4wmPRiTFosdTM52NMW1CrKlUg7ZH6pVGJMUSKRqRFIseT3g0IikWPZ7IjqXO2iMNxqQIByKSYYwZWNcakRSLHk9kxxIpGpEUix5PeDQiKRY9HqWhE0l5FSmx6PGERyOSYtHjCY9GJMWixxPZsdRle0SHeyiKoiiKoiiKoiiKEhGoSaEoiqIoiqIoiqIoSkSgJoVvXooQjVDpRIpGqHQiRSNUOg1JI1Q6kaIRKp2GpBEqnUjRCJVOpGgoDYtIyqtIiUWPJzwaodKJFI1Q6TQkjVDpRIpGqHQiRSNUOpGiERBak0JRFEVRFEVRFEVRlIhAe1Io7Cxw+AAADbdJREFUiqIoiqIoiqIoihIRqElRDSIyTUT2i8jaIDTSROQbEdkgIutE5J4ANOJFZKmIrHY0/hhEPFEislJEPglCI1NEvhORVSKSEaBGcxH5r4hsdM7NOQFo9HJiqFxyReTeAHR+5ZzXtSLyjojEB6Bxj7P/OjcxVJdjItJSRL4UkS3O3xYBaIxxYqkQkRqr8XrR+Jvz/1kjIh+ISPMAdR51NFaJyCwR6eBWw2Pd/SJiRKR1AHE8LCJ7PPLl8kDiEJG7RWSTc37/6kvDRyz/8YgjU0RWBaBxhogsrvwcisjgADROF5FFzuf5YxFJqUGj2uuZm5z1oeE2Z73p+J23PjT8zllvGh7r/c1Zb7G4ylulYeItr0Wki4gc88iPF9xqeKxPF5F8Ebk/gDgGe8SwWkSuC0DjYhFZ7lyPlovIhQGek1bOZylfRJ4JRMNZ94CIbBV7vb/Uh0a11y4RiRWRV53jWS0i59cQizedGBF53dHZICIPBKBxi5zcXqoQkTPcaDjr+ov9zljnxOO1veQjFjc56/N7wc+c9RaHm5z1puE2Z73puMlZX/8fv3K2yj6u2gE+dFy1SbxouGob+dBx1U7zohGy717xsx3gZV9XbWgfOq7b9NVouGqnhQxjjC5VFmAEcBawNgiNVOAs53EysBno41JDgCTncQywBBgSYDy/Bt4GPgnimDKB1kGe29eBO53HsUDzIPWigH3YOXfd7NcR2AEkOM+nA+NdavQD1gKJQDQwG+gRaI4BfwV+5zz+HfB4ABq9gV7AHGBggHFcAkQ7jx+vKQ4fOikej38JvOBWw3k9DfgC2FlT/nmJ42Hgfhf/1+o0LnD+v3HO87aB6FRZ/3fgDwHEMgu4zHl8OTAnAI1lwHnO4wnAozVoVHs9c5OzPjTc5qw3Hb/z1oeG3znrTSOAnPUWi6u81aVhLt7yGuji7drir4bH+veB93zlm484Ej1eTwX2Vz53oXEm0MF53A/YE+A5aQqcC0wCnglQow+wGogDugLbgCgvGtVeu4C7gFedx22B5UATH7F407kZeNfjPGcCXdxoVNnmNGB7AHFEA2uA053nrbydkxp03OSsz+PxM2e9xeEmZ71puM1Zbzpuctabht85W0XPVTvAh46rNokfejW2jbzs57qd5kXnYV955ULH73aAl/1dtaF96Lhu0/ube+FetCdFNRhjvgVygtTINsascB7nARuwP4zdaBhjTL7zNMZZXBcREZFOwBXAy273DSWOSzsCeAXAGFNijDkSpOxIYJsxZmcA+0YDCSISjf3S2uty/97AYmNMoTGmDJgLeHXkPfGSY9dgTRycv9e61TDGbDDGbPInBh8as5zjAVgMdApQJ9fjaVNqyF0fn7t/AL+paf8aNPzGi8bPgMeMMcXONvuDiUVEBLgReCcADQNU3vFoRg1560WjF/Ct8/hLYHQNGt6uZ37nrDeNAHLWm47feetDw++creEa7yZng/6uUBougVyP3WiIyLXAdmBdIBoe338A8fj+zHjTWGmMqbyOrQPiRSQuAJ0CY8x8oMjXsfjSwF7T3jXGFBtjdgBbgWrvDPu4dvUBvnK22Q8cAbzeffShY4CmThslASgBcqvZzt/v/pvw8Z3jQ+MSYI0xZrWz3SFjTHkAOn7jS8NFzlar4TJnvWm4zVlvOm5y1ts58Ttnq+CqHeArNFy0SXzhb9vIC67baWHG73ZAdbhtQ/vQCcV3SNCf6UBQk6IWEJEuWNd1SQD7RjndnvYDXxpjXGsAT2E/KBUB7OuJAWY5XdsmBrB/N+AA8KrYoScvi0jTIGMaRwAXM2PMHuAJYBeQDRw1xsxyKbMWGOF010vEOshpbmPxoJ0xJtuJLxt7B6aumQDMDHRnEZkqIruBW4A/BLD/1dg7FKsDjcHhF05Xt2lSwzAaL/QEhovIEhGZKyKDgoxnOPC9MWZLAPveC/zNOa9PAF67APtgLXC183gMLvK2yvUsoJwN5prop47feVtVI5Cc9dQIJmerOZ5g81ZpWFTN667Od+lcERnuVsP5/v0t4HYo6UlxiMjZIrIO+A6Y5NEg9lvDg9HAysofGkHouMFToyOw22NdFu5Nw9XANSISLSJdgQEE1jb4L1CAbaPsAp4wxgRjwo8lsB9/PQEjIl+IyAoR+U0QMQSSs8cJImer6gSSs95wm7OhJtCcDbgdUIVQtEkqCaZtFMp2WlDfvaFquwbbhq6GUFwva43oug6goSMiSdhuafdWccX8wnGrz3DGEH0gIv2MMX7XyhCRK4H9xpjlUsO4SD8YZozZKyJtgS9FZKNzl9ZforHdzu82xiwRkX9iu4g/FEgwIhKLvcC6viA6F51rsF3jjgDviciPjDH/9lfDGLNBRB7HOtD52IZJMF90EYWIPIg9nrcC1TDGPAg8KHYs7S+AKS7ePxF4EHsXJxieBx7FmmyPYrsSTnCpEQ20AIYAg4DpItLNGBOQs00Nd7Rq4GfAr4wx74vIjdieSRe51JgA/EtE/gB8hL1DVyNVr2f2poc7gr0m1qTjJm+r03Cbs54azvsGlLPVnNtQ5K1SDxCR2UD7alY9aIyZ4WxTNa+zgXRjzCERGQAsEJHt/PBmhC+NPwL/MMbkO5/liSIy3mUcODdP+opIb2CxiPyJH97186nhvN4X2x35kgDPyUkEqFH1ojYKuFhEHvamUQ3TsD0tM7BdvRcCD0n1dcV86QwGyoEO2O+fHSJyH1DqQgOwP8qBQuApEfF5TqohGjskYZCj8ZWIjMUOL3Cj4zpnq8F1zlaH25z1htucrYkANar7IjY16eGiHVCDzkj8aJP4eWw+20Y1xOF3O60GHb++e2vQmIwf7YCazom/7ZHaul7WOqaWxpXUtwUXY+d8aMRgxyP9OkQxTcHlOCngL1hXNRNbu6EQ+HcIYnk4gFjaA5kez4cDnwYRwzXArAD3HQO84vH8VuC5IM/Jn4GfB5pjwCYg1XmcCmxyq+Hx+hz8HDdWnQZwG7AISAz0eKqs6+zP58lTAzt+dr+Tu5nYi+suoH0Qcfj1ua7mf/M5cL7H821AmwDPbTTwPdApwDw5CsenjxYgN8j/TU9gqR8aP7ieuc3Z6jQCzNlqddzkra9Y/M3ZqhpB5GxNsfiVt7o0zMWfvK7p81OdBjDPI1ePYIeF/SLIOL5xG4fzeidsPZZhwZ4TYDw1jO/3cU4eAB7weP4FcE4NOjWd+4X4UZOsqg7wLPBjj+fTgBsDiQXb9Xyyn+e2ahzjgNc8nj8E/D+3Om7Xe4nFVc76GYfPnPWm4TZna/j/+JWzXs6J65ytRtOvdoCXfV23SbzouGobVbN/QO20GjS74PK7lwDbATVo+tWG9rG/6za9P7kX7kWHe4QJsRbvK8AGY8yTAWq0kRNVqxOwzuRGNxrGmAeMMZ2MMV2wXzRfG2N+FEAsTUUkufIx1iF0NfuJMWYfsFtEejkvjQTWu43Fg2DuRu8ChohIovO/GokdC+4Kp1cJIpIOXB9EPGCd7Nucx7cBdeJcisgobHfKq40xhUHo9PB4ejXuc/c7Y0xbY0wXJ3+zsAUG97mMI9Xj6XW4zFuHD4ELHb2e2KKvBwPQAedzbIzJCnD/vcB5zuMLAdfdIj3ytgnwe8BrlXVnO2/XM79zNhTXRF86bvLWh4bfOVudRiA56yOWUOStUs/xltdO+yDKedwN6IEdp++3hjFmuEeuPgX82RhT7QwDPuLoKrZmAiLSGTvOPdOlRnPgU+wPrQW+z0hovqN8aHwEjBOROLFDNXoAS11qJzrtJETkYqDMGBNIW2cXcKFYmmLvELv6HnViaIK9MfNuADGA/dHb3zmuaOz3j+vjcZOz3nCTsz7i8DtnfWi4ytkwE1DOum0H+CDoNolDsG2jkLTTgv3uDWHbNag2tIdOSNr0dUJtuSH1acH+0MzGdqnLAu4IQONcbFehNcAqZ7ncpUZ/YKWjsZYAqt1W0TufAGf3wNaTWO0s67DdfwLROQPbBXIN9oLSIkCdROAQ0CyI8/FH7Id+LfAmTkVglxrzsF/Wq4GRweQYtmL2V9gL/FdAywA0rnMeF2Md6S8C0NiKHd9Ymbc1VhT2ovO+c27XAB9jCxO60qiyPpOaZ0qoLo43seNO12C/zFMD0IgF/u0czwrgwkDOifP6a9gxsIHmybnYavGrsbULBgSgcQ/2DtBm4DGcuyA+NKq9nrnJWR8abnPWm47feetDw++c9aYRQM56i8VV3urSMBdveY0dB7/OuQ6sAK5yq1Flm4fxPVOCtzh+7MSxyonj2gA0fo+tvbDKY/Famd/X8TifuRzsEMwsvPRgqEHjQexd2E04sxZ40aj22oW9+7oJe+NjNjXMPuZDJwk7g8U6bDvDa+8FbxrOuvOxRb5ryjVfGj9y4lgL/DXA43GTszV+L/iRs97icJOz3jTc5qyvc+tvzvrS8Ctnq+i5agf40HHVJvGh8xp+to287O+6neZFJ6TfvQQ4MyIu29A+dFy36d3kXjiXyu45iqIoiqIoiqIoiqIodYoO91AURVEURVEURVEUJSJQk0JRFEVRFEVRFEVRlIhATQpFURRFURRFURRFUSICNSkURVEURVEURVEURYkI1KRQFEVRFEVRFEVRFCUiUJNCURRFURRFURRFUZSIQE0KRVEURVEURVEURVEiAjUpFEVRFEVRFEVRFEWJCP4/bUk8GS8voDAAAAAASUVORK5CYII=\n", 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\n", 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\n", 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pvw0buQU4z8xuNLPdo9fkxWbWpIDwrgcuNbMbzGwPM+scXcuhUey7WRhWcUAU+36EIRu5w0wws2fN7NkC6s8tE897+mjC8+Xi6NzOIc9tbs3sH2b2u2h7N8JkxQsIPREKO35dM3vUzA6Jnu89Cc+13PP4kNCz5SUzGxyV2d/Mzi6sXsLwnfrA09H1O4Dwt/2fu39SyH4zCJPBHhy9h98O7FvEsURE0l9ZTHyhRYsWLalYgGsIDcZlhB4CPxC+Re0aU6Y7odvwBsLkbxcSGrlPx5SZR55JGsln4kVgOnB7zGMHLifMkL+e0Evjr3n22a5uQoLk6qiuzYShGGOB38eUeZpCJuUDDmLH29XlXc6MKfttdH1mAMfnPbc4z6MWcBdhyMxmQrfxEcAhcf6tXiRMiLcpOudPgVPylOlfwLmMKcFzI9+JM6NtVQk9IsYRumWvIXSvv47QY+SK6HrVL2D/V9h+ctQ6hGEr3/Hb7QW/JAyxqRVTrjZhwsWJ0XXeFMU5DOiez3FKPHFmzPa2hCTAvOjvtjj6uw2i6AlmE3Ze/Hb72nyPSbg18HMxj5sS7iQxM/pbLCO8Tk6L/n5vAp8XUFeV6Pl5e0muA6mbOPMcQu+dH6NruIjwmtwvpsyQ6NqeFp3LJsIH5w556jqQ8D63lt9uo/kg20/CeSrhvWET4W4W7/DbrWWfZsfJKY8hzF2znvCamQjcFG3bmTC8ZmFU32LgX8RMtkuY0LPQ1zJxvKdH5a6Prs8vhMTrRbF/M8IdjWZGz9vcc+sWx9+gJqF30NyY591LQOuYMvUIt979MXouzQWuiXme5Tv5JdvfgnQVMbcgLWhfQk+Ll6Pr/XN0Xrcl+rmnRYsWLWW9mHvK5ooSEanQzMyBP7r7c6mOpTQqynmIVHRmNgQ4zd0T2btIRESkTGm4h4iIiIiIiIikBSUpREQkqczsOtv+NovbLQk8ToHHMLPrEnUcEamYonlxCnsfOTDVMYqIVAYa7iEiIkkVTTTZuKDtHiYMTcRxCuvivsLDnSFERPIVTRzaspAii9y90NvEiohI6SlJISIiIiIiIiJpQcM9RERERERERCQtKEkhIiIiIiIiImlBSQoRERERERERSQtKUoiIiIiIiIhIWlCSQkRERERERETSgpIUIiIiIiIiIpIWlKQQERERERERkbSgJIWIiIiIiIiIpAUlKUREREREREQkLShJISIiIiIiIiJpQUkKEREREREREUkLSlKIiIiIiIiISFpQkkJERERERERE0oKSFCIiIiIiIiKSFpSkEBEREREREZG0oCSFiIiIiIiIiKQFJSlEREREREREJC0oSSEiIiIiIiIiaUFJChERERERERFJC0pSiIiIiIiIiEhaUJJCRERERERERNKCkhQiIiIiIiIikhaUpBARERERERGRtKAkhYiIiIiIiIikBSUpRERERERERCQtKEkhIiIiIiIiImlBSQoRERERERERSQtKUoiIiIiIiIhIWlCSQkRERERERETSgpIUIiIiIiIiIpIWlKQQERERERERkbSgJIWIiIiIiIiIpAUlKUREREREREQkLShJISIiIiIiIiJpQUkKEREREREREUkLSlKIiIiIiIiISFpQkkJERERERERE0oKSFCIiIiIiIiKSFpSkEJFfmdkYM9toZr9Ey4xi7JtlZh+b2VozW25mY81sUDH2vy7muBvNbGvM4yklOyMREREpT8zsOTP70czWmNlMMzu7GPtmmtnbZrbSzFaZ2VQzu8PMGpUwlv5m5mZ2dUn2F5GSUZJCRPK62N3rRkvneHYwsxOAV4BngVZAc+Am4Ohoez0zq1VYHe7+t9zjAucDn8fE0a00JyQiIiLlxp1AW3evDwwCbjezvYvaycz6AmOAT4Eu7t4QGAjkAHtGZYpsj+RxBrAi+ikiZURJChEpkpmdaWafmtkjZrbazKab2aHRNgMeAG5z9yfcfbW7b3P3se5+TlTFHsBiMxtqZn1SdR4iIiKS3tx9irtvyn0YLR2iXg0Lo56XP5nZPDM7NWbXe4Cn3P1Od18a1fWDu9/s7mOiMnG3R8ysNnACcBHQ0cwyE3iaIlIIJSlEJK87o3/+n5pZ/5j1+wJzgCbAzcDrZtYY6Ay0Bl4tqEJ3/xzoBSwG/mtm08zsajPbOVknISIiIuWTmT1mZuuB6cCPwLvRphaEdkhLQu+GYWbW2czqAPsBrxVWbzHbI8cDvxB6io4ETi/9mYlIPJSkEJFY/we0J/zzHwa8ZWYdom3LgAfdfYu7vwTMAI4Edoq2/1hYxe4+191vAXYDzgO6AFOjsaNtEn8qIiIiUh65+4VAPeBA4HVgU8zmG919k7uPBd4B/gA0InyuWZJbyMzuiealWGdmN8TUHW975AzgJXffCjwPnGxm1ZJxviKyPSUpRORX7v6lu6+N/vk/QxjX+bto8yJ395ji84FdgJ+jx3H1iojqmAZ8CywEugF1EhG/iIiIVAzuvtXd/0eY6+qCaPVKd18XUyy3LbIS2EZMW8Tdr47mpXgDqJpP/QW2R8ysNXAw8N+o+JtATcKXMyKSZEpSiEhhHLDo95bR/BO52hC6S84AFhC6RRbIzGqY2QlmNhz4HtgbuBRo7+7TEh65iIiIVARVgdxenY2ioR252gCLo8TFl8BxRVUWZ3vkj4TPSW+Z2RLCcNeaaMiHSJlQkkJEADCzhtFtRGuaWdVoMqp+hHGYAM2AS82smpn9HtgdeDf6JuJK4EYz+5OZ1TezDDM7wMyGRXX3IAwHuYzwbURrdz/d3T/K0ztDREREKikza2ZmJ5lZXTOrYmZZwMnAhzHFbjGz6mZ2IHAUYc4IgKuBP5vZNWbWLKqvFdAupv542yOnA7cAe8UsxwNHmtlOiEhS7dD1SUQqrWrA7YSxmVsJk1Ud4+4zzGw/wjcUHYGfgKXACe7+M4C7v2pmvwDXA48AG4ApwL1R3cuA3u4+qwzPR0RERMoXJwzt+Cfhy9T5wOXu/mY0mfcSwtCOxcB64Hx3nw7g7v8zs0MIk3tfE3X+XEhIRjwS1V9keyS660db4FF3Xx6zabiZzSIkTf6RkLMVkXyZvsQUkaKY2ZnA2e5+QKpjERERkconSlI85+6tUh2LiCSXhnuIiIiIiIiISFpQkkJERERERERE0oKGe4iIiIiIiIhIWlBPChERERERERFJC0pSiIiIiIiIiEhaqDC3IG3SpIm3bds21WGIiIiknfHjx//k7k1THUdloPaIiIhI/uJtj1SYJEXbtm0ZN25cqsMQERFJO2Y2P9UxVBZqj4iIiOQv3vaIhnuIiIiIiIiISFpQkkJERERERERE0oKSFCIiIiIiIiKSFpSkEBEREREREZG0UGEmzqwI3J3VG7awZM1GlqzeyNI1G1m6ZhMr1m3m+F6t6N6qQapDFBERERERkXT1+OOw777Qq1eqIykxJSnKyKacrSxbs4mlazZul4RYsmYTS1dvZOnasG5TzrYd9q1WxXjr28W8c+mBtGhQMwXRi4iIiIiISFpbtAguvBAGD4bs7FRHU2JKUiTRghXrufLlicxevo4V6zbvsL1G1QxaNKhJ8/o12bNVQ7K61aRZvRq0aFCTFvXD+mb1a7BgxXoG/eNTLn7+G144tw/VqmiUjoiISLzMbCDwEFAFeMLd78qz/UrgbCAHWA782d3nR9u2ApOjoj+4+6AyC1xERKQ4hg8PPz/8ELZsgWrVUhtPCSlJkUT3jpzB5EWrOa5XK1rUjxIPvyYgatCgVjXMrMh6dmtWj7uO78GlL0zg7vemc8NRXcsgehERkfLPzKoAjwIDgIXA12Y23N2nxhSbAGS6+3ozuwC4Bzgx2rbB3fcq06BFRERKIjsbMjJg7Vr4/HPo1y/VEZWIkhRJMnXxGoZ/u5gL+3fg6oFdSl3foD13Yfy8FTzxv7nsvWsjjui+cwKiFBERqfB6A7PcfQ6Amb0IDAZ+TVK4+0cx5b8ATivTCEVEREpr1arQg+Kcc+CJJ2DEiHKbpNC4gSS5b9QM6tesynn9OiSszuuP7MperRvy11cnMWf5LwmrV0REpAJrCSyIebwwWleQs4D3Yh7XNLNxZvaFmR2TjABFRERK7d13IScHzjwT9tsPRo5MdUQlpiRFEoybt4IPpy/j/P4daFA7ceOAqlfN4NFTe1GtinHhf79hw+atCatbRESkgspvXKXnW9DsNCATuDdmdRt3zwROAR40sx2+fTCzc6NExrjly5cnImYREZHiyc6GFi2gd2/IyoJvvoFly1IdVYkoSZFg7s49I2bQpG4NzuzbNuH1t2xYiwdP6smMpWu5Ifs73PNtZ4mIiEiwEGgd87gVsDhvITM7DLgeGOTum3LXu/vi6OccYAzQM+++7j7M3TPdPbNp06aJjV5ERKQoGzfCe++Fu3pkZMDAgWH9qFGpjauElKRIsLEzl/PVvBVceuhu1K6enCk/DurUlEsP6chr3yzkpa8XFL2DiIhI5fU10NHM2plZdeAkYHhsATPrCQwlJCiWxaxvZGY1ot+bAPsTM5eFiIhIWvjwQ/jlFzgmGpXYqxc0aVJuh3woSZFA27Y5946cQevGtThpnzZJPdalh3bkwI5NuGn4FL5btDqpxxIRESmv3D0HuBgYCUwDXnb3KWZ2q5nl3k70XqAu8IqZTTSz3CTG7sA4M/sW+Ai4K89dQURERFLvjTegXj04+ODwOCMDBgwIPSm2bUttbCWgJEUCvfvdj0xZvIYrDutE9arJvbRVMoyHTurJTnWqc8F/x7N6/ZakHk9ERKS8cvd33b2Tu3dw9zuidTe5+/Do98Pcvbm77xUtg6L1n7l7d3ffM/r5ZCrPQ0REZAdbt8Lw4XDkkVCjxm/rBw4Mc1JMnJi62EpISYoEydm6jQdGzaRT87oM3quwScMTp3Gd6jx6ai9+XLWRv7wykW3bND+FiIiIiIhIpfHFFyEZcUyeG1Adfnj4WQ6HfChJkSCvjl/InJ/WcdXhnamSkd9E4snRq00jrj9yd0ZPW8bQj+eU2XFFREREREQkxbKzoVo1OOKI7de3aAF77aUkRWW1cctWHvrge/Zq3ZABXZuX+fHP7NuWI3vszL0jp/P57J/L/PgiIiIiIiJSxtzDfBSHHgr16++4PSsLPv0U1qwp+9hKQUmKBHjui/n8uHojV2d1xqzselHkMjPuPr4HbZvU4ZIXJrBszcYyj0FERERERETK0JQpMHv2jkM9cmVlQU4OfPRR2cZVSkpSlNLajVt49KNZHLBbE/ru1iRlcdStUZV/nrY36zblcPELE8jZWv5mcRUREREREZE4ZWeDGQwenP/2/feHOnXK3ZAPJSlK6cn/zWXl+i38NatzqkOhU/N63Hlcd76au4J7R85IdTgiIiIiIiKSLNnZ0KdPmH8iP9WrwyGHwIgRYWhIOaEkRSmsWLeZJz6Zy8BuLdizdcNUhwPAMT1bcuq+bRj68RxGTlmS6nBEREREREQk0RYsgPHjCx7qkSsrC+bOhVmzyiauBFCSohQe+2gW6zfncFVWp1SHsp2bju5Kj1YNuOqVb5n/87pUhyMiIiIiIiKJ9Oab4WdRSYqBA8PPcjTkQ0mKElq8agPPfjGf43q1Yrdm9VIdznZqVK3Co6f0IsOM85/7ho1btqY6JBEREREREUmU7GzYfXfoVMQX5h06hGXEiLKJKwGUpCihhz/4Hnfn8sM6pjqUfLVuXJu/n7gn035cw81vTkl1OCIiIiIiIpIIK1bAmDFw7LHxlc/KCnf42LQpqWElipIUJTBn+S+8Mn4hp+67K60a1U51OAU6pEtzLj54N14at4CXxy1IdTgiIiIiIiJSWu+8A1u3Fj3UI9fAgbB+PXz6aXLjShAlKUrggfdnUqNqBhcdvFuqQynSFQM60bfDTtyY/R1TFq9OdTgiIiIiIiJSGtnZ0LIl7L13fOUPPhiqVSs3Qz6UpCim7xat5u1JP/Ln/dvRtF6NVIdTpCoZxsMn96Rh7Wpc8sIENuVofgoREREREZFyacOGkGwYPBgy4vw4X7cu7L9/uZk8U0mKYrpv1Awa1KrGOf3apzqUuDWpW4N7T9iTOcvX8fiY2akOR0REREREREpi9OgwdCPeoR65Bg6ESZNg8eLv2wk7AAAgAElEQVTkxJVASU1SmNlAM5thZrPM7Jp8tl9pZlPNbJKZfWBmu8ZsO8PMvo+WM5IZZ7y+mruCMTOWc0H/DjSoVS3V4RRLv05NOXrPXXjso9nMWf5LqsMRERERERGR4nrjDWjQAPr3L95+WVnh56hRCQ8p0ZKWpDCzKsCjwBFAV+BkM+uap9gEINPdewCvAvdE+zYGbgb2BXoDN5tZo2TFGg93554R02lWrwZn7Nc2laGU2I1H7U6NahnckP0d7p7qcERERERERCReOTkwfDgcdVSYY6I4evSA5s3LxZCPZPak6A3Mcvc57r4ZeBEYHFvA3T9y9/XRwy+AVtHvWcD77r7C3VcC7wMDkxhrkT6asYxx81dyyaEdqVW9SipDKbFm9Wpy9cAufDb7Z7InLkp1OCIiIiIiIhKvzz6Dn38u/lAPCPNXZGXB+++HO4OksWQmKVoCsfe9XBitK8hZwHvF2dfMzjWzcWY2bvny5aUMt2Dbtjn3jpxJm8a1OTGzddKOUxZO7d2GvVo35Pa3p7F6/ZZUhyMiIiIiIiLxyM6GGjV+G7pRXFlZIckxfnxi40qwZCYpLJ91+Y4xMLPTgEzg3uLs6+7D3D3T3TObNm1a4kCL8vbkH5n24xquHNCJ6lXL91yjGRnGHcfuwaoNW7hrxPRUhyMiIiIiIiJFcQ9JisMOg3r1SlbHgAFglvZDPpL5iXshENvtoBWww1SiZnYYcD0wyN03FWffsrBl6zYeGDWDLi3qMWjPXVIRQsJ126UBf+rblhe++oHx81ekOhwREREREREpzKRJMHcuHHtsyeto2hT23rtSJym+BjqaWTszqw6cBAyPLWBmPYGhhATFsphNI4HDzaxRNGHm4dG6MvfKuIXM+3k9Vx3emYyM/Dp4lE9XDOjELg1qct3r37Fl67ZUhyMiIiIiIiIFyc4OvSCOPrp09WRlwRdfwKpViYkrCZKWpHD3HOBiQnJhGvCyu08xs1vNbFBU7F6gLvCKmU00s+HRviuA2wiJjq+BW6N1ZWrjlq089MFMerVpyKG7NyvrwydVnRpVGTKoGzOWruXJ/81NdTgiIiIiIiJSkOxs2H9/aFbKz6UDB4aJMz/4IDFxJUHVZFbu7u8C7+ZZd1PM74cVsu+/gX8nL7qiPfv5PJau2cRDJ/XErOL0osh1eLcWDOjanAdHz+TI7jvTunHtVIckIiIiIiIisebNg4kT4b77Sl/XvvtC/fphyMfxx5e+viQo37NAJtGajVt4bMxsDuzYhD7td0p1OElzy6BuZJhx8/ApuOc7r6mIiIiIiIikyptvhp8lufVoXtWqwaGHwogRYTLONKQkRQGe+GQuq9Zv4eqsLqkOJal2aViLKwd04sPpyxjx3ZJUhyMiIpJwZjbQzGaY2Swzuyaf7Vea2VQzm2RmH5jZrjHbzjCz76PljLKNXEREBHjjDejeHTp0SEx9AwfCggUwPT3v9qgkRQH6dtiJyw7tSPdWDVIdStKd2bctXXeuz5C3prB245ZUhyMiIpIwZlYFeBQ4AugKnGxmXfMUmwBkunsP4FXgnmjfxsDNwL5Ab+DmaEJvERGRsvHTT/DJJ4npRZErKyv8HDEicXUmkJIUBejTfieuGNAp1WGUiapVMvjbcd1ZtnYT94+amepwREREEqk3MMvd57j7ZuBFYHBsAXf/yN3XRw+/INz6HCALeN/dV7j7SuB9YGAZxS0iIgJvvw3btiU2SbHrrtC5c9reilRJCgFgr9YNOW3fXXn283lMXrg61eGIiIgkSktgQczjhdG6gpwFvFfCfUVERBIrOxtat4aePRNb78CBMHYsbNiQ2HoTQEkK+dVfB3Zmp7o1uO6NyWzdlp6TqIiIiBRTfrfnyvefnJmdBmQSbpEe975mdq6ZjTOzccuXLy9xoCIiIttZvx5GjQq9KBJ9t8msLNi4ET7+OLH1JoCSFPKr+jWrcdNRXZm8aDXPfj4v1eGIiIgkwkKgdczjVsDivIXM7DDgemCQu28qzr7uPszdM909s2nTpgkLXEREKrmRI0NPh2OPTXzdBx0ENWqk5ZAPJSlkO0f12Jl+nZpy/6iZLFm9MdXhiIiIlNbXQEcza2dm1YGTgOGxBcysJzCUkKBYFrNpJHC4mTWKJsw8PFonIiKSfNnZ0KgRHHhg4uuuXRv69VOSQtKfmXH74D3YsnUbt7w1JdXhiIiIlIq75wAXE5IL04CX3X2Kmd1qZoOiYvcCdYFXzGyimQ2P9l0B3EZIdHwN3BqtExERSa6cHHjrLTj6aKhaNTnHyMqCqVPD7UjTiJIUsoM2O9Xm0kM78t53S/hw+tJUhyMiIlIq7v6uu3dy9w7ufke07iZ3z01GHObuzd19r2gZFLPvv919t2h5KlXnICIilcwnn8DKlYm9q0deubciTbPeFEpSSL7OObA9HZvV5cbsKazfnJPqcERERERERCqP7GyoWRMOPzx5x+jWDVq2VJJCyofqVTO449juLFq1gYc++D7V4YiIiIiIiFQO7iFJkZUFdeok7zhm4Rjvvx+Gl6QJJSmkQL3bNeYPma148pO5TF+yJtXhiIiIiIiIVHwTJsAPPyR3qEeurCxYvRq++ir5x4qTkhRSqGuP2J36tapx3euT2bYt39vKi4iIiIiISKJkZ0NGBhx1VPKPddhh4VhpNORDSQopVKM61bnud7vzzQ+reGlces36KiIiIiIiUuFkZ4fbjjZpkvxjNW4MvXvDiBHJP1aclKSQIh3fqyV92jfmrvem89Mvm1IdjoiIiIiISPpwh/XrE1PX7NkweXLZDPXINXAgfP01/Pxz2R2zEEpSSJHMjNuP6c76zTnc8c60VIcjIiIiIiKSHnJy4MQToUEDOOIIePJJ+OmnkteXnR1+lmWSIisrJFpGjy67YxYiriSFme2R7EAkve3WrC4XHNSBNyYs4tsFq1IdjoiIVEJqj4iISFrZuhX++Ed45RU47jiYORPOPhtatIABA2DoUFi2rHh1ZmfDXntB27ZJCTlf++wDjRqlzZCPeHtS/NPMvjKzC82sYVIjkrR1Tr/21K9ZlX+OnZ3qUEREpHJSe0RERNLD1q3wpz/Biy/CXXfBSy/BrFnwzTfwf/8H8+fD+efDzjvDwQfDY4/Bjz8WXueyZfDpp2XbiwKgSpWQVBk1KvSoSLG4khTufgBwKtAaGGdmz5vZgKRGJmmnXs1q/HG/XRkxZQmzl/+S6nBERKSSUXtERETSwrZtcN558J//wK23hqQEgBn07Al33AEzZsCkSXDDDbB0KVx0EbRsCf36wcMPw8KFO9b71lshSVDWSQoIQz4WL4bvviv7Y+cR95wU7v49cAPwf8BBwMNmNt3MjktWcJJ+zuzbjupVMvjXx3NSHYqIiFRCao+IiEhKucPFF4e5J264AW68Mf9yZtC9O9xyC0ydClOmwJAhsGoVXHYZtG4NffvCAw/ADz+EfbKzwzCPHj3K6mx+c/jh4WcaDPmId06KHmb2d2AacAhwtLvvHv3+9yTGJ2mmab0a/CGzNa99s5AlqzemOhwREalE1B4REZGUcocrroDHH4errw69KOLVtSvcdFPoXTF9euhtsXEj/OUvsOuusO++8P77cOyxIcFR1lq1gj32gJEjy/7YecTbk+IfwDfAnu5+kbt/A+DuiwnfZkglcm6/9mxz+Penc1MdioiIVC5qj4iISGq4h8TEQw/B5ZeHeShKmkzo3Bmuuy7MXzFrFtx9d6g/JwdOPjmxcRdHVhZ88gmsW5e6GIg/SfE74Hl33wBgZhlmVhvA3f+TrOAkPbVuXJujeuzMf7+Yz+r1W1IdjoiIVB5qj4iISGrceCPcdx9ceGEYopGo3g4dOoTkx1dfwYYN4U4bqZKVBZs3w5gxqYuB+JMUo4FaMY9rR+ukkjr/oA6s27yV/3wxL9WhiIhI5aH2iIiIlL3bbgvDM84+Gx55JHnDMapVS0698TrwQKhVK+VDPuJNUtR0919v5xD9Xjs5IUl5sPvO9enfuSlPfTqPjVu2pjocERGpHNQeERGRsnXXXWEuiTPOgKFDISPue0+UPzVrQv/+5SZJsc7MeuU+MLO9gQ3JCUnKiwsO6sDP6zbzyrgFqQ5FREQqB7VHRESk7DzwAFx7LZxySribR0VOUOTKyoKZM2Fu6uYfjPcqXw68YmafmNknwEvAxckLS8qD3u0a06tNQ4Z+PIecrdtSHY6IiFR8ao+IiEjZ+Mc/wp03TjgBnnkGqlRJdURlY+DA8DOFvSniSlK4+9dAF+AC4EJgd3cfn8zAJP2ZGRf0342FKzfwzuQfUx2OiIhUcGqPiIhImRg2DC65BAYPhuefh6pVUx1R2enUKdwSNd2TFJF9gB5AT+BkMzs9OSFJeXJol2Z0bFaXx8fMxt1THY6IiFR8ao+IiEjyPPUUnHceHHkkvPRS6iezLGtmYcjHDz+E26KmQFxJCjP7D3AfcAChcbAPkBnHfgPNbIaZzTKza/LZ3s/MvjGzHDM7Ic+2rWY2MVqGx3U2UuYyMozzDurA9CVrGTNjearDERGRCqyk7REREZG4PPccnHUWHH44vPoq1KiR6ohS4+GHYfz45N3FpAjx9lvJBLp6Mb4qN7MqwKPAAGAh8LWZDXf3qTHFfgDOBK7Kp4oN7r5XvMeT1Bm05y48MGoGj4+ZzcFdmqU6HBERqbiK3R4RERGJy8svhzt49O8Pb7wR7nRRWaU4ORPvcI/vgBbFrLs3MMvd57j7ZuBFYHBsAXef5+6TAM26WI5Vr5rB2Qe256t5Kxg/f0WqwxERkYqrJO0RERGRwr3xRriDx/77w1tvQW3d3TqV4k1SNAGmmtlIMxueuxSxT0sg9t6UC6N18appZuPM7AszOya/AmZ2blRm3PLlGmqQSif1bk3D2tV4fMycVIciIiIVV0naIyIiIgXLzoYTT4TeveGdd6BOnVRHVOnFO9xjSAnqzm8AS3G6Z7Zx98Vm1h740Mwmu/vs7SpzHwYMA8jMzFTXzxSqXb0qZ/Zty4Ojv2fm0rV0al4v1SGJiEjFMyTVAYiISAWxdClcdVWYhyIzE957D+rpM0w6iPcWpGOBeUC16PevgW+K2G0h0DrmcStgcbyBufvi6OccYAxhFm9JY2fs15Za1arwz7Gziy4sIiJSTCVsj2gibxER+c3WrfD449ClS7h7x/XXw9ix0KBBqiOTSLx39zgHeBUYGq1qCWQXsdvXQEcza2dm1YGTgLj+uZtZIzOrEf3eBNgfmFr4XpJqjepU56TerRk+cTELV65PdTgiIlLBlKQ9EjOR9xFAV8JtS7vmKZY7kffz+VSxwd33ipZBpQhfRERS7ZtvoG9fuPBC6NkTJk2C22/XHBRpJt45KS4iJArWALj790Cht3Fw9xzgYmAkMA142d2nmNmtZjYIwMz2MbOFwO+BoWY2Jdp9d2CcmX0LfATcleeuIJKmzj6wPQBPfDI3xZGIiEgFVOz2CJrIW0REVq+Gyy6DffaBefPCEI8PPgi9KSTtxDsnxSZ332zRfVLNrCpxzC/h7u8C7+ZZd1PM718ThoHk3e8zoHucsUkaadmwFoP3aslLXy/g0kM70rhO9VSHJCIiFUdJ2iP5TeS9bzGOWdPMxgE5hC9Ndui5YWbnAucCtGnTphhVi4hIUrmHW4tecQUsWQIXXAB33AENG6Y6MilEvD0pxprZdUAtMxsAvAK8lbywpDw7/6D2bNiylWc+m5fqUEREpGIpSXskERN5ZwKnAA+aWYcdKnMf5u6Z7p7ZtGnTYlQtIiJJ8/33kJUFJ50Eu+wCX34Jjz6qBEU5EG+S4hpgOTAZOI/QO+KGZAUl5VvH5vUY0LU5z3w+j3WbclIdjoiIVBwlaY9oIm8Rkcpk40YYMgS6dw+JiUceCT/32SfVkUmc4hru4e7bgH9Fi0iRzj+oA+9PXcqLXy/grAPapTocERGpAErYHvl1Im9gEWEi71Pi2dHMGgHr3X1TzETe9xQvahERKTOjRsFFF8GsWXDyyXD//bDzzqmOSoop3rt7zDWzOXmXZAcn5dfeuzaid7vGPPHJHDbnaB4yEREpvZK0RzSRt4hIJbBoEZx4YhjekZEB778Pzz+vBEU5Fe/EmZkxv9ck/BNvnPhwpCK5oH8H/vTU17w5cRG/z2xd9A4iIiKFK1F7RBN5i4hUUDk5YZ6JG2+EzZvh1lvh6quhRo1URyalEFdPCnf/OWZZ5O4PAockOTYp5/p3akqXFvUY+vEctm0rzhxlIiIiO1J7REREfrV6NfTpA5dfDvvvD1OmhGSFEhTlXlw9KcysV8zDDMI3GfWSEpFUGGbGBf07cNmLExk9bSmHd2uR6pBERKQcU3tERER+df31MGECvPBCGOph+d3MScqjeId73B/zew4wD/hDwqORCufI7jtz36gZPDZmNgO6Nsf05iEiIiWn9oiIiMAXX8Bjj8Gll4ZbjEqFEu/dPQ5OdiBSMVWtksG5B7bnxjen8OXcFfRpv1OqQxIRkXJK7REREWHLFjjvPGjZEm67LdXRSBLEO9zjysK2u/sDiQlHKqLfZ7bmwdHf8/iY2UpSiIhIiak9IiIi/P3vMGkSZGdDPY34q4jimjiTMObzAqBltJwPdCWMA9UzQwpVs1oV/nxAO8bOXM6UxatTHY6IiJRfao+IiFRmc+fCkCFwzDEweHCqo5EkiXdOiiZAL3dfC2BmQ4BX3P3sZAUmFctpfXbl8TGzGTp2Dg+f3DPV4YiISPmk9oiISGXlDhdeCFWqwCOPpDoaSaJ4e1K0ATbHPN4MtE14NFJhNahVjVP3bcPbkxbzw8/rUx0OAItWbeDFr35gU87WVIciIiLxUXtERKSyevllGDEC7rgDWrVKdTSSRPEmKf4DfGVmQ8zsZuBL4NnkhSUV0Z8PaEfVjAyGfTI71aHg7lz2wgSueX0yRzz0CZ/N+inVIYmISNHUHhERqYxWroTLLoPMTLjoolRHI0kWV5LC3e8A/gSsBFYBf3L3vyUzMKl4mtevyXG9WvLyuIUsX7sppbEM/3Yx4+av5PT9diVnq3PKE19yxUsT+emX1MYlIiIFU3tERKSSuvZaWL4chg0Lwz2kQou3JwVAbWCNuz8ELDSzdkmKSSqwc/u1J2frNv4+embKYli3KYe/vTuNHq0aMOToboy6oh+XHLIbb09azCH3jeH5L39g2zZPWXwiIlIotUdERCqTTz+FoUPh8suhp+a2qwziSlJEXSr/D7g2WlUNeC5ZQUnF1b5pXf68fzue//IHvp63IiUx/OOjWSxds4mbj+5GRoZRs1oV/nJ4Z967rB9dd6nPdW9M5vh/fsbUxWtSEp+IiORP7RERkUpm82Y47zxo0wZuuSXV0UgZibcnxbHAIGAdgLsvRrf6khK6YkAnWjasxbWvTy7zSSvn/rSOJz+Zy3G9WrL3ro2227Zbs7q8cE4fHvjDnvzw83qO/sf/uOOdqazblFOmMYqISIHUHhERqUzuuw+mTIFHH4W6dVMdjZSReJMUm93dAQcwszrJC0kqujo1qnL7sXswa9kv/HPMnDI99u1vT6V61QyuGdgl3+1mxnG9WvHBXw7iD5mt+dcncznsgbGMnLKkTOMUEZF8qT0iIlJZzJoFt90GJ5wARx2V6mikDMWbpHjZzIYCDc3sHGA08K/khSUV3cGdmzFoz1149KNZzFr2S5kc86Ppy/hg+jIuPXQ3mtWvWWjZhrWrc+dx3Xntgv1oUKsa5/1nPGc/8zULV6bH7VNFRCoptUdERCoDd7jgAqheHR56KNXRSBmL9+4e9wGvAq8BnYGb3P2RZAYmFd+NR3WlVvUqXPf65KRPVLk5Zxu3vj2V9k3qcGbf+OdY23vXxrx1yQFc/7vd+XTWzwx44GP+OXY2W7ZuS2K0IiKSH7VHREQqieefh9Gj4c47YZddUh2NlLEikxRmVsXMRrv7++7+V3e/yt3fL4vgpGJrWq8G1/9ud76at4KXxi1I6rGe+nQuc39ax01Hd6V61eLc1AaqVcngnH7tGf2XgziwYxPuem86Rz38P8alaOJPEZHKSO0REZFKYsUKuOIK2HffMGmmVDpFflpz963AejNrUAbxSCXz+8xW9GnfmL+9O41lazYm5RjL1mzk4Q++57Ddm9G/c7MS19OyYS2GnZ7Jv07PZO3GLZzwz8+55rVJrFy3OYHRiohIftQeERGpJK6+OiQqhg2DKlVSHY2kQLxfKW8EJpvZk2b2cO6SzMCkcjAz/nZsdzblbOOWt6cm5Rh3vTedLVudG47smpD6BnRtzvtXHsR5/drzyviFHPrAWL5fujYhdYuISKHUHhERqcg+/hiefBL+8hfo0SPV0UiKxJukeAe4EfgYGB+ziJRa+6Z1ufSQ3Xhn0o98MG1pQuseP38Fr09YxNkHtqNtk8RNAl+nRlWu/d3uvHPpAeRs3cbdI2YkrG4RESmQ2iMiIhXVpk1heEfbtnDTTamORlKoamEbzayNu//g7s+UVUBSOZ3brwPDv13MjdnfsW/7nahbo9CnZly2bnOGDJ9Ki/o1uejg3RIQ5Y66tKjPuf3ac9+omUz4YSU92zRKynFERCoztUdERCqBu++G6dPh3Xehju4wXZkV1ZMiO/cXM3stybFIJVa9agZ3HteDH9ds5P5RiemV8Mq4BUxetJprf9eFOglIehTkT/u3Y6c61bl/1MykHUNEpJJTe0REpCKbORPuuANOPBGOOCLV0UiKFZWksJjf2yczEJG9d23EH/vsytOfzWPiglWlqmv1hi3cM3IG+7RtxKA9k3vbojo1qnJB/w78b9ZPfD7756QeS0SkklJ7RESkonKH88+HWrXgwQdTHY2kgaKSFF7A7yJJ8deszjSvV5NrXpvElq3bSlzPg6Nnsmr9ZoYM6oaZFb1DKZ3WZ1da1K/JfaNm4K6XiohIgqk9IiJSUT37LHz0URju0aJFqqORNFBUkmJPM1tjZmuBHtHva8xsrZmtKYsApXKpV7MatwzuxvQla3nik7klqmPm0rU8+/l8Tu7dhm67lM2d6mpWq8Ilh+7G+PkrGTNjeZkcU0SkEilVe8TMBprZDDObZWbX5LO9n5l9Y2Y5ZnZCnm1nmNn30XJGAs9JRKR8WbQI7rsPevWCdu3gpJNCz4cvvgiTXpbETz+FO3n07QvnnJPYeKXcKjRJ4e5V3L2+u9dz96rR77mP6xdVuRoFUhJZ3VqQ1a05D46eyfyf1xVrX3dnyPAp1K1RlasO75ykCPP3+71b07pxLe4bNYNt2/RFn4hIopSmPWJmVYBHgSOArsDJZpb3ntQ/AGcCz+fZtzFwM7Av0Bu42cw0Q7KIVB5r1sBTT8Fhh0Hr1vDXv0K1apCZCZ9/DldcAfvtB/XrQ58+cPnl8MILMHduGMZRlKuugtWrYehQyIj3xpNS0SXtmaBGgZTGLYP2oHqVDK5/47tiDZ8Y8d0SPpv9M1cd3olGdaonMcIdVa+aweWHdmLK4jWMnLKkTI8tIiIF6g3Mcvc57r4ZeBEYHFvA3ee5+yQg7zjDLOB9d1/h7iuB94GBZRG0iEjKbN4Mb70VJrFs3hz+/GeYNy/cFnTmTPjyS3jlFZg/P/SueP31kJyoUQOGDYNTToH27cPQjcGD4c47w3COtWu3P86HH8Izz8DVV8Mee6TkVCU9Je+WBzGNAgAzy20UTM0t4O7zom0FNgqi7bmNgheSGK+kkRYNanL1EV24Mfs7Xv9mEcfv3arIfTZs3srt70yjS4t6nNy7TRlEuaNjerbk8bGzuf/9mRzerQVVMpI/H4aIiBSqJbAg5vFCwpcgJd23Zd5CZnYucC5Amzap+f8jIlIq7qFnxHPPwUsvwYoV0KQJnHUWnHYa7Lsv5DfP2y67wLHHhgUgJwcmTw5DQL74IiQ0hg8P2zIyQjKiT59Q3113hWTGDTeU3XlKuZDMJIUaBVIqp/ZuQ/aERdz+zlT6d27KTnVrFFp+6MezWbRqAy+e24eqVVLTXaxKhnHlgE5c+N9veHPiIo7rVXRyRUREkiq/bHG8XfTi2tfdhwHDADIzMzXeT0TKjxkz4L//DcucOVCzJhxzTEhMHH54GNpRHFWrQs+eYbnggrBuxQr46qvfEhcvvxx6XACMGhXu6iESI5mf5MqkUeDume6e2bRp02IFJ+kvI8O487ju/LIphzvemVZo2YUr1/P4mNkc1WNn+rTfqYwizN/Abi3otkt9Hhz9fanuUCIiIgmxEGgd87gVsLgM9hURSU9Ll8JDD8E++0CXLnDHHdChAzz9dNj2wgtw5JHFT1AUpHFjGDgQhgyBESPg559h2jT47DMYMCAxx5AKJZlJCjUKpNQ6Na/HBQd14PUJi/jk+4LvmvG3d6dhBtf9f3v3HR5VlT5w/HvSKRKS0A2hE5pIDSCKSBOxrbqKoqhr+2Fd26rYd+2uunZdRWxgXQWxrDSXsvTeCYQWQGpCSAKkzZzfH+eyhJAp984kM0nez/PMk5lk7jvvnZzMnLxzyoiOlZhd+SIiFA8OSyUz+yjfLN3p+wAhhBAVaQnQTinVSikVA1wNTPHz2KnAMKVUgrU21jDre0IIUXXs3w8//QRPPAGDB8Ppp5s1JNxuePVV2LnTjGi44QazAGZFi4gwxZF+/Sr+sUSVVJHTPf7XKQB2YzoFo/w8dirwfKnFMocBY4OfoqgK7jivLT+t3sNjk9Yy9d4B1IqJPOnn8zIO8suavTwwtD3N6ofHcLGBqQ3pkVKft2ZmcEWPZOKiI30fJIQQIui01iVKqbswfYtIYLzWep1S6m/AUq31FKVUb2ASkABcrJT6q9a6s9Y6Wyn1DKZPA/C34+tlCSFEWDp6FJYvN9MrFi0yX7dvNz+LiIAzzjALVV53HXQqu6eBEOFB2dk5wXZwpUYAr3OiU/Ccl05BAbBXa93ZOvYm4FEr1HNa64+9PVavXr300qVLKyP3+nsAACAASURBVOpURIgt3JrF1R8s5P/Obc3YC06Mlih2ubnwzbkcK3Yx/b5zw6oYMH/LQUZ9uIjHL+zILee0DnU6QogaTCm1TGvdK9R51ATSHxFCVBqXCzZuPFGMWLTILFrpcpmft2gBaWlmkcq0NOjRA+rUCW3Ookbztz9SkSMp0Fr/AvxS5ntPlrq+BDOVo7xjxwPjKzI/UXX0bZ3EyF7NGTd3G5ec2YzOzeIBmLBwB5v25fPB6J5hVaAAOKtNA/q3TeK9WVu4Ji2FOrEV+ucmhBBCCCGCbeZMSEqCbt1CnQn8/rspRBwvSixdemJbz/h4s8bEI4+YgkRamtkCVIgqKDRbIAjhwKMjOpJQO4ax36/B5dZk5Rfy2vRNnNOuAUM7NQ51euV6cFgqWUeK+GT+9lCnIoQQQggh7HjjDRgyxIxAuPVWs7ZDKGRmwqhRZi2Jyy8360jk5cHo0fDpp2YRyuxsmD4dnn0WLrlEChSiSpMihagy4mtH89TFnVi96zCfzN/OK9PSOVbk4qmLO6HK27c5DHRPSWBIx0b8c/YWDh8rDnU6PuUcLeLzBdspKHaFOhUhhBBCiNDQ2vyzf++9cNllcP/9ZueL9u1N4aK4kvp0eXnw2GOQmgqTJplREvPnm+8vWQLvvAPXX28WoYyQf+tE9SGtWVQpF3VtynmpDfn71I18tWQnN57VkraNTgt1Wl7dPzSV3IISPpyzNdSpeFVY4uLWz5byxA/ruPnTJRwrqvqFCrdbc/ho+BeHhBBCCBEmtIaHHzY7YYweDd98A6+8YtZ66NPHFC66dYMZMyouB5cLxo2Ddu3g+efhiisgPR1eeMHsiBEXV3GPLUQYkCKFqFKUUjzzhy5EKEVSnVjuGdIu1Cn51KlZPS7s2pTx87ZxML8w1OmUS2vNI9+tYcn2Q1yTlsKCLVnc+PFijhSWhDo1x9L35nH5e/NJe34Ga3cfDnU6QgghhAh3bjfccQf8/e/m6yefQJS1pliHDvDrr/DDD1BQAEOHmqkX27YFN4eZM09ML2nTBhYuhAkTICUluI8jRBiTIoWocpITajPxlj5MuCWNenHRoU7HL/cNaU9BsYv3Zm0JdSrleuu3DCat2M0DQ9vzwuVn8PrV3Vm64xA3fryY/CpWqCgodvHqtHQufHMumdlHOS0uivu+XilTWIQQQgjhWUmJmTrx/vtmJMXbb586hUIps97DunXw3HMwdSp07AhPPmm2/gxEerqJPWQI5OaaERz//a8ZvSFEDSNFClEldU9JoEOTeqFOw29tG9Xl8h7JfL5wB3sOHwt1OieZsup3Xpu+icu7n85dg9oCcMmZzXjrmu6syMxh9EeLyC2oGlMmFm3NYsSbc3nrtwwuObMZM+4/l1euPJPN+/N5+df0UKcnhBBCiHBUWAhXXgkTJ5rpFS++aAoSnsTFwaOPmsLC5ZfDM8+YkRbffGOmi9iRlQX33ANdusCsWeaxN2ww+YTpmmtCVDQpUghRSf48uB1aa97+LSPUqfzPsh2HePDbVaS1TOSFK844aQHSEWc05e1RPVi7+zCjxy0K67UdDh8rZuz3axj5wUKKStx8dlMar43sRmKdGAamNuL6fi0YP28b8zIOhjpVIYQQQoSTI0fg4oth8mR4800YO9b/Y5OT4YsvYM4cSEyEkSPhvPNg9WrfxxYVwT/+AW3bmgUwb7kFMjLMKA5Zc0LUcFKkEKKSNE+szdW9U/h6yU4yswIcEhgEO7OPcttnS2kaH8f7o3sSGxV5yn2Gd2nCe9f2ZMOePK79aCGHjhSFIFPvfl27h6GvzebrJZncek4rpt03gAHtG550n7EXdKR1wzo88M2qsC62CCGEEKIS5eTA+eebdSA+/hjuvttZnHPOgWXL4L33YO1a6N4d7rzTjJIoS2tTEOnc2ewakpYGq1aZYxs1Cux8hKgmpEghRCW6a1BbIiMUr8/cFNI8cguKuemTJRS73Iy/sTeJdWI83ndIp8b88/qebNqXz6hxi8gOk0LFvtwC/u/zpYyZsJwGdWOZfGd/HruwE7Vjok65b62YSF4f2Y2D+YU8/sPaEGQrhBBCiLBy4AAMGgSLF8PXX8ONNwYWLzISxoyBTZvMopvvv2+2LH3vPbNbB8CKFeYxL7sMoqPhl1/MuhZdugR8OkJUJ1KkEKISNa4Xxw1ntWTyit1k7M8LSQ7FLjd3TlzOtoNHeH90T9o0rOvzmPNSGzHu+l5sPZDPqA8XhnSXErdbM2HhDoa8OptZ6Qd4eHgHfrirP12T63s9rmtyff48uB0/rvqdH1burqRshRBCiBpm0SKzK0Z6uv31GSrL7t1w7rlm7YcffoA//jF4sRMT4a23TEGia1dTsOjZ0yzK2bOnGWnxzjtmSsgFFwTvcYWoRqRIIUQlG3NuG2pFR/La9MofTaG15ukp65i7+SDPX3YGZ7Vp4PexA9o35OMbe7M96wjXfLCQ/XkFFZhp+TL25zPygwU8PnktZyTHM/XeAdw+sA3Rkf69lN0+sA09Uurz+OS1/J4TXguYCiGEEFXelClw9tnwpz+ZhSQbNjQ7Vrz0EsydC8fC4L1361YzPWPXLrOlaEUVCrp2hd9+M4tpZmeb0RoPPgibN5vCRdSpIz+FEIYUKYSoZIl1Yrj57Fb8smYva3cfrtTHHj9vOxMXZTLm3DZc1bu57ePPatuAT/6Uxu6cY1z9wUL25VZOoaKoxM2bMzcz4o25bNqXz8t/7MrEW/rQskEdW3GiIiP4x8huuNyaB75Zhdsdpp/wCCGEEFXNr7+aHSm6d4fly2HcOFOg2LQJHnkEBgyA+Hjo1w8eeAC+/x727q3cHNevNwWKnByzDsW551bs4yllnpPNm830kpdfhvreR34KIaRIIURI3DKgNfG1onl1WuVtizlj/T6e/Xk9wzs34aHzUx3H6ds6iU9vSmPf4QJG/nNBhY9IWLbjEBe9NZfXpm9iWOfGzLj/XK7q1fyknUjsaJFUhycv6sSCrVmMn7ctyNkKIYQQNdCMGfCHP5jFIKdONYWKm2+G8eNh40bzD/oPP5iFIqOizHSHK66Apk2hTRszFeL992HNGnC7KybH5ctNUcLlgtmzoXfvinmc8sTGQr16lfd4QlRxSofrXDGbevXqpZcuXRrqNITw27uzMnj513S+u70fPVskVuhjrfv9MFe+v4C2jery9W39qBVz6k4edi3PPMQNHy2mfp1ovry1L8kJtYOQ6Qm5BcW8OjWdzxbuoGm9OJ69rAuDOjQOSmytNbd+tow5mw/w411nk9rktKDEFSJcKaWWaa17hTqPmkD6I6LGmT3bTJlo2xb+8x9ISvJ9TGGhWbNh3rwTl/37zc/i46FvX+jTxyw82batufgT15N582DECDOKYcYMaNfOeSwhhGP+9kekSCFEiBwtKmHAy7No16guX97Wt8IeZ19uAZe+PQ+l4Ic7+9OoXvD23l61M4fRHy3itDhTqEhJcl6oKHG5Wb37MPM2H+S/GQdZnnmIErfmhn4tefD8VOrGBnfu5sH8Qoa/PoeGp8Ux+c6zyt2CVYjqQooUlada9kdcLvPP57RpZrvGwYNDnZEIF/Pnw7BhkJICs2Y530JTa7NWxLx5Jua8ebBu3ckLbyYknChYlL00bGimVpRn+nQzyiM52RQomtuf7iqECA4pUghRBXw8bxt//XE9E2/pQ/+2/i9i6a+jRSVc9c8FbDtwhG/HnEWnZsEfarh292Gu+2gRtaIj+fLWvn6vE6G1JmN/PvMyDvLfjCwWbc0ir7AEpaBzs3r0b9OAi7o244zk+KDnfNzMDfu4+dOl/N+5rRl7QccKexwhQk2KFJUn6P2R+++HjAwzt//ii6FxcEaU+aQ1LFsGEyfCV1+dvHbA+eebhRDPPLNychHhafFiGDrUtMnZs83UjWAqKIBt20z7L3vZvv3kaSGnnVZ+8WLXrhOLeE6bVnl/P0KIcvnbH5FlZYUIoWvSUvhgzlb+PjWds9okOV5noTxut+ber1ay/vdcxt3Qq0IKFABdTo/ni1v6cu24hYz8YAFf3NrX47amew4fY15GFvMzzGiJ/XlmK9MWSbW5uFsz+rdpQL82SSTWiamQXMsa3LHx/34H56U2om/rAIaSCiFERUhIgFWr4McfzSfFffqYgsUll0CnTp4/PXZq82b44gtz2bQJYmLgwgvh2mvNCIqPPoLnnjNrDoweDc88Yz5FFzXL8uWmWNWggdnBItgFCoC4OOjY0VzKKiqCHTtOLV6sXAmTJkFJyYn79ukDv/xitgYVQlQJMpJCiBD7cnEmY79fw0PDU7m02+mcXr9WUOK+8MsG/jlnK09d3Ik/9W8VlJjebNyby7UfLiIiQvHFLX1o1/g0Dh8rZuHWE0WJLQeOAJBUJ4az2jagf5sk+rdtQPPE4K5nYceRwhIufHMuxS7Nv+89h3px0SHLRYiKIiMpKk+F9Ee0htWrzfaOU6bA8fht2pwoWJx9tvMtDffsMdsjTpxoYisF550Ho0aZxQ3L7kZw6BC88AK8+aa5/ec/m90bEhKcn6OoOtasgYEDoW5dmDMHWrQIdUYnKymBzExTtDh40Px91C3/wxMhROWS6R5CVBHFLjdXvDef1bvMdqTNE2vRt1USfVsn0bdNkqOixVeLM3nk+zWM7tuCv13aOagjNLzZvC+Paz5chNaa5om1Wb0rB7eGWtGRpLVK5Oy2DejftgEdmpxGRETl5OSP5ZmHuPL9BVzarRmvXdUt1OkIEXRSpKg8ldIf2b0bfvrJ7JYwc6b5VDkhwYx4uOQS8wm3r50EDh82W0B+8YX5JNzthh49zIiJkSPh9NN955GZCU88AZ9/bgoZjz0Gd95pPgEXlSsry4wguOKKii0WrV9vChQxMWaKR5s2FfdYQohqR4oUQlQhbrdm4948Fm7NYuHWLBZty+bwsWIAUhJr07d1oilatE6imY+ixbyMg9wwfjH92zbgoxt6ERVZuTsNbzmQzz1friA2KuJ/RYnuKQnERIX3jsevTd/EmzM38+61PRhxRgUMWxUihKRIUXkqvT+Sn2/m2k+ZYgoXWVkQHQ2DBp1Yx+L4QoGFhWbY+8SJ5r6FheafzFGjzKVDB2c5rFoFDz9stp5s0cJMB7nmGogI79f9amPbNrO7Rnq6KU7ddx/ce++pI2ACtWmT2cITTIGiffvgxhdCVHtSpBCiCnNatMjYn8dl786naXwc/7r9LJm6YEOxy80f35vPjuyjTL13AI2DuAuKEKEmRYrKE9L+iMtldkaYMsWMsti82Xy/e3czr//nn80IikaNzGiJa6+FtLTgrWsxYwY89JDZWrJ7d3j5ZRgyJDixRflWrDBbaxYWwhtvmNEUkyaZAsX995upOL5G1fhj61YYMMCM2pk1y6yHIoQQNkmRQohqxJ+iRa+Wibz9WwZHi0qYdEf/kK7zUFVtOZDPhW/OJa1VEp/+qXelTZMRoqJJkaLyhFV/JD39xDoWGzaY6SCjRpkFMJ2uX+GL2212A3n0UbOw4bBhZieQbjKVLuimT4fLLzcLQv7664kFJlesgKefNr/3xER44AG4+26zA4YTO3aYERR5eWYr2q5dg3YKQoiaRYoUQlRjnooWsVERfHlbX3qkyOJlTn2+YDtP/LCOv13amev7tQx1OkHncmt2Zh8lMkIRFx1JXHQEtaIjK31akKhcUqSoPNIfsRQWwjvvwLPPQk4OXHed2Qkk3BZZrKo+/xxuusmMaPj3v6FZs1Pvs3SpKVb8/DMkJcFf/mLWDLGziOSuXaZAkZ1t1j/p0SNopyCEqHmkSCFEDeJ2azbszSU6MoL2jR1+UiIA0Frzp0+WsGBLFj/fcw5tG1XtFcGLStys2Z3D4m2HWLwti6XbD5FXWHLK/aIiFLWiI4mNjqRWTARxUZHERUda3zOFjNJFjbiYSLo0i2dgakNOk2lFaK1ZveswRS43XZPjiY2KDHVKJ6npRQql1HDgDSASGKe1frHMz2OBz4CeQBYwUmu9XSnVEtgApFt3Xai1HuPtsaQ/UkZODrz4Irz+url9990wdqxsB+mU1mZkytixZt2R77+H+HjvxyxeDE89ZUZbNGxopuTccQfU9jHics8es0jmnj1mKk9aWtBOQwhRM0mRQgghHNqfW8D5r88hOaE2391+Vtgv+lna0aISVmTmsGhbNku2ZbNi5yEKit0AtGlYh7RWSXRrHo9SisJiFwXFbo4Vuygodb2w2HXK9wqKXRSWuDlW5KKgxMXRQhdFLjfRkYp+bRowtFNjhnZsTJP4mrWWR35hCZNX7GbCwh1s3JsHQGxUBN1T6pPWKom+rRLpnpJArZjQFi1qcpFCKRUJbAKGAruAJcA1Wuv1pe5zB9BVaz1GKXU1cJnWeqRVpPhJa93F38eT/ogHmZnw5JPw2Wdm949Ro8w/yvLJvP9cLrPGxDvvmIVJP/nE7LLhrwULzMiKadOgcWOz2OmYMVCrnAW59+8329Du2GEWRO3fP1hnIYSowaRIIYQQAfh17R7GTFjO3YPa8sCw1FCn49Hho8Us3ZHN4m3ZLNqWzdrdhylxayIUdGxaj7RWifRpZdYsaVA3NmiP63JrlmceYvr6fUxbt5ftWUcBODM5nmGdmzC0U2PaNapbbdf12LAnlwkLdzB5xW6OFLno1LQe1/VtQVLdGOt3kcX633Nxa4iOVHRNrk+fVon0aZ1EzxYJ1I2toPUAPKjhRYp+wNNa6/Ot22MBtNYvlLrPVOs+C5RSUcBeoCHQAilSBNfatfDmm2aHkaNHoW9fMwXhyishNnivUdXOsWNmodNJk8y0jRdfdL57yrx5ZmTFzJnQtCk88gjcdtuJrWOzskyBIiPD7AYzcGDQTkMIUbNJkUIIIQL04Ler+H75Lr4d04+eLcJjaPL+vAKWWFM3Fm3LJn1fHtr6R/jM5Pr0bpVIWqtEerZIqLTdXbTWZOzPZ9r6fUxbv49VO3MAaJlUm6GdGjOscxN6pCQQGVG1CxYFxS7+vXYPExZmsmzHIWKjIrioazOu65tCt+b1TynI5BYUs2z7IRZuy2LR1mzW7D6My62JjFB0aVaPPq2TSGuZSO9WicTXqtjfVQ0vUvwRGK61vsW6PRroo7W+q9R91lr32WXd3gL0AeoC6zAjMXKBx7XWc709nvRH/JSTA59+Cu++a7a2bNAAbrnFfLIv61acLDvbbCc7fz784x9mNEUwzJljRrfMng2nn24WO73iCrNbyLp1Zpta2Z1FCBFEUqQQQogA5RUUc8Ebc4lQih/u7E9CHRvDaoNs495cnv9lI3M2HQCgVnQkPVskkNYqkd4tE+meUp+46PBYB2FfbgEzNuxj2rp9LNiSRZHLTVKdGAZ3bMTQTk04p12DsMnVHzuyjjBxUSbfLt3JoaPFtGpQh2v7pPDHnsnUr+1/mzhSWMLyzENmpMXWbFbuzKHI5UYp6NjEjHrp2zqRtFZJJAa5rdXwIsWVwPllihRpWuu7S91nnXWf0kWKNCAfqKu1zlJK9QQmA5211rllHuM24DaAlJSUnjt27KiEM6sm3G7zif6775rdKAAuusiMrhgyxPlogepixw4YPtxsATphghlxEmz/+Y8ZWTF3LkRGmud88mRTrBBCiCCSIoUQQgTB4m3ZjPpwIbWiI7ltQGv+dHarSh2qvz+3gFenbeLbZTupGxvFzWe3ZkD7BnQ5PZ7oKrAjR15BMbM3HWD6+n38tnE/eQUlxEVHMKBdQ4Z2akz3lATioiOIiYogNiqS2KgIYiIjiAjxqIsSl5uZG/czYeEO5m4+SGSEYlinxlzXtwX9WicFJb+CYhcrd+awaKuZHrI806wfEhmhWPXUsKC2sxpepHA83UOX6SQppWYBD2qtPXY4pD8SgMxM+Oc/4cMP4cABaNcObr8dbrwREgLcterIEbMN6/r1ZpTA+vWwezdcdZUpiDjdnrMirVoFF1xgpnpMnmx22agoWpti0Vtvwa23mkKREEIEWVgUKWQ1bSFEdbBxby6vTtvE9PX7SKwTwx0D23Bd3xYVOhrgaFEJH8zZygdztlLscjO6b0vuGdzW1if34aaoxM3ibdlMX7+Xaev3sedwgcf7RkcqYqMiibGKFrHRZb5aP4uNiiAuOpLEOjEk1I4hsW4MSdb1pLrma0LtaL+3WN2XW8BXi3fy1ZJM9hwuoEm9OK5JS+HqtOY0rlexi4Ie34ll8758rk5LCWrsGl6kiMJM1xgM7MYsnDlKa72u1H3uBM4otXDm5Vrrq5RSDYFsrbVLKdUamGvdL9vT40l/JAgKC+G778wCkfPnm4UdR40yxYTu3b0fm59vihHHCxHHv27ffuI+MTGQmmq24lywwOw0ct99ZucRXztlVJaZM+Gyy0w+v/4KnTuHOiMhhAhYyIsUspq2EKK6Wbkzh1enpTN380Ga1IvjnsHtuLJXclBHNLjcmu+W7eKVaenszyvkgi5NeHh4B1o2qBO0xwgHWmvW/Z5Lxv58ikrcFLrc5muJy/pqbpf7PZebwmJzzPGdSA4dKSK34NStVQGUgvha0STWjjHFjDqmkJFY6hITFcFPq/YwfcM+XG7NgPYNua5PCoM6NPK7wBHOanKRAkApNQJ4HfOhyXit9XNKqb8BS7XWU5RSccDnQHcgG7haa71VKXUF8DegBHABT2mtf/T2WNIfCbKVK81UkOMLbfbrZ4oVw4fDli0nChHHixGlp9rExECHDtCpk/kn//jXNm0gyhqptHgxPPss/PijKQjce69Z8yHQkRuB+OILM3okNRX+/W9ITg5dLkIIEUThUKSQ1bSFENXSgi1ZvDItnWU7DtEiqTb3DWnPxWc2C3hhyLmbD/DczxvYuDePbs3r8/iFHenVMjwW7KwKil1uDh0tIvtIEdn5RWQfv25dso4UcejIyd8rcZ94D0ysE8OVvZIZlZZCi6TqVRSq6UWKyiT9kQqSk2O23Hz3Xdi8+eSfxcaWX4xo3fpEMcKX5ctNsWLSJDP14+67zeiKBg2CfioeaQ2vvAIPPWR21Jg0CerXr7zHF0KIChYORQpZTVsIUW1prflP+n5embqJ9Xtyad+4LvcPTeX8zo1tb7u5aV8ez/+ygVnpB0hOqMXDwztwUdem1Xb7znChtSa3oMQahVFMapPTiI2qOgt62iFFisoj/ZEKdnyhzRUroH17U4xo1cr/YoQvq1ebYsW//gW1a5tRGw88AI0aBSe+Jy4X3H+/2Z71qqvgs89kS1YhRLXjb3+kIld/K693XbYi4uk+e4CU0qtpK6V8raYdhJSFEMI/SikGdWjMwPaN+GXtHl6bvokxE5bRNTmeB4elck67Bj6LDAfyCnlt+ia+XpJJndgoHh3Rgev7taxSO19UZUop4mtFV/j2n0KIIIqIgKFDzaUidO0K33xjpo4895wZ2fDWW2Zr1L/8BZo2Df5jFhTA6NGmMHLffeYxa/quJkKIGi0sp3vIatpCiKqmxOVm0ordvD5jM7tzjpHWKpG/nJ9K73KmaxwrcjFu7lben72FwhI31/VtwT2D2wV920khjpORFJVH+iPVTHo6PP+8WRMjKgpuu81Mx3CyTkRenom3cePJl82boagIXn3VjKYQQohqKhyme8hq2kKIGqewxMXXS3by1m8ZHMgrZGBqQx4clkqX0+NxuzXfr9jNK1PT2ZtbwPmdG/Pw8A60blg31GmLak6KFJVH+iPV1JYt8MIL8OmnZpTDzTfDww9DixYn309rs7Vp2ULExo3m+8dFRkLbtmYtjQ4dYNgwGDSocs9JCCEqWciLFFYSspq2EKJGOlbk4tMF23lv1hYOHytmeOcm7Dx0lHW/59I1OZ7HRnSkT+ukUKcpaggpUlQe6Y9Uc9u3w0svwUcfmYLEDTeYNTFKFyPy80/cv169E4WIjh1PXG/d2uw+IoQQNUhYFCkqk3QKhBDhKLegmHFzt/HR3K3Urx3DQ8NTubhrMyIC3AlECDukSFF5pD9SQ+zcCS+/DB9+CIWF0Lz5yUWI45cmTcw+yEIIIaRIIYQQ4aSwxEWkUkRFymJoovJJkaLySH+khsnNNdM/6sq0PSGE8CUcdvcQQghhqa5bWwohRI1Wr16oMxBCiGpHPtITQgghhBBCCCFEWJAihRBCCCGEEEIIIcKCFCmEEEIIIYQQQggRFqRIIYQQQgghhBBCiLAgRQohhBBCCCGEEEKEhWqzBalS6gCwI8hhGwAHwyBGOOUi5xPeuYRLjHDKRc6nYmKEUy5yPr610Fo3DHJMUQ7pj1SpGOGUS7jECKdc5HwqJkY45SLnE965hKw/Um2KFBVBKbU00H3lgxEjnHKR8wnvXMIlRjjlIudTMTHCKRc5H1HdhVO7Cpdc5HwqJkY45SLnUzExwikXOZ/wziWU/RGZ7iGEEEIIIYQQQoiwIEUKIYQQQgghhBBChAUpUnj3QZjECFaccIkRrDjhEiNYcapTjGDFCZcYwYpTnWIEK064xAhWnHCJIaqXcGpX4ZKLnE/FxAhWnHCJEaw41SlGsOKES4xgxQmXGMGKEy4xHJE1KYQQQgghhBBCCBEWZCSFEEIIIYQQQgghwoIUKcqhlBqvlNqvlFobQIzmSqn/KKU2KKXWKaX+7CBGnFJqsVJqlRXjrwHkE6mUWqGU+imAGNuVUmuUUiuVUksdxqivlPqXUmqj9dz0cxAj1crh+CVXKXWvgzj3Wc/rWqXUl0qpOAcx/mwdv85ODuW1MaVUolJqulJqs/U1wUGMK61c3Eopn6vxeojxd+v3s1opNUkpVd9hnGesGCuVUtOUUs3sxij1sweVUlop1cBBHk8rpXaXai8jnOShlLpbKZVuPb8ve4vhJZevS+WxXSm10kGMbkqphcf/DpVSaQ5inKmUWmD9Pf+olKrnI0a5r2d22qyXGHbbrKc4frdbLzH8brOeYpT6ub9t1lMuttqtqJ48tWulVEulJTcSiQAADNFJREFU1LFS7eN9uzFK/TxFKZWvlHrQQR5ppXJYpZS6zEGMoUqpZdbr0TKl1CCHz0mS9beUr5R620kM62djlVIZyrzen+8lRrmvXUqpGKXUx9b5rFJKDfSRi6c40UqpT604G5RSYx3EuFad3F9yK6W62Ylh/ayrMu8Z66x8PPaXvORip816fV/ws816ysNOm/UUw26b9RTHTpv19vvxq82WOcZWP8BLHFt9Eg8xbPWNvMSx1U/zECNo773Kz36Ah2Nt9aG9xLHdpy8nhq1+WtBoreVS5gIMAHoAawOI0RToYV0/DdgEdLIZQwF1revRwCKgr8N87ge+AH4K4Jy2Aw0CfG4/BW6xrscA9QOMFwnsxey5a+e404FtQC3r9jfAjTZjdAHWArWBKGAG0M5pGwNeBh6xrj8CvOQgRkcgFZgF9HKYxzAgyrr+kq88vMSpV+r6PcD7dmNY328OTAV2+Gp/HvJ4GnjQxu+1vBjnWb/fWOt2Iydxyvz8VeBJB7lMAy6wro8AZjmIsQQ417p+E/CMjxjlvp7ZabNeYthts57i+N1uvcTwu816iuGgzXrKxVa7lUv1vHhq10BLT68t/sYo9fPvgG+9tTcvedQu9f2mwP7jt23E6A40s653AXY7fE7qAGcDY4C3HcboBKwCYoFWwBYg0kOMcl+7gDuBj63rjYBlQISXXDzFGQV8Vep53g60tBOjzH3OALY6yCMKWA2cad1O8vSc+Ihjp816PR8/26ynPOy0WU8x7LZZT3HstFlPMfxus2Xi2eoHeIljq0/iRzyffSMPx9nup3mI87S3dmUjjt/9AA/H2+pDe4lju0/vb9ur6IuMpCiH1noOkB1gjD1a6+XW9TxgA+YfYzsxtNY637oZbV1sLyKilEoGLgTG2T02mKwq7QDgIwCtdZHWOifAsIOBLVrrHQ6OjQJqKaWiMG9av9s8viOwUGt9VGtdAswGPFbkS/PQxi7FFHGwvv7Bbgyt9Qatdbo/OXiJMc06H4CFQLLDOLmlbtbBR9v18nf3D+AhX8f7iOE3DzFuB17UWhda99kfSC5KKQVcBXzpIIYGjn/iEY+PdushRiowx7o+HbjCRwxPr2d+t1lPMRy0WU9x/G63XmL43WZ9vMbbabMBv1eI6svJ67GdGEqpPwBbgXVOYpR6/wOIw/vfjKcYK7TWx1/H1gFxSqlYB3GOaK3/CxR4OxdvMTCvaV9prQu11tuADKDcT4a9vHZ1AmZa99kP5AAeP330EkcDdaw+Si2gCMgt537+vvdfg5f3HC8xhgGrtdarrPtlaa1dDuL4zVsMG2223Bg226ynGHbbrKc4dtqsp+fE7zZbhq1+gLfUsNEn8cbfvpEHtvtpFczvfkB57PahvcQJxntIwH/TTkiRohIopVpiqq6LHBwbaQ172g9M11rbjgG8jvlDcTs4tjQNTLOGtt3m4PjWwAHgY2WmnoxTStUJMKercfBiprXeDbwCZAJ7gMNa62k2w6wFBljD9WpjKsjN7eZSSmOt9R4rvz2YT2BC7Sbg304PVko9p5TaCVwLPOng+Eswn1CscpqD5S5rqNt45WMajQftgXOUUouUUrOVUr0DzOccYJ/WerODY+8F/m49r68AHocAe7EWuMS6fiU22m2Z1zNHbTaQ10Q/4/jdbsvGcNJmS8cIpM2Wcz6BtltRvZRt162s99LZSqlz7Maw3n8fBuxOJT0pD6VUH6XUOmANMKZUh9jvGKVcAaw4/o9GAHHsKB3jdGBnqZ/twn7RcBVwqVIqSinVCuiJs77Bv4AjmD5KJvCK1jqQIvxInP3z1x7QSqmpSqnlSqmHAsjBSZv9nwDabNk4TtqsJ3bbbLA5bbOO+wFlBKNPclwgfaNg9tMCeu8NVt810D50OYLxellpokKdQHWnlKqLGZZ2b5mqmF+sanU3aw7RJKVUF62132tlKKUuAvZrrZcpH/Mi/dBfa/27UqoRMF0ptdH6lNZfUZhh53drrRcppd7ADBF/wkkySqkYzAus7RdE60XnUszQuBzgW6XUdVrrCf7G0FpvUEq9hKlA52M6JoG80YUVpdRjmPOZ6DSG1vox4DFl5tLeBTxl4/FrA49hPsUJxHvAM5gi2zOYoYQ32YwRBSQAfYHewDdKqdZaa0eVbXx8ouXD7cB9WuvvlFJXYUYmDbEZ4ybgTaXUk8AUzCd0PpV9PTMfetgT6Guirzh22m15Mey22dIxrMd11GbLeW6D0W5FFaCUmgE0KedHj2mtf7DuU7Zd7wFStNZZSqmewDyl1FZO/TDCW4y/Av/QWudbf8u3KaVutJkH1ocnnZVSHYGFSqlnOfVTP68xrO93xgxHHubwOTmJwxhlX9SGA0OVUk97ilGO8ZiRlksxQ73nA0+o8tcV8xYnDXABzTDvP9uUUg8AxTZiAOafcuAo8LpSyutzUo4ozJSE3laMmUqpkZjpBXbi2G6z5bDdZstjt816YrfN+uIwRnlvxNpXPGz0A3zEGYwffRI/z81r38hHHn7303zE8eu910eMR/GjH+DrOfG3P1JZr5eVTlfSvJKqdsHG3DkvMaIx85HuD1JOT2FznhTwAqaquh2zdsNRYEIQcnnaQS5NgO2lbp8D/BxADpcC0xweeyXwUanb1wPvBvicPA/c4bSNAelAU+t6UyDdboxS35+Fn/PGyosB3AAsAGo7PZ8yP2vhz99T6RiY+bP7rba7HfPimgk0CSAPv/6uy/nd/AoMLHV7C9DQ4XMbBewDkh22k8Pwv+2jFZAb4O+mPbDYjxinvJ7ZbbPlxXDYZsuNY6fdesvF3zZbNkYAbdZXLn61W7lUz4s/7drX3095MYC5pdpqDmZa2F0B5vEfu3lY30/GrMfSP9DnBLgRH/P7vTwnY4GxpW5PBfr5iOPruZ+PH2uSlY0DvAOMLnV7PHCVk1wwQ88f9fO5LZvH1cAnpW4/AfzFbhy7P/eQi60262ceXtuspxh226yP349fbdbDc2K7zZYT069+gIdjbfdJPMSx1Tcq53hH/TQfMVti870Xh/0AHzH96kN7Od52n96ftlfRF5nuUUGUKfF+BGzQWr/mMEZDdWLV6lqYyuRGOzG01mO11sla65aYN5rftNbXOciljlLqtOPXMRVCW7ufaK33AjuVUqnWtwYD6+3mUkogn0ZnAn2VUrWt39VgzFxwW6xRJSilUoDLA8gHTCX7Buv6DUBIKpdKqeGY4ZSXaK2PBhCnXambl2C/7a7RWjfSWre02u8uzAKDe23m0bTUzcuw2W4tk4FBVrz2mEVfDzqIA9bfsdZ6l8PjfwfOta4PAmwPiyzVbiOAxwGPq6xb9/P0euZ3mw3Ga6K3OHbarZcYfrfZ8mI4abNecglGuxVVnKd2bfUPIq3rrYF2mHn6fsfQWp9Tqq2+DjyvtS53hwEvebRSZs0ElFItMPPct9uMUR/4GfOP1jzvz0hw3qO8xJgCXK2UilVmqkY7YLHN2LWtfhJKqaFAidbaSV8nExikjDqYT4htvY9aOURgPpj5ykEOYP7p7WqdVxTm/cf2+dhps57YabNe8vC7zXqJYavNVjBHbdZuP8CLgPsklkD7RkHppwX63hvEvmtAfehScYLSpw+JyqqGVKUL5h/NPZghdbuAmx3EOBszVGg1sNK6jLAZoyuwwoqxFger3ZaJNxCHu3tg1pNYZV3WYYb/OInTDTMEcjXmBSXBYZzaQBYQH8Dz8VfMH/1a4HOsFYFtxpiLebNeBQwOpI1hVsyeiXmBnwkkOohxmXW9EFORnuogRgZmfuPxdutzRWEPcb6zntvVwI+YhQltxSjz8+343imhvDw+x8w7XY15M2/qIEYMMME6n+XAICfPifX9TzBzYJ22k7Mxq8Wvwqxd0NNBjD9jPgHaBLyI9SmIlxjlvp7ZabNeYthts57i+N1uvcTwu816iuGgzXrKxVa7lUv1vHhq15h58Ous14HlwMV2Y5S5z9N43ynBUx6jrTxWWnn8wUGMxzFrL6wsdfG4Mr+387H+5rIxUzB34WEEg48Yj2E+hU3H2rXAQ4xyX7swn76mYz74mIGP3ce8xKmL2cFiHaaf4XH0gqcY1s8GYhb59tXWvMW4zspjLfCyw/Ox02Z9vi/40WY95WGnzXqKYbfNentu/W2z3mL41WbLxLPVD/ASx1afxEucT/Czb+TheNv9NA9xgvrei8OdEbHZh/YSx3af3k7bq8jL8eE5QgghhBBCCCGEECEl0z2EEEIIIYQQQggRFqRIIYQQQgghhBBCiLAgRQohhBBCCCGEEEKEBSlSCCGEEEIIIYQQIixIkUIIIYQQQgghhBBhQYoUQgghhBBCCCGECAtSpBBCCCGEEEIIIURYkCKFEEIIIYQQQgghwsL/A5ORdaWw9DtvAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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hH4bTUpRbCpxfU8xJz/Py2o4vheTvN4C9zaxv7FgOIrSWSpSdSehDIPmzm3idP4qmk9KMYXlU/oAa6qyIHes8d/+5u48mdWuUTJlPeB4GJhZErYBKo3WNrfvYpGXHEC7ma/qu34m7f+zuj7r7OOBUwq0xB5jZXoQ+Jn7k7v8XfUfvALqnqKYh/2t6pXh9FqcTcyx2d/fX3P02dz+KkMw8vz51iIhkVK7uM9GkSZOmdCZCS4lLCCeifYDPE3pmXwd0j8rMILQcGEK4iJhMaBXwUKyeJcQ6t4yWLWLXDhffJnZvN591OHk5oQntFYT7p8+qqW7CBd9HhF+h9ydc1FwIXBcrU2ufFIRm368RTlD7EYYeTZ7aES7wVxFGujiAcFL7MqGjvR+kexyEX+yeJ/zC/RVCkufQqNy303idioAnCB2/HZ0UZ8dYuVsIv9b1JzS3/zbh9pS70tzHh8D/1LB+CVH/FoRO/Z6P6v5e7P0zmjBs5VWETiK3AmNT1NWB8Av4N6P5LoTm4isJF2HDotflK4QL29Nj5eZG5S6NnsO+hE78pgBVsX2kXTa2TUb7pCB0PuikGAWFz+55PyKa7xs9/+8QWsocRHh/jyW0XDi4AeXKCZ/DsYTP736ETgtfBxZG5U6lhr4BCL/gVxGN7hK9zp8QvhNOi47hIMIwn+8TmthfSfh+aJOivnHR9u0Jo70srOF5+1FN6+r5/fYUocPCz/FZh4v/IIwQMTAqk+g484XoPXJY9PzM5rOOM08gXPj+jPAduD/h9phJQOuozCVRmeuj52Rw9FzU1HHmBVEc10dlBxLe77+J1g8kfI8dGcV+BOH7alKsjnT7pGhIx5lDqLnjzL1j9RxHUp8LJH1GaHzHmT8m3N52QDT9mvDd05HQ2udjwvfjAVHsrxC+X25MOtaPCe/VAXzWufHpsf0kd5z5e0JC6RuEz84wQkLvv1M9h7HtXiDqQ4PwfX0DoSVFb0KLmBXAzY19f2vSpElTQ6ecB6BJkyZNtU3AeMJF9ypCC4Gl0UltfASOIYRbQbYSLlQvJdxj+1CszBIanqT4L8KF7RZC8uG/k7bZqe7opPTaqK7thFsxXgS+GivzELWM7kH4Vc/rmM6PlU0ME7iQ0Ex8p2NL8zjaAT8hXMxtj05U/0EanQQSLjhrijMexy+i2LYSEk0zCbepFKaxj6+Q1HFe0vqfEjpZLYjmiwgn+jMIF3kbCBd21xNajVwdPWe7DJUZbf8EO3eQ2oHQV8Sbsfhfi+JvFyvXntDRaWJIv4romCcCQ5L2kXbZqHymkxT3Ej5TKTuijZ6v+OdoT8LF+7tRrKsIFzznsnPHpemW6xG9bm9Hz+lWwq/adxINzUrolPDlGuIrItw6E3+P9QMeiN4L2wkXcc8SdaYYvX4P11BfN8KF/MWEz+0Payg3rK7XIZ0p2s8/CZ+1CkJy5yng8FiZxBCk50XHtC3apl9SXccC/yZ0OLkZWED4vMWf728Skm0V0fE9Q+1DkJ5BGH1kK599fhIX1j2jWD+MxX4/sc8TGRzdI7bPxwlJpsQQpIfE1jcoSREtiw9Buorw2Uh3CNIfRO/bzVFsLxAl96L1xxMSdNsIiZyvsHOHy/EhSCcTvgtSDROdnKQoIoxKtZDwXl8d7fvMmp7DaPkLfJakGEL4fCSGT10C3E7UMbEmTZo05WJKZOBFRCQFM3PCL+2P5DqWxmgpxyGyuzGzCYQWTwfmOhbJjuj2pB2E4Zofq6u8iEhLpz4pRERERERERCQvKEkhIiJ1MrPrk4ZV3GnK4H5q3IeZXZ+p/YiIpCPFkLLJ07W5jlFEpKXR7R4iIlInM9uD0NFkSp40fGAj9rN/LavXunvy0JQiIllVx/fSx+6+rsmCERHZDShJISIiIiIiIiJ5Qbd7iIiIiIiIiEheUJJCRERERERERPKCkhQiIiIiIiIikheUpBARERERERGRvKAkhYiIiIiIiIjkBSUpRERERERERCQvKEkhIiIiIiIiInlBSQoRERERERERyQtKUoiIiIiIiIhIXlCSQkRERERERETygpIUIiIiIiIiIpIXlKQQERERERERkbygJIWIiIiIiIiI5AUlKUREREREREQkLyhJISIiIiIiIiJ5QUkKEREREREREckLSlKIiIiIiIiISF5QkkJERERERERE8oKSFCIiIiIiIiKSF5SkEBEREREREZG8oCSFiIiIiIiIiOQFJSlEREREREREJC8oSSEiIiIiIiIieUFJChERERERERHJC0pSiIiIiIiIiEheUJJCRERERERERPKCkhQiIiIiIiIikheUpBARERERERGRvKAkhYiIiIiIiIjkBSUpRERERERERCQvKEkhIiIiIiIiInlBSQoRERERERERyQtKUoiIiIiIiIhIXlCSQkRERERERETygpIUIiIiIiIiIpIXlKQQERERERERkbygJIWIiIiIiIiI5AUlKUREREREREQkLyhJISKfMrMXzGybmW2KpoX12PYkM3vJzDaa2Woze9HMTq3H9tfH9rvNzKpi8/MbdkQiIiLSnJjZI2b2kZltMLN3zOyiemxbambPmNk6M/vEzBaY2W1m1rWBsRxnZm5m1zZkexFpGCUpRCTZ5e7eMZoGprOBmZ0FPAH8AegF7AXcBHw5Wt/JzNrVVoe7/yixX+ASYFosjsGNOSARERFpNn4M9HX3zsCpwAQzO7SujczsCOAFYCpwoLsXA6OBSmBYVKbO85Ek5wFro78i0kSUpBCROpnZ+WY21czuMbP1Zva2mX0+WmfAncAP3f237r7e3avd/UV3/3ZUxcHAcjO738w+l6vjEBERkfzm7vPdvSIxG037Ra0ayqOWl2vMbImZnRvb9A5gkrv/2N1XRnUtdfeb3f2FqEza5yNm1h44C7gMGGBmpRk8TBGphZIUIpLsx9E//6lmdlxs+eHAYqA7cDPwVzPbAxgIlABP1lShu08DDgGWA380s7fM7Foz2ydbByEiIiLNk5n92sy2AG8DHwFTolV7E85DehJaN0w0s4Fm1gEYBfyltnrreT5yJrCJ0FL0OeCbjT8yEUmHkhQiEncd0J/wz38i8LSZ7RetWwX80t13uPufgYXAKUC3aP1HtVXs7u+7+y3A/sDFwIHAguje0d6ZPxQRERFpjtz9UqATcDTwV6Aitvp/3L3C3V8E/g58DehKuK5ZkShkZndE/VJsNrMbY3Wnez5yHvBnd68CHgXGmFmrbByviOxMSQoR+ZS7v+buG6N//r8n3Nf5xWj1h+7useIfAPsCH0fzabWKiOp4C5gLlAODgQ6ZiF9ERERaBnevcvdXCH1dfSdavM7dN8eKJc5F1gHVxM5F3P3aqF+Kp4CiFPXXeD5iZiXA/wP+GBX/X6At4ccZEckyJSlEpDYOWPS4Z9T/REJvQnPJhcAyQrPIGplZGzM7y8wmA+8ChwJXAv3d/a2MRy4iIiItQRGQaNXZNbq1I6E3sDxKXLwGnFFXZWmej4wlXCc9bWYrCLe7tkW3fIg0CSUpRAQAMyuOhhFta2ZFUWdUxxDuwwTYE7jSzFqZ2VeBg4Ap0S8R1wD/Y2YXmFlnMysws6PMbGJU91DC7SBXEX6NKHH3b7r7f5JaZ4iIiMhuysz2NLOzzayjmRWa2UnAGODfsWK3mFlrMzsa+BKhzwiAa4ELzWy8me0Z1dcL6BerP93zkW8CtwDDY9OZwClm1g0Ryapdmj6JyG6rFTCBcG9mFaGzqtPdfaGZjSL8QjEAWAOsBM5y948B3P1JM9sE3ADcA2wF5gM/jepeBYx090VNeDwiIiLSvDjh1o77CD+mfgD8l7v/b9SZ9wrCrR3LgS3AJe7+NoC7v2JmxxM69x4fNf4sJyQj7onqr/N8JBr1oy9wr7uvjq2abGaLCEmTX2XkaEUkJdOPmCJSFzM7H7jI3Y/KdSwiIiKy+4mSFI+4e69cxyIi2aXbPUREREREREQkL2Q1SWFmo81soZktMrPxKdZfYmbzzGyOmb1iZoOi5X3NbGu0fI6Z3ZfNOEVEREREREQk97J2u4eZFQLvACcS7gebDoxx9wWxMp3dfUP0+FTgUncfbWZ9gWfc/eCsBCciIiIiIiIieSebLSlGAovcfbG7bwceA06LF0gkKCIdCJ3liIiIiIiIiMhuKJtJip7Asth8ebRsJ2Z2mZm9B9xBGKM4oZ+ZzTazF6MhhkRERERERESkBcvmEKSWYtkuLSXc/V7gXjM7B7gROI8wfnFvd//YzA4F/mZmg5NaXmBm44BxAB06dDj0wAMPzPQxiIiINHszZ85c4+49ch3H7qB79+7et2/fXIchIiKSd9I9H8lmkqIcKInN9yKMaVyTx4DfALh7BVARPZ4ZtbQ4AJgR38DdJwITAUpLS33GjJ1Wi4iICGBmH+Q6ht1F37590fmIiIjIrtI9H8nm7R7TgQFm1s/MWgNnA5PjBcxsQGz2FODdaHmPqONNzKw/MABYnMVYRURERERERCTHstaSwt0rzexy4DmgEHjQ3eeb2a3ADHefDFxuZicAO4B1hFs9AI4BbjWzSqAKuMTd12YrVhERERERERHJvWze7oG7TwGmJC27Kfb4qhq2+wvwl2zGJiIiIiIiIiL5JZu3e4iIiIiIiIiIpE1Jiho8PXc5Nzw1L9dhiIiIiIiIiDSd3/0ObroJKitzsnslKWrw/prNPPr6UjZs25HrUERERERERESyb/NmuOEGeOklKCzMSQhKUtRgWEkx7jCvfH2uQxEREZFGMLPRZrbQzBaZ2fgU6y8xs3lmNsfMXjGzQbF134+2W2hmJzVt5CIiIk3sV7+ClSvhttvALCchKElRg2G9ugAwZ9knOY5EREREGioa0vxe4GRgEDAmnoSIPOruQ9x9OHAHcGe07SDCEOqDgdHArxNDpIuIiLQ469fD7bfDySfDkUfmLAwlKWpQ3L41/bp3UJJCRESkeRsJLHL3xe6+HXgMOC1ewN03xGY7AB49Pg14zN0r3P19YFFUn4iISMtz552wbh1MmJDTMJSkqMXwkmLmLPsEd6+7sIiIiOSjnsCy2Hx5tGwnZnaZmb1HaElxZT23HWdmM8xsxurVqzMWuIiISJNZsyYkKc48Ew45JKehKElRi2G9urB6YwUfrd+W61BERESkYVLdULvLrw/ufq+77wdcB9xYz20nunupu5f26NGjUcGKiIjkxO23h04zb70115EoSVGb4b27AjBXt3yIiIg0V+VASWy+F7C8lvKPAac3cFsREZHmZ/ny0GHm2LEwKLnbpqanJEUtDtqnE60LC9QvhYiISPM1HRhgZv3MrDWhI8zJ8QJmNiA2ewrwbvR4MnC2mbUxs37AAOD1JohZRESk6UyYAJWVcPPNuY4EgKJcB5DP2hQVctC+nZWkEBERaabcvdLMLgeeAwqBB919vpndCsxw98nA5WZ2ArADWAecF20738weBxYAlcBl7l6VkwMRERHJhvffhwcegIsugv79cx0NoCRFnYb36sITM8upqnYKC3IzTqyIiIg0nLtPAaYkLbsp9viqWra9Dbgte9GJiIjk0C23QFER3Hhj3WWbiG73qMPw3sVs2V7Fu6s25joUERERERERkcx46y14+GG49FLoucvgVTmjJEUdhvUqBmDOUt3yISIiIiIiIi3EzTdD+/YwfnyuI9lJVpMUZjbazBaa2SIz2+XIzewSM5tnZnPM7BUzGxRb9/1ou4VmdlI246xNv+4d6Ny2iLnlSlKIiIiIiIhICzB7NjzxBFx9NeTZ8NlZS1KYWSFwL3AyMAgYE09CRB519yHuPhy4A7gz2nYQofftwcBo4NdRfU3OzBhWUsxstaQQERERERGRluDGG6FrV/jud3MdyS6y2ZJiJLDI3Re7+3bCuOOnxQu4+4bYbAfAo8enAY+5e4W7vw8siurLiRElxbyzciNbtlfmKgQRERERERGRxisrgylT4NproUuXXEezi2wmKXoCy2Lz5dGynZjZZWb2HqElxZX13Hacmc0wsxmrV6/OWODJhpUUU+0wr3x91vYhIiIiIiIiklXucMMNsNdecMUVuY4mpWwmKVKN1+m7LHC/1933A64DEuOepLvtRHcvdffSHlm8j2ZYSeg8U/1SiIij8x/eAAAgAElEQVSIiIiISLP1r3/BCy/A9ddDhw65jialbCYpyoGS2HwvYHkt5R8DTm/gtlnVvWMbenVtx5xlSlKIiIiIiIhIM5RoRVFSAhdfnOtoapTNJMV0YICZ9TOz1oSOMCfHC5jZgNjsKcC70ePJwNlm1sbM+gEDgNezGGudhpcUM3eZbvcQERERERGRZujpp+H11+Gmm6BNm1xHU6OibFXs7pVmdjnwHFAIPOju883sVmCGu08GLjezE4AdwDrgvGjb+Wb2OLAAqAQuc/eqbMWajuElxTzzxkes2riNPTu1zWUoIiIiIiIiIumrrg4jegwYAOedl+toapW1JAWAu08BpiQtuyn2+Kpatr0NuC170dXP8ES/FMvWc+IgJSlERERERESkmfjzn2HePHj0UWjVKtfR1Cqbt3u0KIP37UJhgTFX/VKIiIiIiIhIc1FZCTffDEOGwNe/nuto6pTVlhQtSbvWhRy4dyd1nikiIiIiIiLNx+9/D+++C3/7GxTkfzuF/I8wjwwrKWZu+SdUV+8yGqqIiIiIiIhIfqmogFtvhZEj4dRTcx1NWpSkqIfhJcVs3FbJ4jWbcx2KiIiIiIiISO0mToSlS2HCBDDLdTRpUZKiHj7rPFO3fIiIiDQXZjbazBaa2SIzG59i/TVmtsDM3jCzf5lZn9i6KjObE02Tk7cVERHJW5s3h+TEccfBCSfkOpq0KUlRD/v16EjHNkXql0JERKSZMLNC4F7gZGAQMMbMBiUVmw2UuvtQ4Engjti6re4+PJqaRztZERERgHvugVWr4Lbbmk0rClCSol4KC4whPbswt1xJChERkWZiJLDI3Re7+3bgMeC0eAF3/4+7b4lmXwV6NXGMIiIimfXJJ3DHHfDFL8IRR+Q6mnpRkqKehvcu5q2PNrBtR1WuQxEREZG69QSWxebLo2U1+RbwbGy+rZnNMLNXzez0VBuY2biozIzVq1c3PmIREZHGuvNOWLcu3O7RzChJUU/DehWzo8pZ8NGGXIciIiIidUvVvjXlMF1m9g2gFPhpbHFvdy8FzgF+aWb77VKZ+0R3L3X30h49emQiZhERkYZbvRp+8Qs46ywYMSLX0dSbkhT1NKJ36DxzzlLd8iEiItIMlAMlsflewPLkQmZ2AnADcKq7VySWu/vy6O9i4AWg+Z3tiYjI7uX222HLljD0aDOkJEU97dW5LXt3bqt+KURERJqH6cAAM+tnZq2Bs4GdRukwsxHA/YQExarY8q5m1iZ63B04EljQZJGLiIjU14cfwq9+BWPHwkEH5TqaBinKdQDN0fCSYo3wISIi0gy4e6WZXQ48BxQCD7r7fDO7FZjh7pMJt3d0BJ6w0Pv50mgkj4OA+82smvDDzk/cXUkKERHJXxMmQHU13HxzriNpMCUpGmBYSTH/mL+CdZu307VD61yHIyIiIrVw9ynAlKRlN8Uepxw83t3LgCHZjU5ERCRDKirgoYfgvPOgX79cR9NgWb3dw8xGm9lCM1tkZuNTrL/GzBaY2Rtm9i8z6xNbV2Vmc6JpcvK2uTS8JOqXQrd8iIiIiIiISD6YPRu2bYOTT851JI2StSSFmRUC9wInA4OAMWY2KKnYbKDU3YcCTwJ3xNZtdffh0XRqtuJsiCG9umAGc3XLh4iIiIiIiOSDsrLwd9So3MbRSNlsSTESWOTui919O/AYcFq8gLv/x923RLOvEnrcznsd2xRxwJ6d1C+FiIiIiIiI5Idp06BvX9hnn1xH0ijZTFL0BJbF5sujZTX5FvBsbL6tmc0ws1fN7PRUG5jZuKjMjNWrVzc+4noYVtKFucs+wT3lUOsiIiIiIiIiTcM9tKQ44ohcR9Jo2UxSWIplKa/ozewbQCmhd+2E3u5eCpwD/NLM9tulMveJ7l7q7qU9evTIRMxpG17SlXVbdrB07Za6C4uIiIiIiIhky9KlsHy5khR1KAdKYvO9gOXJhczsBOAGwtjkFYnl7r48+rsYeAEYkcVY621YSRcA3fIhIiIiIiIiuTVtWvjbzPujgOwmKaYDA8ysn5m1Bs4Gdhqlw8xGAPcTEhSrYsu7mlmb6HF34Eggr8YlH7hXJ9q2KlCSQkRERERERHKrrAzat4ehQ3MdSaMVZatid680s8uB54BC4EF3n29mtwIz3H0y4faOjsATZgawNBrJ4yDgfjOrJiRSfuLueZWkKCosYEjPLhrhQ0RERERERHKrrAwOPxyKsnaJ32SyegTuPgWYkrTsptjjE2rYrgwYks3YMmF4STG/n/YB2yuraV2UzUYpjVe+bgubK6oYuHenXIciIiIiIiIimbJ5M8yZA9ddl+tIMiK/r6zz3LCSYrZXVrNwxcZch1Ird+fih2dy9sRpbNtRletwREREREREJFNmzICqqhbRaSYoSdEow0uKAZizbF2OI6ndy++uYf7yDazbsoP/nfNhrsMRERERERGRTEl0mvm5z+U2jgxRkqIReha3o3vH1sxZtj7XodTqNy+8x16d23Dg3p2YNHUJ7ilHghUREREREZHmpqwMBg6Ebt1yHUlGKEnRCGbG8JLivG5JMWfZJ0xb/DHfOqofFx7Zj7dXbGTa4o9zHZaIiIiIiIg0lntoSdFCbvUAJSkabVivYt5bvZkN23bkOpSU7nvhPTq3LWLMyN6cOnxf9ujQmklTl+Q6LBEREREREWmsRYtgzRolKeQzw3uHfineyMNbPt5bvYnnFqxg7Kg+dGrbiratCjlnZG/++dZKln68JdfhiYiIiIiISGOUlYW/o0blNo4MUpKikYb2CkmKueWf5DiSXU18cTGtCws4/4h+ny77xuf6UGjGH6YtyVlcIiIiTcnMRpvZQjNbZGbjU6y/xswWmNkbZvYvM+sTW3eemb0bTec1beQiIiJ1KCuDLl3goINyHUnGpJWkMLODsx1Ic9WlXSv69+jA7KX5laRYuWEbT83+kK+W9qJHpzafLt+7S1tOHrIPf56xjM0VlTmMUEREpH4acj5iZoXAvcDJwCBgjJkNSio2Gyh196HAk8Ad0bZ7ADcDhwMjgZvNrGvDj0BERCTDpk0Lo3oUtJz2B+keyX1m9rqZXWpmxVmNqBka3quYOcs+yatRMx585X0qq6sZd/R+u6y74Mi+bNxWyV9mlecgMhERkQZryPnISGCRuy929+3AY8Bp8QLu/h93T9wH+SrQK3p8EvC8u69193XA88Doxh+GiIhIBqxfD2++2aL6o4A0kxTufhRwLlACzDCzR83sxKxG1owM713Mmk0VfLR+W65DAWD91h388bWlnDJ0X3p3a7/L+kN6d2VYSTEPTV1CdXX+JFZERERq08DzkZ7Asth8ebSsJt8Cnq3PtmY2zsxmmNmM1atX1xGOiIhIhrz2WhjdY3dMUgC4+7vAjcB1wLHA3Wb2tpmdka3gmothUb8Uc5blxy0fj7z6AZsqKrn4mP41lrnwyL4sXrOZF9/VyZSIiDQfDTgfsVTVpCxo9g2gFPhpfbZ194nuXurupT169KjrEERERDJj2jQwg5Ejcx1JRqXbJ8VQM/sF8BZwPPBldz8oevyLLMbXLBy0T2daFxYwNw+SFNt2VDFp6vscc0APDu7ZpcZyJx+8D3t2aqPhSEVEpNlo4PlIOaHlRUIvYHmKuk8AbgBOdfeK+mwrIiKSE2VlMGQIdO6c60gyKt2WFL8CZgHD3P0yd58F4O7LCb9m7NZaFxUwaN/OzM6DJMWTM8tZs2k7lxxbcysKCDGP/VwfXnpnNYtWbWqi6ERERBqlIecj04EBZtbPzFoDZwOT4wXMbARwPyFBsSq26jngC2bWNeow8wvRMhERkdyqroZXX21xt3pA+kmKLwKPuvtWADMrMLP2AO7+cE0b7U5Dfg0vKWZe+Xoqq6pzFkNlVTUTX1rMsJJiRvXvVmf5cw7vTeuiAh4qe78JohMREWm0ep+PuHslcDkhufAW8Li7zzezW83s1KjYT4GOwBNmNsfMJkfbrgV+SEh0TAdujZaJiIjk1oIFsGEDjBqV60gyLt0kxT+BdrH59tGyGu1uQ34NLylm644q3s1hq4Rn31zB0rVb+M6x/TFLdRvtzrp1bMOpw/blLzM/ZP3WHU0QoYiISKPU+3wEwN2nuPsB7r6fu98WLbvJ3RPJiBPcfS93Hx5Np8a2fdDd94+mSRk+HhERkYYpKwt/d+OWFG3d/dOr7+jxrsNG7Gy3GvJreEnoPDNX/VK4O/e9+B79e3TgC4P2Tnu7C47sy9YdVTw+fVndhUVERHKrIecjIiIiLc+0adCjB+y3X64jybh0kxSbzeyQxIyZHQpsrWOb3WrIrz7d2lPcvlXORvh4+d01zF++gYuP6U9BQd2tKBIG79uFkf324PfTllCl4UhFRCS/NeR8REREpOUpKwu3eqTRgr65STdJ8V+E+zRfNrOXgT8T7u+szW415JeZMaxXcc6SFPe9+B57dW7D6SNqywOlduGRfSlft5XnF6zMQmQiIiIZ05DzERERkZZlzRp4550WeasHQFE6hdx9upkdCAwkJBDedve6OjGo75BfxyYN+XVc0rYvpBNrLg0rKeZX/36XzRWVdGiT1lObEXOXfULZex9z/RcPpE1RYb23P3HQ3vQsbsekqe8z+uD0bxURERFpSg08HxEREWlZXn01/G2hSYp0W1IAHAYMBUYQOsH8Zh3ld7shv0aUFFPt8OaH65t0v/e9+B6d2xYxZmTvBm1fWGCcd0QfXnt/LfOXN23sIiIi9VTf8xEREZGWpawMioqgtDTXkWRFWkkKM3sY+BlwFOHk4DDC7Rk12h2H/BraqwtAk97y8d7qTfxj/grGjupDp7atGlzP10t7065VIQ9NXZK54ERERDKoIecjIiIiLU5ZGYwYAe3a1V22GUr3noRSYJC716tnRXefAkxJWnZT7PEJtWz7IPBgffaXa906tqH3Hu2ZW950SYoHXlpMq8ICzj+iX6Pq6dK+FWce2pPHZ5Qz/uQD6daxTYYiFBERyZgGnY+IiIi0GDt2wPTp8O1v5zqSrEn3do83AXVWkIZhJcXMWdo0SYqVG7bx11kf8rXSXvTo1PikwvlH9GV7ZTWPvrY0A9GJiIhknM5HRERk9/bGG7BlSxjZo4VKN0nRHVhgZs+Z2eTElM3AmqvhJcUsX7+NVRu2ZX1fD77yPpXV1Yw7OjNj4+6/ZyeOHtCdh1/9gB1V1RmpU0REJIN0PiIiIru3srLwt4V2mgnp3+7xg2wG0ZIML/msX4ovDM7ejz3rt+7gj68t5ZSh+9K7W/uM1Xvhkf244KHpTJn3EacNr/9wpiIiIln0g1wHICIiklPTpkHPnlBSUnfZZiqtlhTu/iKwBGgVPZ4OzMpiXM3W4H27UFRgWe+X4pFXP2BTRSUXH9M/o/Uee0AP+nfvwCR1oCkiInlG5yMiIrLbKytr0a0oIP3RPb4NPEkYLhSgJ/C3bAXVnLVtVciB+3TK6ggf23ZUMWnqEo45oAcH9+yS0boLCozzjujLnGWfMHvpuozWLSIi0hg6HxERkd3a8uXwwQdKUkQuA44ENgC4+7vAntkKqrkbXlLMG8vWU12dnc7Hn5xZzppNFVxybGZbUSSceWgvOrUpUmsKERHJNzofERGR/FNeDv/6V/b3M21a+NuCO82E9JMUFe6+PTFjZkWAhv+qwbBexWysqGTxmk0Zr7uyqpqJLy1mWEkxo/p3y3j9AB3bFPG1w0qYMu8jVqzPfgegIiIiadL5iIiI5JcVK+Doo+HEE2HRouzuq6wM2rSBESOyu58cSzdJ8aKZXQ+0M7MTgSeAp7MXVvM2oncxAHOWrc943c++uYKla7fwnWP7Y2YZrz/hvFF9qXLnkVc/yNo+RERE6knnIyIikj82bYIvfQlWrYKiIrjrruzur6wMDjsMWrfO7n5yLN0kxXhgNTAPuBiYAtyYraCau/7dO9KxTRFzlmW2Twd3574X36N/jw58YVB2h4nv3a09nz9wLx59fSnbdlRldV8iIiJpatD5iJmNNrOFZrbIzManWH+Mmc0ys0ozOytpXZWZzYkmDXcqIiJBZSWcfTbMng1//jOccw48+CCsy1K/ftu2waxZLf5WD0h/dI9qd3/A3b/q7mdFj9W8sgYFBcbQXl2Ym+GWFK8sWsP85Ru4+Jj+FBRkrxVFwoVH9mXt5u1Mnrs86/sSERGpS0POR8ysELgXOBkYBIwxs0FJxZYC5wOPpqhiq7sPj6ZTM3AYIiLS3LnDFVfA3/8O994bWlNcfTVs2QIPPJCdfc6aBdu3t/hOMyH90T3eN7PFyVO2g2vOhpcU89ZHGzLaCuE3L7zHXp3bcPqInhmrszaj9uvGwL06MWnqEpSTEhGRXGvg+chIYJG7L476s3gMOC1ewN2XuPsbQHWWQhcRkZbk9tvhvvtg/Hi45JKwbNgwOP54uPtu2LEj8/vcTTrNhPRv9ygFDoumo4G7gUeyFVRLMKykmMpqZ/7yDRmpb+6yTyh772O+dVQ/2hQVZqTOupgZFxzZl7c+2sBr769tkn2KiIjUoiHnIz2BZbH58mhZutqa2Qwze9XMTk9VwMzGRWVmrF69uh5Vi4hIs/Poo/D978OYMXDbbTuvu+Ya+PBDeOKJzO+3rAz694e99sp83Xkm3ds9Po5NH7r7L4HjsxxbszaiJNF55icZqe++F9+jc9sixozsnZH60nX6iJ50bd+KSVPfb9L9ioiIJGvg+Uiq+yPr0zywt7uXAucAvzSz/VLENdHdS929tEePHvWoWkREmpUXX4QLLoBjj4VJk6Ag6XL65JNh4ED4+c/DLSGZ4h6SFLvBrR6Q/u0eh8SmUjO7BOiUxna7bUdVe3Zuyz5d2jI3A0mKxas38Y/5Kxg7qg+d2rbKQHTpa9uqkDEje/P8gpUsW7ulSfctIiIS18DzkXKgJDbfC0i7syV3Xx79XQy8ALTscd9ERCS1BQvg9NNhv/3gqafCUKDJCgpC3xSzZsHLL2du3x98EIY63U2SFEVplvt57HElsAT4Wm0bxDqqOpFwgjDdzCa7+4JYsURHVd9LUcVWdx+eZnx5aXhJcb1aUrg7FZXVbKqoZNO2SjZVVLJxWyUPv7qEVoUFnH9EvyxGW7Oxo/pw/0uL+cO0JdxwSnJfYyIiIk2m3ucjwHRggJn1Az4Ezia0iqiTmXUFtrh7hZl1B44E7qhv0CIi0sx99FFoJdG2LTz7LHTtWnPZsWPhhhvgzjvhmGMys/+ysvB3N+iPAtJMUrj7/2tA3Z92VAVgZomOqj5NUrj7kmhdi+yoalhJMc++uYL7XnyPih3VbKrYERIQFVVs2rbj0yREWFbJ5opKdlSlbhZ04ZH96NEpRbauCezTpR0nH7w3j01fxn+dcAAd2qSb2xIREcmchpyPuHulmV0OPAcUAg+6+3wzuxWY4e6Tzeww4CmgK/BlM7vF3QcDBwH3R+cpBcBPkn5sERGRlm7TJjjlFPj443C7R58+tZdv3x6+853QX8WiRbD//o2PoawMOnaEgw9ufF3NQFpXm2Z2TW3r3f3OFItTdVR1ePqhhY6qCL+U/MTd/1aPbfPCEft1A+Anz74NQLtWhXRsW0SnNkV0aFNExzZFlOzRnk5tiujY9rNlndqGvx3ahLKd27Vi8L6dc3koXHBkX5554yP+OqucsaP65jQWERHZPTXwfAR3nwJMSVp2U+zxdMJtIMnblQFDGhSsiIg0f5WV8LWvwdy58PTTcOih6W132WVwxx1w111wzz2Nj2PaNDj8cCjaPX4sTvcoE71pJ/qG+DLwEjsnIZJloqOq5WbWH/i3mc1z9/d22oHZOGAcQO/eTduhZDqG9ipm9v+cSEGB0aF1IUWF6Q6mkn8O6d2Vob26MKlsCece3oeCglQvr4iISFY15HxERESk/txDi4hnn4X774cvfjH9bffeO4z+8eCDcOuttd8eUpdNm0KS5Pvfb3gdzUy6V83dgUPc/bvu/l3gUKCXu9/i7rfUsE3WO6pqDr1pd+3Qmi7tWjXrBAWE4UjHHdOfxas3c/Pk+Xgme6sVERFJT0POR0REROrvxz+G3/4Wrr8exo2r//ZXXw1btsDEiY2LY/p0qKrabTrNhPSTFL2B7bH57UDfOrb5tKMqM2tN6KgqrVE6zKyrmbWJHic6qtI9oDn2paH7cvGx/Xn41Q+48/l3ch2OiIjsfhpyPiIiIlI/jzwSOr885xyYMKFhdQwbBp//PNx9N2zfXnf5mkybFv5+7nMNr6OZSfd2j4eB183sKcItG18B/lDbBuqoqmUaP/pA1m/ZwT3/XkSXdq246Oj+uQ5JRER2H/U+HxEREamXf/8bLrwQjjsu3K5hjbjN/ZprQqebTzwB557bsDrKyuCggxp3y0gzY+k22zezQ4Cjo9mX3H121qJqgNLSUp8xY0auw9gtVFU7V/xpFlPmreCOs4bytdKSujcSEZGcMbOZ7l6a6zgyQecjIiKSNW++CUcdBT17wtSpUFzcuPqqq2Hw4DDix4wZ9U94uEP37vCVr4RbT5q5dM9H6tNRQntgg7vfBZRH443LbqiwwPjF14dz9IDujP/LGzw3f0WuQxIRkd2HzkdERCTzli8PnWO2bx86y2xsggKgoCD0TTFrFrz0Uv23f+cdWLsWRo1qfCzNSFpJCjO7GbgOSHQp2gp4JFtBSf5rU1TIfd84lGElxVzx6GymLlqT65BERKSF0/mIiIhkxcaN4baMtWvh73+HTI4cOXYsdOsGv/hF/bctKwt/d6NOMyH9lhRfAU4FNsOnI290ylZQ0jx0aFPEpPMPo1/3Doz7wwzmLvsk1yGJiEjLpvMRERHJrB074KtfhXnz4MknYcQug0o2Trt2YSjTyZPh3Xfrt+20aaEvioEDMxtTnks3SbHdQ+cVDmBmHbIXkjQnxe1b8/C3RtKtYxvOn/Q6767cmOuQRESk5dL5iIiIZE51NVx8MTz3HNx3H4wenZ39XHYZtGoFd91Vv+3KysKoHgX16aWh+Uv3aB83s/uBYjP7NvBP4IHshSXNyZ6d2/LItw6nqLCAsb97nWVrt+Q6JBERaZl0PiIiIplRXQ2XXAKTJsFNN8FFF2VvX3vvHYYznTQp3FKSjk8+gfnzd7tbPSDNJIW7/wx4EvgLMBC4yd3vyWZg0rz07taeh781ki3bKxn7u9dYvbEi1yGJiEgLo/MREWlyH3wA69fnOgrJtESC4oEH4Prr4Qc/yP4+r74atmyBiRPTK//aa+GvkhS7MrNCM/unuz/v7v/t7t9z9+ebIjhpXg7cuzOTLhjJyg0VfPPB11m/dUeuQxIRkRZC5yMi0uQefTT0BTB4MLzySq6jkUyprg59RCQSFBMm1H9o0IYYOhROOAHuuQe2b6+7fFlZuM1j5Mjsx5Zn6kxSuHsVsMXMujRBPNLMHdqnK/ePPZRFqzZy0e+ns3V7Va5DEhGRFkDnIyLSZKqrw8XruefCYYdB27Zw3HHws5+Be66jk8ZIJCgmToTvf7/pEhQJ11wThjp9/PG6y5aVhcRGx47ZjyvPpNsnxTZgnpn9zszuTkzZDEyar2MO6MFdZ49g5gfr+M4fZ7K9sjrXIYmISMug8xERya6NG+GMM+DHP4Zx4+Bf/4KZM+H00+G//xtOOw3Wrct1lNIQ1dVw6aWfJShuu61pExQAJ50EBx4Id95Ze8Krqirc7rEb3uoB6Scp/g78D/ASMDM2iaT0xSH7cNtXhvDCwtV894m5VFUr69wc/On1pZxy98uUr1PnpyKSl3Q+IiLZs2QJHHkkPPNMaJJ/333QujV06QJPPBFGZvjHP+CQQ2D69FxHK/WRSFDcfz+MH5+bBAWE2zeuvhpmz4aXXqq53Pz5IWE2alTTxZZHak1SmFlvAHf/faqpaUKU5mrMyN6MP/lAnp67nJsnv4mreVze2l5ZzQ1PzeP7f53H/OUb+M0L7+U6JBGRTzX2fMTMRpvZQjNbZGbjU6w/xsxmmVmlmZ2VtO48M3s3ms7L3FGJSF556aVwa8eyZfDss3D55TtfxJrBlVfCyy+HC96jjoJ779XtH81BdXUYAjSRoPjRj3KToEgYOxa6dQutKWpSVhb+qiVFSn9LPDCzv2Q5FmmBLjl2Py45dj8eeXUpdz7/ToPr2bK9kjfKP+GJGcv40ZS3uGDS6zzw0uIMRrr7Wr2xgm/89jX++NpSLj62P18vLeGJGeWs3LAt16GJiCQ0+HzEzAqBe4GTgUHAGDMblFRsKXA+8GjStnsANwOHAyOBm82sa32DF5E899vfhg4Nu3ULTexPPLHmsocfDrNmhfKXXw5nnw0bNjRdrFI/iQTFfffBddflPkEB0K5daNXx9NPwTg3XR9OmwV57Qb9+TRtbniiqY338FeyfzUCk5bpu9EDWb93OPf9eRJd2rbjo6JrfShWVVSxevZl3Vm7knZUbWbhiE++s3MiydVs+TVS3Lipgr85t+M/C1bQqNM4/cvf88GbCvPL1jHt4Buu2bOeus4dz2vCeLP14C0/OKueBlxZz45eSz+NFRHKiMecjI4FF7r4YwMweA04DFiQKuPuSaF1yJ0onAc+7+9po/fPAaOBP9YxBRPJRZSV873vhNo6TToLHHoPi4rq369YtXGD+9Kdwww2h6f6TT4ZODiV/VFeHRFIiQfHjH+c+QZFw6aVw++3hvXfvvbuuLysLt3rkS7xNrK4khdfwWCRtZsaE04ewYWslE/7+Fp3bteKMET1Z8vGWT5MRISGxkSUfb/m0/4qiAqNf9w4M6dWFsw7txQF7deKAvTrSp1sHAC55ZCa3PLOAfYvb8YXBe+fyEJulv83+kOv+8gbdO7b5/+2dd3hU1daH352EBELvJST03iFUBRGxoYKoSFWsiIKKeq8NK6hXwXsVlSYoIiqgIkXlAwVROoTeawgQQg01gfT9/bEnEmJmMmdKZpKs93nmyZlyfmedkzVn9llnrazjq6QAACAASURBVLX5cWgnmoaZhvkR5UPp2aIa3647wlM31qVc8WAfWyoIguDWeCQMOJrleSwmM8LVdcOyf0gpNQQYAhAREWHRPEEQfML589C3L/z2G4wYYQIOQbldGmUhIMBc+HbsaLIp2rc3F5uPPOI9m/2BY8dg1CgT4BkzxgRs/BGtTYBi4kT/C1AAVKliZo+ZNg1Gj4Zy5a6+d+oUHDhgGrcWUnL7JrZQSl3E3MEoZlvG9lxrrUs5WlkpdRswDggEpmqt38/2fhfgY6A50E9r/WOW9wYDr9meviM9MPI3gQGKj/q25GJSKi/P2cZrc3eQkm5uWCkFNcqFUr9ySXo0q0q9yiVpULkktSoUJzjIfkXSJ/1a0W/KWp6ZtZmZj3egVYRk4DpDWnoGHyzaw5QVh2hXqxwTBramQomQaz7zVNc6zN18jGmrDvHCLQ18ZKn7rI2OZ+qKQ/znnmZULBmS+wqCIPgr7oxHchqVOhvocGpdrfXnwOcAkZGRclNHEPydffvgrrvg0CFT6vHoo65rdeliMikGDjQ6y5ebYEXx4p6z1x+4dMkEJf77XzPzhNbw66+mz0OvXr627lq0NiUeEyfCiy/6X4Aik+eeM0GKyZPNbCOZrF1r/hbSfhSQS5BCax3oqnCWGtCbMXceopRSC7TWu7J8LLMG9F/Z1s2sAY3EDAY22taV+X7yMcFBAUx+oA0fL9mPAupXLkmDKiWpU7EExYKtu1qx4EC+GBzJPRNW89j0Dfz0VKe/syyEnDl/OYWnZ25mxf4zPNixBq/f2Zgigf8MBNWrXJLbmlThq9UxPN6lNqWKFvGBte6RnqF5Y/4O9p1M4MRXV5g1pCMlQizcIREEwW9wZzyCGYOEZ3leHYizsG7XbOv+6YYtgiD4mt9/h/vvN1kTS5dC587ua1auDIsXmzvio0bBhg2m/KNhQ/e1fU1qKkyZAm+9BadPm6yR994zQYvBg83UrAMHwiefXJsN4CuyZlC8+CK8/75/BigAmjUzvU0+/RReeMHMJAOm1KNIEWjTxrf2+RBnpyB1hb9rQLXWKUBmDejfaK1jtNbbALs1oLbARGYNqJDPCQ0O4tUejXilRyPubVOdpmGlXQpQZFKhRAhfPdyWdK15aFoU5xJTPGhtwWLviUv0/GwVa6Pjef+eZozq1TTHAEUmw26sy6WkNGasOZyHVnqOX7bFse9kAgPaR7D7+CWemLGB5LR0X5slCELeEwXUU0rVUkoFA/2ABU6uuxi4RSlV1tYw8xbba4Ig5De0NhfSt98O4eFmClFPBCgyCQw0F/KLFsHJkxAZCTPzcfsarWHePHMhPWwYNGoE69ebfapVy/TfWL/e7PPs2dCkienT4Wubhw+HCRPg3//27wBFJs8/D8ePw/ffX31t9WozzW3Ror6zy8d4M0jhVB2nO+sqpYYopTYopTacPn3aZUOF/E3tiiWY+mAkx85f4fGvN5CUKhei2Vm04wS9J6ziSmo6s4Z0pF+73Gumm1UvzQ31K/LlykNcSclfxzQtPYNxS/bTsEpJ3unVlA/ubc6qA/G88P1WMjIkE1sQChNa6zRgOCa4sBv4Xmu9Uyk1SinVE0Ap1VYpFQv0ASYrpXba1j0LjMYEOqKAUZlNNAVByEekpMATT8Czz8Idd8CqVVCzpne2dcstsGULtGwJAwbAk09CUj6bMW3dOrjhBujd21zkz58Pf/5ppmjNSpEi8OabJlhRsSL07GmyK875IPlda3j66asBig8+8P8ABZiGrY0amelItTaZK1FRptdJIcabQYo8qQHVWkdqrSMrVqxoyTihYBFZsxwf923JxiPn8vRCNCk1nV+3Hefo2ct5sj2rZGRo/vf7PoZ+s5F6lUvy8/DraVPD+d4dw7vVJT4xhZnrj3jRSs8zb0sc0WcSGdG9PgEBivvaVOel2xryy7bjjPplF1rmNBeEQoXWeqHWur7Wuo7W+l3ba29orRfYlqO01tW11sW11uW11k2yrPul1rqu7THNV/sgCIKLnDljphSdMsXU/c+dCyVLenebYWGwbJm5WJ40Ca67DqKjvbtNTxAdbZqJduhg+nZMmgTbt5vgg6ML/latTInL66/Dt99C06awcGHe2Z0ZoBg/Pn8FKMA0YH3uOdPX5K+/TIArKalQ96MA7wYp3K0BdXVdoZDSo1lVRvZoxK/bj/P+oj1e397eE5fo9dkqhn23ic5jljFo6jp+2RbnNyUFl5JSGTJjI58s3c99baoze0gHqpS2ljbWtmY52tcqx+TlB/1mv3IjNT2DcUv30TSsFLc2qfz360NvqM0j19Xiq9UxTPzroA8tFARBEAQhT9ixw9z9X7fOXDy/9565KMwLihQxjSbnzzcX/9ddB2f9NBErPt5cKDdsCL/8Am+8Afv3m+wTZ2c8CQ42/TjWrYOyZU3GyiOPmFlUvInW8MwzJkDxr3/lrwBFJoMGQYUKJptizRrzmmRSeA2pARXynEevr8VDnWry+fJopq+O8co2tNZMXx3DXZ+tJD4xhU/6t+K57vU5dCaR4d9tpsN7Sxn9yy72n7zkle07w6EzifSesJple0/x5l2NGXtfc4oWca33x/BudTl5MZk5G4952Erv8MOGWI6evcLzN9dHZfmRUkrx2h2N6NmiGmMW7eX7DUcdqAiCIAiCkK/5/XdzoZecbGbcGDDAN3b07Al//GGaTr70km9ssEdSkpl6tU4d069j8GATnHj7bdezTdq0gY0b4dVXYfp0k1WxaJFn7QYTnDhwwJTTfPaZCVCMGZP/AhQAxYrBU0+Znh4zZpieKdWr+9oqn6K8mfaslOqBmWI0EPhSa/2uUmoUsEFrvUAp1RaYC5QFkoATmSmWSqlHgFdtUu/mlmIZGRmpN2zY4K1dEfIR6Rmaod9sZOnuk0wa1IZbmlTxmHZ8QjIv/riNpXtOcWODiozt0+Lv6TvTMzQrD5xhdtQRft91ktR0TZsaZenbNpw7m1clNDhvZpb4c+8pnp65maAAxfgBrelUt4Jbelpr7h6/inOXU/njhRsIctBs09ckp6Vz49g/qVSqKHOf6nRNkCKTlLQMHp0exeqD8Uwe1IbujSvnoCQIBQul1EatdaSv7SgMFNjxiNaQkOD9NHlB8AQzZ5oL7kaNzDSZ/nDB9+KLJiCwfLlnG3a6QkaGOUYjR8Lhw9Cjh8lAaNrUs9uJioKHHoJdu+Cxx8z0paUczRjtgIwM2LnTHL8VK8zf48fNe/k5QJHJyZMQEWH6p/TtC7Nm+doir+DseMSrQYq8pMAOCgSXuJKSTr8pa9l74iKzhnSkZXgZtzWX7zvNCz9s5cKVVF69vSGDO9XM8SIY4ExCMnM3HWNm1BGiTydSIiSIni2r0a9tOM3CSttdzx201kxeHs0Hi/bQoHJJpjwYSXi5UI9o/7bzBENmbOSjvi3o3coPfujtMH11DG8u2Mk3j7bn+nr2gzMJyWkMmLKWvScu8d3j7WlTww+mzBIELyJBirwjX49HUlPNBcvBg+YRHX3tcmKiaaQ3caKZclEQ/JGPPjIzJtxwgym1KF3a1xYZEhNNEKBoUdN3ICTEN3YsX25KOzZtMr0kPvwQunXz3vaSkswMIGPHml4dX3xheoTkRlqa6dOwfPnVwERmQ87q1c3/t3Nn87cgTPUKpjxm2jQYN86UsBRAJEghFHrOJCRzz4TVJCanMfep64go79oFe3JaOh8u3suUFYeoV6kEn/RvRaOqzkWBtdZsOHyOWeuP8uv2OJJSM2hUtRT924XTq0UYpUOLWLJFa825y6kcOXuZI2cvc/TsZY7Em+XD8YnEXUjijmZVGdunuUczNzIyNLePW0GG1iwe0YWAAP+LVCelptNlzDJqVijO7CEdcg0EnUlIps+kNZxNTOGHoR2pX1nuDgoFFwlS5B1+Px65eDHnIMTBg3DkiLlbmUnRolC7tkkFr1PH1KZ/+imUKGHSq/v2zd93LoWChdbw8svmjvq998I33/jfFI6LFpkpUN96y8yKkdesXg1duphgwbvvmhKYvOrRsW6dyarYs8f0uhg79trMrKQkk3mRGZRYvdpkbwHUq2fsznzUqFEwzz1798J995mpX+vU8bU1XkGCFIIARJ9O4J6JqykXGsycJztRtniwpfUPnErg2Vmb2Rl3kQc61GDkHY1c7u1wMSmV+VvimB11hB3HLhISFECPZlXp2zac9rXK/X1RnZSazrHzV/4RhDhy9jKx566QkJx2jW6FEiFElCtGRLlQ2tUqT/924V7J1Ji/5RjPztrCpEGtua1pVY/ru8vUFdG88+tuZg/pQPva5Z1a5+jZy9wzcTVBAYo5T3aiWpliXrZSEHyDBCnyDr8cj2zZAsOHmwHwmTPXvlehghkMZw1GZD6vWvWfFzB79pgLjXXr4J57zHR/klUh+JrUVFNO8PXXpkfBp59CoGvjNa8zYADMmQNbt+ZtBsC5c2Za1MBAk6HgiwyTK1dMcObDD01pw5tvmgDp8uXmnJKSYj7XrNnVgETnzuZcJBQIJEghCDaiYs4ycOo6moeV5pvH2jsVZNBaMzvqKG//vIuiRQIYc18LbvZg74Idxy4wO+oo87Yc41JSGrUqFKdiiRCOnL3MyUtJZP1ahgQFEFEulIhyoYT/42+xPOt1kZ6huem/f1KiaBA/D7/eK4EQV0lMTqPLmGU0qlqKbx5rb2ndnXEX6Dd5LZVLF+XHoR0pE2otkCUI+QEJUuQdfjceSU83sxvExppSjewBCVfqw9PTTRf61183WRXjx8P99xfMO5uC/5OYCH36wP/9n5ld4rXX/NsXT540wYnmzc00pXmRyaC1uUO/YAGsWgXt2nl/m45YvRoefthMcxoYCK1bXw1KXH89lJMy3IKKBCkEIQu/bjvOsO82cUezqnzav5XDcoXzl1N4ec52Fu08wfV1K/Df+1tQuZR30gWvpKTzfzuOM2dTLKlp+u/gQ0T5Yn8HIyqWCPGbgMDsqCO8NGc7Xz3clq4NKvnanL+Z8OcBxizay09PdaJ1RFnL6685GM/gL9fTNKwU3z7WgWLBfnr3RRBcRIIUeYffjUe++MLcYf7uO+jf37Pau3ebC43MrIqJE6GS//w2CIWAM2fgzjtNmcDEiTBkiK8tco7M7+XUqfDoo97f3qRJJsNk7FjTZNIfuHLFZJM0bWqCnUKhQIIUgpCNKcujeXfhboZ0qc2rPRrl+Jk1B+N5bvYW4hOT+fetDXjs+tp+2X/BV6SkZdB17DKqlSnGD0M7+kXw5GJSKp0/WEbriDJMe9j1OwP/t/04T323iW4NKjH5gTZ+PYuJIFhFghR5h1+NRy5eNLXcdevCypXeubuclmayKt54w1xoTJhgsioEwdscPgy33goxMWYmhLvv9rVFzqM1dO0K27aZEipvlkxt22YyJ7p2hYUL864HhSDkgLPjEfFSodDwWOdaDO5Yg8+XRzN9dcw176WmZzB28R4GTF1LaHAgc5+6jiFd6kiAIhvBQQE8cUMdNhw+x7pDZ31tDgBfrjzEhSupPH9zA7d0bm9WldG9mrJ0zyle+Wk7BSWAKwhCIeadd+DUKdMp3ltB5aAgM7Xipk2mfKRvX5N6f+qUd7YnCADbt0OnTnDiBPz2W/4KUID5Pk6eDJcvm5k2vEViIvTrB2XLmn4dEqAQ8gniqUKhQSnFG3c1oXujyrz9805+23kCgMPxidw3aQ3jlx2kb2Q4vzxzPU3D/GS6Kj+kb9twKpQIYfyyA742hfOXU/hixSFuaVyZZtXd/58N6lCDZ26qxw8bYxm7eK8HLBQEQfAR+/fDxx+bcozIPEiiadzY1Lq//76pe2/SBL7/3vvbFQofK1aYZoqZy126+NYeV2nYEF59FWbONLN+eINnnzWZGjNmSCmWkK+QIIVQqAgMUHzavxXNqpfhmVmbGbdkPz3GreDQ6QQmDGzN+/d6durOgkjRIoE81rkWK/afYevR8z61ZcqKaC4lp/HczfU9pvlc93r0bxfBhD8P8uXKQx7TFQRByFNeeAFCQuC99/Jum0FB8NJLZuaAWrUkq0LwPPPmwc03Q5Uqpvlis2a+tsg9Xn7ZBCuefNJkPXiSWbNM74tXXoHu3T2rLQheRoIUQqGjWHAgXwyOpGLJED5aso8mYaVZNKILPZrJ9EbOMqhDDUoXK8JnPsymiE9IZtqqGO5sXpVGVV3oTm8HpRTv3N2UW5tUZtQvu1iwNc5j2oIgCHnCb7/Bzz+bWQ6qVMn77TdubC4g//Ofq1kVP/yQ93YIBYvPP4d77zXTaK5cCTVq+Noi9wkJMfsVEwNvv+053eho00S0Y0d46y3P6QpCHiFBCqFQUqFECDMf78D/7m/BzMc7UK1MMV+blK8oERLEQ51q8vuuk+w5cdEnNkxeHk1SajojunsuiyKTwADFuH6taFerHC98v4WV+894fBuCIAheITXV1LjXqQMjRvjOjqAgc5d40yaoWdM007z/fjh92nc2CfkTrc3Uok88YRplLl0KFSr42irP0bmzmenjf/+DLVvc10tJMX0oAgPNrD5FirivKQh5jAQphEJL9bKh3NO6OoHSHNMlHr6uJsWDA5mw7GCeb/vUxSS+XhPD3S3DqFvJO9NWFS0SyJQHI6lTsQRPzNjAzrgLXtmOIAiCR5k0CXbtgg8/NHdpfU2TJrBmjSk7mT/fPJ8/39dWCfmF9HQYNgzefBMGDza+U7y4r63yPGPGmMDL44+bfXaH114zU7JOnWoChIKQD5EghSAILlEmNJhBHWrwy7Y4Ys54uI4yFyb8eZDUdM0zN9Xz6nZKFyvC9EfaUapYEYZ8vZH4hGSvbk8QBO+glLpNKbVXKXVAKfVyDu+HKKVm295fp5SqaXu9plLqilJqi+0xKa9tt0R8vLmYu+km6NXL19ZcJSjI1MVv3AgREWYmhpdeMtOXCoI9kpJMX5OJE42/TJtWcLMCypY1jW43bIDx413XWbQIxo6FoUNNaYwg5FNUQZlmz6/mJReEQsKpS0lc/8EyercM44P7mufJNuPOX6Hr2D/p3Srvtrkt9jx9Jq2hRXgZvn2sPUUCJb4r5C+cnZe8IKKUCgT2ATcDsUAU0F9rvSvLZ54Cmmuthyql+gG9tdZ9bcGKX7TWTZ3dnk/HI8OHmwu6LVv8t6FgcrIpR5k4EW680cxsULmyr60S3CEuzgSgsj7OnYPQ0KuPYsWufe7M61OmwJ9/wkcf+bZ0Ka/QGu64w8xYsmsXhIdbW//4cWjRwnyf1q83x1AQ/AxnxyNencZAKXUbMA4IBKZqrd/P9n4I8DXQBogH+mqtY2yDgt1A5hyAa7XWQ71pqyAI1qlUsij92oYzc/0Rnulej7A86O3x2bIDaDRP31TX69vKpHn1Mnxwb3NGzN7C6F92MaqX09crgiD4nnbAAa11NIBSahbQC9iV5TO9gLdsyz8Cnyml8lct4I4dptRj6FD/DVCAKUGZMAE6dDA9Blq3hh9/NA3+BP9G65wDEifMlO4EBJiZKrp3NxfKV67A5ctX/2Y+4uP/+fqVK//cXlAQfPstDBiQt/vpK5Qy340mTUyJy/z55jVnyMiABx6AhAQT2JEAhZDP8VqQwnbnYjxZ7lwopRZkvXMBPAqc01rXtd25+ADoa3vvoNa6pbfsEwTBMzxxQx2+W3eEz/86yNtevng/evYy30cdpV+7cKqXDfXqtrJzd6swdsZdYMqKQzSpVoq+bSPydPuCILhMGHA0y/NYoL29z2it05RSF4DytvdqKaU2AxeB17TWK7JvQCk1BBgCEBHhg3OD1uZOc6lSpsFgfuDBB81d33vugRtuME0Dhw1z/qJM8C5aw7Fj/wxInDxp3g8IgEaN4JZboE0b82jZ0vV+ERkZprwja/CidGnfzE7jS2rWNLN8/Pvf8NNPzpdsfPCBaSg6daqZXUcQ8jnezKQoHHcuBKGQE1amGPe0DmNW1FGGd6tHxZLea9T2ydL9BAQoht/o3V4U9njptobsOXGJ1+btoG6lErSpUc4ndgj+R1p6BofOJJKQnEax4ECKFbE9bMtBUiLkS3IaV2SvdbX3meNAhNY6XinVBpinlGqitb5mWiOt9efA52DKPTxgszUWLDAXKOPGQfnyuX/eX2jRwtTgP/ggPP00rF0LkycXzMaI/obWcOGCyYI4efLq37g42LbNBCROnTKfDQgwF7633XY1INGihWf/TwEBV0s9CjsjRpgMkqefNlkppUs7/vzq1fD666Z/xyOP5I2NguBlvBmkKPh3LgRBAODJrnX5cWMsU1dG88rtjbyyjUNnEvlp8zEe7FiDKqWLemUbuREUGMCn/VvRa/wqhn6ziZ+HX+8zWwTfkZyWzr4TCeyIu8DOuAvsOHaRPScukpSaYXedIoGKokUCCbUFLYraAhhZn/+9HBzIS7c2JEBmHvIUsUDW4u7qQJydz8QqpYKA0sBZbRp3JQNorTcqpQ4C9QH/aYKVnAwvvGDuaj/5pK+tsU7Zsiat/T//MRdaW7eaO8j1fBOMztdoDZcu/TPwkPk3+2spKf/UCAoyvtSjx7UBCQke5B1BQaYfR/v2puHshAn2P3vuHPTvbxrSTp4smUhCgcGbQYqCf+dCEAQAalUozh3Nq/HNmsM8eUMdyoQGe3wb45bsIzgwgCe71vG4thXKhAYz5cFIeo9fxRMzNjD7iY4ULRLoU5sE75GQnMbu4xfZcewCO+PM3wOnEkjLMD85JUOCaFytFAPb16BJtVKULR5MUko6V1LTuZySTlJqOldS0rls+5uU+s/3ziWmXvN6anqG14J9hZQooJ5SqhZwDOgHZC9yXwAMBtYA9wF/aK21UqoiJliRrpSqDdQDovPOdCcYNw4OHjRd/fPrzAcBATByJLRta/oPREbC9OlmFhDBOX76ydxFv5DDdNkBAVCpkukTUaWKCUJkLmf/W66c+bzgWyIj4ZlnzPf7gQdy7tmiNTz2mMl+WbUq94wLQchHeDNIUbDvXAiCcA3DbqzDz1vj+Gp1DCO61/eo9v6Tl5i/NY4hXWpTqaTvMxfqVy7J//q25IkZGxk5dwcf9mmOVKrlf84mprAz7mowYlfcRQ7FJ5I5CVaFEsE0qVaabg0r0TSsNE2qlSK8bKhkPPg5tkzN4cBiTCPvL7XWO5VSo4ANWusFwBfADKXUAeAsJpAB0AUYpZRKA9KBoVrrs3m/F3Y4cQJGj4Y774Rbb/W1Ne5zyy2mzOC++6B3bzPt5DvvmDvLgn2iomDgQNNwsV+/q0GHzMBD+fIQKMH0fMfo0TBnDgwZYr4XwdluAE2ebIJTY8ZAu3a+sVEQvIQ3z/oF+86FIAjX0LBKKbo3qsy0VTE81rk2JUI8d3r5aMk+QosE8kQX32ZRZOXWJlUY0b0eHy/ZT+NqpXj0+lq+NklwkTUH4xk5bzvRpxP/fi2sTDGaVCvF3a3CaFKtFE3DSlOpZIgEo/IpWuuFwMJsr72RZTkJ6JPDenOAOV430FVGjjTlHv/9r68t8Rw1asDKlfDss6YZYFSUmaa0UiVfW+afxMZCr14mGLFwoRyngkSJEjB+PPTsCR9+CK++evW9bdtM74pbbzXlXoJQwPBakKJA37kQBCFHhnery93jV/Ht2sM8cYNnAgo74y6wcPsJnu5Wl3LFPV9G4g7PdKvHrriLvLdwNw2rlOS6uhV8bZJggdT0DD76fR8T/zpIzfLFeeX2hjQNK03jqqZsQxD8mo0bYdo0c4FS37PZaz4nJMRMp9qhg+mzkTlNaYcOvrbMv0hMNBewCQnw228SoCiI3HWXySwaNQr69DG9WhITTcZM2bLw9ddSniMUSJTWBaOVQ2RkpN6wQapBBMHXDJq6jj0nLrHypRs90qvhsekbWHconpUvdqN0qP/VWyckp3HPhFWcupTMgmHXE1FemovlBw7HJ/LMrC1sPXqevpHhvHFXY4p7MPvH31BKbdRaR/rajsJAnoxHtIbOnWHfPti/v2DXom/ZYqYpjY2Fjz82QQvJaDJTdt57r5nZ5eefTaNLoWASF2f6iERGwpIl8Pjj8OWXJjDVvbuvrRMESzg7HpHQmyAIHmV4t7qcSUjm5o/+YsjXGxi7eA/ztxxjV9xFklLTLWltPXqeJbtP8njn2n4ZoAAoERLE5w9EkpGhGTJjA4nJab42SciFnzbF0mPcCg6dTmD8gNZ8cF/zAh2gEAogs2ebRnnvvVewAxQALVuarJFbboFhw8x0pZcv+9oq3zNyJMybZ0p9JEBRsKlWzZQ+/fGHaSz7xRfw8ssSoBAKNJJJIQiCR9FaM3P9UVbsP82+k5eIib9Mum0mhAAFNcsXp17lEtSvXJJ6lUtSv3IJalUoTkjQP7MuBn+5nq2x51nx4o2ULOqfQYpMlu87zUPT1nNrkypMGNjaK70LLlxOJT4xmdoVS3hcuzBwMSmVN+btYN6WONrVLMdH/VoSVqaYr83KEySTIu/w+njk8mVo0AAqVjT9GgpLQ8SMDHj3XXjzTWjaFL75xvRhcIeQkPwZ5Jk+HR56yDRUnDRJMksKAxkZJntq9Woz08dff+Xf2XyEQo2z4xG5dSQIgkdRSjGgfQQD2kcAkJyWzqEziew/mcD+k5fYdzKBfacusWT3qb+DF4EBihrlQ6lfyQQt6lUuiQb+2neal25r6PcBCoAu9Svyyu2NeHfhbj774wBP31TPY9pHz17mi5WHmB11lCup6bQML8MDHWpwR/OqMv2pk2w8fI4RszcTdz6J52+uz7Ab6xIos3II+ZGxY03pw3ffFZ4ABZi6+9dfN7MYDBgALVq4r6mUuSM9enT+OZYrV5p0/27d4LPPJEBRWAgIMCUer71mmmhKgEIo4EgmhSAIPiE5LZ3o04nsO3mJ/ScTzN9TCRyOTyQjy5SPy1+8kdDg/BFP1Vrz/Pdbmbv5GFMfjKR748pu6W2LPc/ny6NZuP04W+g71AAAGLJJREFUgQGKni3CaFS1JDPXH+Hg6UTKhhbh/shwBravIb0w7JCeoZmw7AAfL91P1dJFGdevJW1qlPO1WXmOZFLkHV4djxw5Ag0bmmZ6s2d7Zxv5gdhY+OUXc3fZHdauhRkzzAwJ330H5fz83BAdDe3bm4aJa9f6v72CIAjZcHY8IkEKQRD8iqRUE7zYf+oSdSqWoGlY/krFTUpNp8+kNRw6k8i8YZ2oW6mkpfUzMjR/7TvN5OUHWRt9lpIhQQzoEMHDnWpRpXRRwARD1kTH883awyzeeZIMrbmhfkUe6FCDrg0qSYaAjWPnr/DcrC2sjzlLzxbVeKd3U0rlg6wcbyBBirzDq+OR/v1NH4I9e8xUnYL7fP45DB8O1avD3LmeydDwBhcuQKdOcPy4CVAUtBldBEEoFEiQQhAEwUfEnb9Cz89WUrJoEeYNu47SxXK/ME5OS2f+ljimLI9m/6kEqpYuyiPX1aJfu3CH5S4nLiQxK+oIM9cf4eTFZMLKFGNghwj6RoZTvkSIJ3crX7Fw+3FenrON9AzN6Lub0rtVmFf6hOQXJEiRd3htPLJypalJf/11Mx2h4DnWrjUzZZw7Z5oS9u/va4uuJS3NZM8sWQKLF5tSD0EQhHyIBCkEQRB8SFTMWQZMWUunOhX48qG2drMbLlxJ5bt1R5i26hCnLiXTsEpJhnSpzV0tqlEk0PkJmFLTM1iy6yQz1h5m9cF4ggMD6NGsCg90rEHriLKF5gL9ckoao37exayoo7QIL8Mn/VpSo3xxX5vlcyRIkXd4ZTySkQFt28LJk7B3LxQXn/Y4J05Anz4mGPT882Y2hSA/KTV89ln45BOT9fH44762RhAEwWWkcaYgCIIPaVuzHG/1bMLIuTsYu3gvL9/e8Jr3j52/wrSVh5i5/giJKelcX7cCH/ZpQed6FVwKKBQJDOD2ZlW5vVlVDpy6xDdrjzBnYyzztsTRqGopHuhQg14tqxXoqTZ3HLvAMzM3cyg+kWE31mFE9/qWAj2C4Ld89RVs2mRmtJAAhXeoUgWWLoUXXoD//Q82bzZ9PypW9K1dkyaZAMVzz0mAQhCEQoNkUgiCIHiRkXO38+26I4zr15JeLcPYFXeRz5cf5Jdtx9HAnc2r8njn2l7pvZGYnMaCrXF8veYwu49fpGRIEPe2qc6gDhGWe2V4mj0nLvL1msNEn06gbGgwZYsHUza0iFkODaZc8SyvFQ+mZEiQ3eBNRobmi5WHGLN4D+WLh/BR35Z0rFM+j/fIv5FMirzD4+ORixdN/4HatWHVKpnNIS/4+mt44gkToJgzx2Sx+IIlS+C228xj/vz8MwOJIAiCHaTcQxAEwQ9ISctg4NS1bD92gdYRZVl9MJ7iwYH0axfBI9fXIqxMMa/boLVm05HzfLP2ML9uO05KegZta5alb9sIejSrkmezp6RnaH7fdZLpq2NYEx1PSFAATaqV4mJSGucSUzh/JfXvaWmzExSgKBN6NWhRNrSICWSEBrMt9gIrD5zh1iaVef+e5pQtHpwn+5OfkCBF3uHx8chLL8GYMbB+ve8ulgsjmzZB796mxGbiRHj44bzd/t690KGDaei5ahWUKpW32xcEQfACEqQQBEHwE05fSubu8atITc/goetqMrBdDUqH+maWifiEZH7YGMv3UUeJPpNIyZAgerasRt+24TQLK+2V3hXnL6cwO+ooX685zLHzVwgrU4wHOtagb2T4NQGFjAzNpaQ0zl1O4ezlFM5fTuFsYqrtbwrnLqdyLvGf74UEBTDyjsb0bxdeaHpvWEWCFHmHx8cjb75p+iVMnuw5TcE5zpyBfv1MGciTT8LHH0NwHgRB4+NNgOLCBROcqlnT+9sUBEHIAyRIIQiC4EckpaYTGKD8pkeC1pqomHPMijrCwu3HSUrNoFHVUvRrG87dLcM8EkTZc+Ii01fHMHfzMZJSM2hfqxwPX1eT7o0qE+Sh46C1Jj1De0yvoCJBirzDK+MRraXMw1ekpcGrr8LYsWYK0B9/hKpVvbe9lBS49VZYvRqWLTPbFARBKCBIkEIQBEFwiotJqSzYEsfsqKNsP3aB4KAAbm9ahb5tw+lQqzwBdmYmyYn0DM2S3Sf5atXVko7ercIY3KkmjapKurKvkCBF3iHjkQLK7NnwyCNQurQJVHgjeKA1DBkCU6eaJqkDB3p+G4IgCD7EL2b3UErdBowDAoGpWuv3s70fAnwNtAHigb5a6xjbe68AjwLpwDNa68XetFUQBKGwUqpoEQZ1qMGgDjXYcewC3284yrzNx5i/JY4a5UO5PzKc+9pUp3KponY1cirpePn2hv8o6RAEXyDjEcFt+vaFxo1Nn4quXU3px5NPejbD5aOPTIBi5EgJUAiCUKjxWiaFUioQ2AfcDMQCUUB/rfWuLJ95CmiutR6qlOoH9NZa91VKNQZmAu2AasASoL7WOt3e9uTOhSAIgudISk1n0Y4TzIo6wtroswQGKG5sUJG+bSO4sUHFv8sr9p64xFerY5i7OdZrJR2C+xTmTAoZjwge5dw5GDQIFi40zTQnTICi9gO4TvPzz9CrF9x7r8naCJDzpyAIBQ9/yKRoBxzQWkfbDJoF9AJ2ZflML+At2/KPwGfKdD3rBczSWicDh5RSB2x6a7xoryAIgmCjaJFA7m4Vxt2twog5k8j3G47yw8ZYluzeQKWSIfRsUY2dcRelpEPID8h4RPAcZcuagMJbb8Ho0bB9Ozz9tHsZFVeuwAsvQOvWMH26BCgEQSj0eDNIEQYczfI8Fmhv7zNa6zSl1AWgvO31tdnWDcu+AaXUEGAIQEREhMcMFwRBEK5Ss0JxXrytIc/fXJ9le08zO+oI01bHUKVUUSnpEPIDMh4RPEtAAIwaBW3awAMPwODB7muGh8P8+RAa6r6WIAhCPsebQYqcQsrZa0vsfcaZddFafw58Dia90qqBgiAIgvMEBQZwc+PK3Ny4MgnJaRQNCpCSDiE/IOMRwTv06gWxsXD6tPta1apBsWLu6wiCIBQAvBmkiAXCszyvDsTZ+UysUioIKA2cdXJdQRAEwUeUCPFq32VB8CQyHhG8R6lS5iEIgiB4DG/eAosC6imlaimlgoF+wIJsn1kAZObI3Qf8oU0nzwVAP6VUiFKqFlAPWO9FWwVBEARBKJjIeEQQBEEQ8hFeuxVmq+kcDizGTPn1pdZ6p1JqFLBBa70A+AKYYWtEdRYzcMD2ue8xTa3SgGGOOmkLgiAIgiDkhIxHBEEQBCF/4bUpSPMamfJLEARBEHKmME9BmtfIeEQQBEEQcsbZ8Yh0PBMEQRAEQRAEQRAEwS+QIIUgCIIgCIIgCIIgCH5BgSn3UEqdBg57WLYCcMYPNPzJFtkf/7bFXzT8yRbZH+9o+JMtsj+5U0NrXdHDmkIOyHgkX2n4ky3+ouFPtsj+eEfDn2yR/fFvW3w2HikwQQpvoJTa4G4Nryc0/MkW2R//tsVfNPzJFtkf72j4ky2yP0JBx5/8yl9skf3xjoY/2SL74x0Nf7JF9se/bfHleETKPQRBEARBEARBEARB8AskSCEIgiAIgiAIgiAIgl8gQQrHfO4nGp7S8RcNT+n4i4andAqShqd0/EXDUzoFScNTOv6i4Skdf9EQChb+5Ff+Yovsj3c0PKXjLxqe0ilIGp7S8RcNT+n4i4andPxFwyWkJ4UgCIIgCIIgCIIgCH6BZFIIgiAIgiAIgiAIguAXSJAiB5RSXyqlTimldrihEa6UWqaU2q2U2qmUetYFjaJKqfVKqa02jbfdsCdQKbVZKfWLGxoxSqntSqktSqkNLmqUUUr9qJTaYzs2HV3QaGCzIfNxUSk1wgWd52zHdYdSaqZSqqgLGs/a1t9pxYacfEwpVU4p9btSar/tb1kXNPrYbMlQSuXajdeOxljb/2ebUmquUqqMizqjbRpblFK/KaWqWdXI8t6/lFJaKVXBBTveUkody+IvPVyxQyn1tFJqr+34jnGk4cCW2VnsiFFKbXFBo6VSam3m91Ap1c4FjRZKqTW27/PPSqlSuWjkeD6z4rMONKz6rD0dp/3WgYbTPmtPI8v7zvqsPVss+a1QMLHn10qpmkqpK1n8Y5JVjSzvRyilEpRS/3LBjnZZbNiqlOrtgsbNSqmNtvPRRqVUNxePSXnbdylBKfWZKxq2915RSh1Q5nx/qwONHM9dSqlgpdQ02/5sVUp1zcUWezpFlFLTbTq7lVKvuKAxUF07XspQSrW0omF7r7kyvxk7bfbYHS85sMWKzzr8XXDSZ+3ZYcVn7WlY9Vl7OlZ81tH/xymfzbaOpXGAAx1LYxI7GpbGRg50LI3T7Gh47LdXOTkOsLOupTG0Ax3LY/ocNCyN0zyG1loe2R5AF6A1sMMNjapAa9tySWAf0NiihgJK2JaLAOuADi7a8zzwHfCLG/sUA1Rw89hOBx6zLQcDZdzUCwROYObctbJeGHAIKGZ7/j3wkEWNpsAOIBQIApYA9Vz1MWAM8LJt+WXgAxc0GgENgD+BSBftuAUIsi1/kJsdDnRKZVl+BphkVcP2ejiwGDicm//ZseMt4F8W/q85adxo+/+G2J5XckUn2/v/Bd5wwZbfgNttyz2AP13QiAJusC0/AozORSPH85kVn3WgYdVn7ek47bcONJz2WXsaLvisPVss+a08CubDnl8DNe2dW5zVyPL+HOAHR/7mwI7QLK9XBU5lPreg0QqoZltuChxz8ZgUB64HhgKfuajRGNgKhAC1gINAoB2NHM9dwDBgmm25ErARCHBgiz2dAcCsLMc5BqhpRSPbZ5oB0S7YEQRsA1rYnpe3d0xy0bHisw73x0mftWeHFZ+1p2HVZ+3pWPFZexpO+2w2PUvjAAc6lsYkTujlOjays57lcZodnbcc+ZUFHafHAXbWtzSGdqBjeUzvrO95+yGZFDmgtV4OnHVT47jWepNt+RKwG3NhbEVDa60TbE+L2B6Wm4gopaoDdwBTra7rSWxR2i7AFwBa6xSt9Xk3ZW8CDmqtD7uwbhBQTCkVhPnRirO4fiNgrdb6stY6DfgLsBuRz4odH+uFCeJg+3u3VQ2t9W6t9V5nbHCg8ZttfwDWAtVd1LmY5WlxcvFdB9+7j4AXc1s/Fw2nsaPxJPC+1jrZ9plT7tiilFLA/cBMFzQ0kHnHozS5+K0djQbActvy78C9uWjYO5857bP2NFzwWXs6TvutAw2nfTaXc7wVn3X7t0IouLhyPraioZS6G4gGdrqikeX3D6Aojr8z9jQ2a60zz2M7gaJKqRAXdBK11iuBJEf74kgDc06bpbVO1lofAg4AOd4ZdnDuagwstX3mFHAesHv30YGOBorbxijFgBTgYg6fc/a3vz8OfnMcaNwCbNNab7V9Ll5rne6CjtM40rDgszlqWPRZexpWfdaejhWftXdMnPbZbFgaBzgyDQtjEkc4Ozayg+VxmpdxehyQE1bH0A50PPEb4vZ32hUkSJEHKKVqYqKu61xYN9CW9nQK+F1rbVkD+BjzRclwYd2saOA3W2rbEBfWrw2cBqYpU3oyVSlV3E2b+uHCyUxrfQz4EDgCHAcuaK1/syizA+hiS9cLxUSQw63akoXKWuvjNvuOY+7A+JpHgP9zdWWl1LtKqaPAQOANF9bviblDsdVVG2wMt6W6falyKaOxQ32gs1JqnVLqL6VUWzft6Qyc1Frvd2HdEcBY23H9ELCbAuyAHUBP23IfLPhttvOZSz7rzjnRSR2n/Ta7his+m1XDHZ/NYX/c9VuhYJHdr2vZfkv/Ukp1tqph+/19CbBaSnqNHUqp9kqpncB2YGiWAbHTGlm4F9iceaHhho4VsmqEAUezvBeL9aDhVqCXUipIKVULaINrY4MfgUTMGOUI8KHW2p0gfF9cu/irD2il1GKl1Cal1Itu2OCKz/6NGz6bXccVn7WHVZ/1NK76rMvjgGx4YkySiTtjI0+O09z67fXU2NXdMXQOeOJ8mWcE+dqAgo5SqgQmLW1EtqiYU9ii1S1tNURzlVJNtdZO98pQSt0JnNJab1S51EU6wXVa6zilVCXgd6XUHttdWmcJwqSdP621XqeUGodJEX/dFWOUUsGYE6zlE6LtpNMLkxp3HvhBKTVIa/2Nsxpa691KqQ8wEegEzMDEnR86v0IpNRKzP9+6qqG1HgmMVKaWdjjwpoXthwIjMXdx3GEiMBoTZBuNSSV8xKJGEFAW6AC0Bb5XStXWWrsU2SaXO1q58CTwnNZ6jlLqfkxmUneLGo8Anyil3gAWYO7Q5Ur285m56WENd8+JuelY8ducNKz6bFYN23Zd8tkcjq0n/FbIByillgBVcnhrpNZ6vu0z2f36OBChtY5XSrUBVimlovnnzQhHGm8DH2mtE2zf5SFKqYcs2oHt5kkTpVQjYK1S6h3+edfPoYbt9SaYdORbXDwm1+CiRvaT2m3AzUqpt+xp5MCXmEzLDZhU79XA6yrnvmKOdNoB6UA1zO/PIaXUC0CqBQ3AXJQDl4GPlVIOj0kOBGFKEtraNJYqpfpiygus6Fj22Ryw7LM5YdVn7WHVZ3PDRY2cfoh1bnpYGAfkonMTToxJnNw3h2OjXOxwepyWi45Tv725aLyKE+OA3I6Js+ORvDpf5jk6j+pK8tsDC7VzDjSKYOqRnveQTW9isU4K+A8mqhqD6d1wGfjGA7a85YItVYCYLM87A7+6YUMv4DcX1+0DfJHl+YPABDePyXvAU676GLAXqGpbrgrstaqR5fU/cbJuLCcNYDCwBgh1dX+yvVfDme9TVg1M/ewpm+/GYE6uR4Aqbtjh1Pc6h//NIqBrlucHgYouHtsg4CRQ3UU/uQB/Tx+tgItu/m/qA+ud0PjH+cyqz+ak4aLP5qhjxW8d2eKsz2bXcMNnc7PFKb+VR8F8OOPXuX1/ctIAVmTx1fOYsrDhbtqxzKodtterY/qxXOfuMQEeIpf6fgfH5BXglSzPFwMdc9HJ7divxomeZNl1gPHAA1mefwnc74otmNTzV508ttnt6Ad8leX568C/repYfd+OLZZ81kk7HPqsPQ2rPpvL/8cpn7VzTCz7bA6aTo0D7KxreUxiR8fS2CiH9V0ap+WiWROLv724OA7IRdOpMbSD9S2P6Z3xPW8/pNzDSygT4v0C2K21/p+LGhXV1a7VxTCRyT1WNLTWr2itq2uta2J+aP7QWg9ywZbiSqmSmcuYCKGl2U+01ieAo0qpBraXbgJ2WbUlC+7cjT4CdFBKhdr+VzdhasEtYcsqQSkVAdzjhj1gItmDbcuDAZ9ELpVSt2HSKXtqrS+7oVMvy9OeWPfd7VrrSlrrmjb/jcU0GDxh0Y6qWZ72xqLf2pgHdLPp1cc0fT3jgg7Yvsda61gX148DbrAtdwMsp0Vm8dsA4DXAbpd12+fsnc+c9llPnBMd6VjxWwcaTvtsThqu+KwDWzzht0I+x55f28YHgbbl2kA9TJ2+0xpa685ZfPVj4D2tdY4zDDiwo5YyPRNQStXA1LnHWNQoA/yKudBa5fiIeOY3yoHGAqCfUipEmVKNesB6i9qhtnESSqmbgTSttStjnSNAN2UojrlDbOl31GZDAObGzCwXbABz0dvctl9BmN8fy/tjxWftYcVnHdjhtM860LDks17GJZ+1Og5wgNtjEhvujo08Mk5z97fXg2NXt8bQWXQ8Mqb3CXkVDclPD8yF5nFMSl0s8KgLGtdjUoW2AVtsjx4WNZoDm20aO3Ch2202va64OLsHpp/EVttjJyb9xxWdlpgUyG2YE0pZF3VCgXigtBvH423Ml34HMANbR2CLGiswP9ZbgZvc8TFMx+ylmBP8UqCcCxq9bcvJmIj0Yhc0DmDqGzP9NteOwnZ05tiO7TbgZ0xjQksa2d6PIfeZEnKyYwam7nQb5se8qgsawcA3tv3ZBHRz5ZjYXv8KUwPrqp9cj+kWvxXTu6CNCxrPYu4A7QPex3YXxIFGjuczKz7rQMOqz9rTcdpvHWg47bP2NFzwWXu2WPJbeRTMhz2/xtTB77SdBzYBd1nVyPaZt3A8U4I9Ox6w2bHFZsfdLmi8hum9sCXLw25nfkf7Y/vOncWUYMZiJ4MhF42RmLuwe7HNWmBHI8dzF+bu617MjY8l5DL7mAOdEpgZLHZixhl2sxfsadje64pp8p2brznSGGSzYwcwxsX9seKzuf4uOOGz9uyw4rP2NKz6rKNj66zPOtJwymez6VkaBzjQsTQmcaDzFU6Ojeysb3mcZkfHo7+9uDgzIhbH0A50LI/prfieNx+Z6TmCIAiCIAiCIAiCIAg+Rco9BEEQBEEQBEEQBEHwCyRIIQiCIAiCIAiCIAiCXyBBCkEQBEEQBEEQBEEQ/AIJUgiCIAiCIAiCIAiC4BdIkEIQBEEQBEEQBEEQBL9AghSCIAiCIAiCIAiCIPgFEqQQBEEQBEEQBEEQBMEvkCCFIAiCIAiCIAiCIAh+wf8DyxKx5VOTCtUAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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zouAkhZmZmZmZmZkVBScpzMzMzMzMzKwoOElhZmZmZmZmZkXBSQoz+5KkJyUtlrQgfUyrQ90DJT0t6XNJFZKeknRIHepfmPO6iyUtz9meVL8zMjMzs7WJpDslvS9pvqQ3JZ1eh7qlkv4l6RNJn0qaLOkKSR3qGctekkLSefWpb2b14ySFmVV3VkSsnz62yKeCpKOAe4C/A92AjYFhwLfS4xtIWm91bUTElVWvC5wBPJ8TR/81OSEzMzNba/wG6BUR7YBDgF9L2qG2SpJ2BZ4ExgJbRsSGwBCgEtg2LVPr/Ug1JwEfp3/NrJE4SWFmtZJ0sqSxkq6X9JmkqZL2TY8JuBa4PCL+EhGfRcSKiHgqIr6XNrE1MEfSzZJ2zuo8zMzMrLhFxKSIWFK1mT42TXs1lKc9Lz+UNEvSiTlVrwJui4jfRMQHaVvvRsTFEfFkWibv+xFJbYCjgDOBvpJKG/A0zWw1nKQws+p+k/7Pf6ykvXL27wTMADoBFwP3S+oIbAF0B+5dVYMR8TywPTAH+IekKZLOk/S1Qp2EmZmZrZ0k3ShpITAVeB8YnR7ahOQ+pCtJ74bhkraQ1BbYBbhvde3W8X7kSGABSU/RR4HvrvmZmVk+nKQws1znA31I/uc/HHhY0qbpsXnAHyJiWUT8E5gGHAxslB5/f3UNR8TMiLgU2Az4PrAlMDkdO9qj4U/FzMzM1kYR8UNgA2AP4H5gSc7hX0XEkoh4Cvg3cAzQgeR7zdyqQpKuSuel+ELSRTlt53s/chLwz4hYDtwFHC+pRSHO18y+ykkKM/tSRLwYEZ+n//O/nWRc5zfSw+9FROQUfwf4OvBRup1Xr4i0jSnABKAc6A+0bYj4zczMbN0QEcsj4lmSua5+kO7+JCK+yClWdS/yCbCCnHuRiDgvnZfiAaB5De2v8n5EUndgb+AfafGHgNYkP86YWYE5SWFmqxOA0udd0/knqvQg6S45DZhN0i1ylSS1knSUpFHAW8AOwDlAn4iY0uCRm5mZ2bqgOVDVq7NDOrSjSg9gTpq4eBE4orbG8rwf+Q7J96SHJc0lGe7aGg/5MGsUTlKYGQCSNkyXEW0tqXk6GdVgknGYAF2AcyS1kHQ0sBUwOv0l4lzgV5JOkdROUjNJu0sanra9DclwkB+R/BrRPSK+GxFPVOudYWZmZk2UpC6SjpO0vqQSSQcCxwOP5xS7VFJLSXsA3ySZMwLgPOBUSRdI6pK21w3ondN+vvcj3wUuBbbLeRwJHCxpI8ysoFbq+mRmTVYL4NckYzOXk0xWdVhETJO0C8kvFH2BD4EPgKMi4iOAiLhX0gLgl8D1wCJgEnB12vY8YFBETG/E8zEzM7O1S5AM7biJ5MfUd4AfR8RD6WTec0mGdswBFgJnRMRUgIh4VtI+JJN7X5B2/iwnSUZcn7Zf6/1IuupHL+CGiKjIOTRK0nSSpMmfG+RszaxG8o+YZlYbSScDp0fE7lnHYmZmZk1PmqS4MyK6ZR2LmRWWh3uYmZmZmZmZWVFwksLMzMzMzMzMioKHe5iZmZmZmZlZUXBPCjMzMzMzMzMrCk5SmJmZmZmZmVlRWGeWIO3UqVP06tUr6zDMzMyKzssvv/xhRHTOOo6mwPcjZmZmNcv3fmSdSVL06tWL8ePHZx2GmZlZ0ZH0TtYxNBW+HzEzM6tZvvcjHu5hZmZmZmZmZkXBSQozMzMzMzMzKwpOUpiZmZmZmZlZUXCSwszMzMzMzMyKgpMUq/DYlA+48IGJRETWoZiZmdkakDRE0jRJ0yVdUMPxMyRNlPSapGcl9Uv395K0KN3/mqSbGj96MzOzRvaXv8CvfgXLl2fy8uvM6h4N7a15C7jrxXfZsVcHDh/YLetwzMzMrB4klQA3APsD5cA4SaMiYnJOsbsi4qa0/CHAtcCQ9NjbEbFdY8ZsZmaWmWXL4NJLoU8fKCnJJAT3pFiF7+3Rh9KeHRj20CTe/2xR1uGYmZlZ/QwCpkfEjIhYCowEDs0tEBHzczbbAu5GaWZmTdPIkVBeDuefn1kITlKsQkkzcc3R21K5PDjv3tc97MPMzGzt1BWYnbNdnu77CklnSnobuAo4J+dQb0mvSnpK0h41vYCkoZLGSxpfUVHRkLGbmZk1ngi46irYems46KDMwnCSYjV6dWrLhQdvxTNvfcg/Xnw363DMzMys7lTDvpV+eYiIGyJiU+B84KJ09/tAj4gYCJwL3CWpXQ11h0dEaUSUdu7cuQFDNzMza0T/+Q+88Qb8/Oegmv732TicpKjFt3fqwR59O3Hl6Cm889EXWYdjZmZmdVMOdM/Z7gbMWU35kcBhABGxJCI+Sp+/DLwNbF6gOM3MzLJ11VXQvTscf3ymYThJUQtJXHXUNpQ0Ez+9ewLLV3jYh5mZ2VpkHNBXUm9JLYHjgFG5BST1zdk8GHgr3d85nXgTSX2AvsCMRonazMysMb34Ijz1FPzkJ9CiRaahOEmRh6+1X49LD+nP+Hc+4S/P+N7EzMxsbRERlcBZwKPAFODuiJgk6bJ0JQ+AsyRNkvQaybCOk9L9g4HXJU0A7gXOiIiPG/kUzMzMCu+qq2DDDeH007OOxEuQ5uvwgV15dNJcfv/fN9l7yy5svvEGWYdkZmZmeYiI0cDoavuG5Tz/0Srq3QfcV9jozMzMMvbmm/DAA3DhhbBB9t9zC9qTQtIQSdMkTZd0QQ3Hz5A0UdJrkp6V1C/d30vSonT/a5JuKmSc+ZDEFYcPYIPWzTn37tdYtnxF1iGZmZmZmZmZrZlrroGWLeHss7OOBChgkiIdw3kDcBDQDzi+KgmR466IGBAR25Es+XVtzrG3I2K79HFGoeKsi07rt+KKwwfwxnvzuf7x6VmHY2ZmZmZmZlZ/c+fC7bfDySfDxhtnHQ1Q2J4Ug4DpETEjIpaSzJZ9aG6BiJifs9mWGpYEKzZDtt6EIwZ25YYnpjNh9qdZh2NmZmZmZmZWP3/6EyxbBj/7WdaRfKmQSYquwOyc7fJ031dIOlPS2yQ9Kc7JOdRb0quSnpK0RwHjrLOLD+lPlw1a8dN7JrB42fKswzEzMzMzMzOrm/nz4cYb4cgjYbPNso7mS4VMUqiGfSv1lIiIGyJiU+B84KJ09/tAj4gYSDLL9l2S2q30AtJQSeMlja+oqGjA0Fev/Xot+N2R2zB93gKueXRao72umZmZmZmZWYO45Rb47DM477ysI/mKQiYpyoHuOdvdgDmrKT8SOAwgIpZExEfp85eBt4HNq1eIiOERURoRpZ07d26wwPMxePPOfHvnHvx17ExemPFRo762mZmZmZmZWb0tXQrXXQd77w077ph1NF9RyCTFOKCvpN6SWgLHAaNyC0jqm7N5MPBWur9zOvEmkvoAfYEZBYy1Xi78xlb06NiGn90zgQVLKrMOx8zMzMzMzKx2d90F771XdL0ooIBJioioBM4CHgWmAHdHxCRJl0k6JC12lqRJkl4jGdZxUrp/MPC6pAnAvcAZEfFxoWKtrzYtm/P7o7flvU8XccW/p2QdjpmZmZmZmdnqrVgBV18N22wDBx6YdTQraV7IxiNiNDC62r5hOc9/tIp69wH3FTK2hlLaqyNDB/fh5qdmcED/jdl7iy5Zh2RmZmZmZmZWs9GjYfJkuPNOUE1TSWarkMM9moyf7Lc5m2+8Puff+zqfLlyadThmZmZmZmZmNfvd76BHDzjmmKwjqZGTFA2gdYsSrj1mOz7+YinDHpqUdThmZmZmZmZmK3vuOXj2WfjpT6FFi6yjqZGTFA1k667tOWffvoyaMId/v/5+1uGYmZmZmZmZfdXVV0PHjnDaaVlHskpOUjSgH+61Kdt2a89FD05k3ueLsw7HzMzMAElDJE2TNF3SBTUcP0PSREmvSXpWUr+cY79I602TVHyzi5mZmeVr6lR46CE480xo2zbraFbJSYoG1LykGb8/Zlu+WLqcC++fSERkHZKZmVmTli5pfgNwENAPOD43CZG6KyIGRMR2wFXAtWndfiRLqPcHhgA3Vi2RbmZmtta55hpo1QrOPjvrSFbLSYoGtlmXDTjvwC0omzKPe14uzzocMzOzpm4QMD0iZkTEUmAkcGhugYiYn7PZFqj6leFQYGRELImImcD0tD0zM7O1y5w5cMcdcOqp0Llz1tGslpMUBXDqbr3ZqXdHLnt4MuWfLMw6HDMzs6asKzA7Z7s83fcVks6U9DZJT4pz6lh3qKTxksZXVFQ0WOBmZmYN5o9/hMrKZMLMIuckRQE0ayauOXpbIoLz7n2dFSs87MPMzCwjNS0Av9L/mCPihojYFDgfuKiOdYdHRGlElHYu8l+nzMysCfrsM7jpJjj6aOjTJ+toauUkRYF079iGi77Zj+fe/ojRb3i1DzMzs4yUA91ztrsBc1ZTfiRwWD3rmpmZ1c/8+fDjH8OrrzZ828OHJ+3//OcN33YBOElRQMeUdqd3p7bc8vQMT6JpZmaWjXFAX0m9JbUkmQhzVG4BSX1zNg8G3kqfjwKOk9RKUm+gL/BSI8RsZmZNzb33JkMyBg2CYcNg6dKGaXfJErjuOth3X9hhh4Zps8CcpCigkmbitN17M6H8M16a+XHW4ZiZmTU5EVEJnAU8CkwB7o6ISZIuk3RIWuwsSZMkvQacC5yU1p0E3A1MBh4BzoyI5Y1+EmZmtu4rK4MuXeDEE+Hyy6G0FF5+ec3b/cc/4P334fzz17ytRqJ15Rf+0tLSGD9+fNZhrGTR0uXs9rvH2b7HhvzlpB2zDsfMzJogSS9HRGnWcTQFxXo/YmZmRWzFCthkEzjgALjzTvj3v2HoUPjggyS5MGxYsnRofdrt3x9at4ZXXgHVNNVS48n3fqSgPSkkDZE0TdJ0SRfUcPwMSRMlvSbp2dx1yyX9Iq03TdKBhYyzkNZrWcJ3du5J2ZR5TJ+3IOtwzMzMzMzMrJi88QZUVMB++yXbBx8MkybBSSfBlVfC9tvDuHF1b/fhh2HqVDjvvMwTFHVRsCSFpBLgBuAgoB9wfG4SInVXRAyIiO1Ilvy6Nq3bj2TMaH9gCHBj2t5a6bu79KRV82b85ZkZWYdiZmZmZmZmxaSsLPm7777/27fhhvDXv8J//pNMernzznDBBbB4cf7tXnUV9OqVrOqxFilkT4pBwPSImBERS0lmyz40t0BEzM/ZbMv/lvU6FBgZEUsiYiYwPW1vrbTR+q04aodu3P/Ke8z7vA4fKjMzMzMzM1u3lZXBFltA9+4rHxsyJOlpceqp8LvfwcCB8MILtbc5diw89xz89KfQvHnDx1xAhUxSdAVm52yXp/u+QtKZkt4m6UlxTh3rDpU0XtL4ioqKBgu8EE7bvTfLVqzgjuffyToUMzMzMzMzKwZLl8JTT/1vqEdN2reHW26BRx+FL76A3XZLlhNdtGjVdX73O9hoIzjllIaPucAKmaSoadDLSrN0RsQNEbEpcD5wUR3rDo+I0ogo7dy58xoFW2h9Oq/P/lttzB0vvMPCpZVZh2NmZmZmZmZZe+EFWLhw9UmKKgcckPSq+N734JprYLvtkt4S1U2enMxHcfbZ0LZtw8dcYIVMUpQDuf1VugFzVlN+JHBYPeuuFYYO7sOnC5dx78vlWYdiZmZmZmZmWSsrg2bNYK+98ivfrh3cdBOMGQNLlsDuu8O55yaJjirXXAPrrQdnnlmQkAutkEmKcUBfSb0ltSSZCHNUbgFJfXM2DwbeSp+PAo6T1EpSb6Av8FIBY20Upb06JkuRPjOT5SvWjaVfzczMzMzMrJ7KymDQoGSizLrYbz+YOBF+8AO47jrYdlt45hkoL0+WMT39dOjUqTAxF1jBkhQRUQmcBTwKTAHujohJki6TdEha7CxJkyS9BpwLnJTWnQTcDUwGHgHOjIjlhYq1MQ0d3Id3P17Io5PmZh2KmZmZmZmZZeWzz+Cll/Ib6lGTDTaAG26Axx+H5cthzz2TiTZXrEh6V6ylCjrNZ0SMBkZX2zcs5/mPVlP3CuCKwkWXjf37bULPjdpw89MzOGjrTdBatF6tmZmZmZmZNZCnnkqSC/VNUlTZe294/XX4xS/gz3+GE05Ilh5dSxVyuIfVoKSZOH333kyY/Snj3/kk63DMzMzMzMwsC2PGQJs2sPPOa97W+uvD9dfD1KkwfPhN9qpZAAAgAElEQVSat5chJykycNQO3enQpgXDn56RdShmZmZmZmaWhbIyGDwYWrVquDa32GKtXNEjl5MUGVivZQnf2aUXZVM+4O2KBVmHY2ZmZmZmZo2pvDzp9bCmQz3WQU5SZOS7u/SkRUkz/vLMzKxDMTMzW6dJGiJpmqTpki6o4fi5kiZLel3SY5J65hxbLum19DGqel0zM7N6eeyx5K+TFCtxkiIjndZvxZHbd+O+V8r5cMGSrMMxMzNbJ0kqAW4ADgL6AcdL6let2KtAaURsA9wLXJVzbFFEbJc+DsHMzKwhlJVB584wYEDWkRQdJykydPoevVm2fAV/f/6drEMxMzNbVw0CpkfEjIhYCowEDs0tEBFPRMTCdPMFoFsjx2hmZk1JRJKk2HdfaOav5NX5imRo087rs99WG3PH87NYtHR51uGYmZmti7oCs3O2y9N9q3Ia8J+c7daSxkt6QdJhNVWQNDQtM76iomLNIzYzs3Xb5Mkwd66HeqyCkxQZGzq4D58sXMa9L8+uvbCZmZnVlWrYFzUWlL4NlAJX5+zuERGlwAnAHyRtulJjEcMjojQiSjt37twQMZuZ2bqsrCz56yRFjZykyFhpzw5s131D/vLsTJavqPGeyczMzOqvHOies90NmFO9kKT9gF8Ch0TEl5NFRcSc9O8M4ElgYCGDNTOzJqCsDDbbDHr2rL1sE+QkRcYkMXRwH975aCFjJs/NOhwzM7N1zTigr6TekloCxwFfWaVD0kDgZpIExbyc/R0ktUqfdwJ2AyY3WuRmZrbuWbYMnnzSvShWw0mKInBg/03o0bENw5+ekXUoZmZm65SIqATOAh4FpgB3R8QkSZdJqlqt42pgfeCeakuNbgWMlzQBeAL4bUQ4SWFmZvX30kuwYAHsv3/WkRSt5oVsXNIQ4I9ACfCXiPhttePnAqcDlUAFcGpEvJMeWw5MTIu+uy4v+1XSTJy+R2+GPTSJl9/5mB16dsw6JDMzs3VGRIwGRlfbNyzneY0/Z0XEc4DXhjMzs4ZTVgYS7L131pEUrbx6Ukjauq4Ne13yujlqh25s2KYFNz/l3hRmZmY1qc/9iJmZWVEpK4PSUujQIetIila+wz1ukvSSpB9K2jDPOl6XvA7atGzOd3buyZgpHzCjYkHW4ZiZmRWj+tyPmJmZFYfPP4cXXvB8FLXIK0kREbsDJ5LMjj1e0l2SahtEU/B1ydc1392lFy1KmvHXZ2dmHYqZmVnRqef9iJmZWXF4+mmorHSSohZ5T5wZEW8BFwHnA3sCf5I0VdIRq6hS8HXJJQ1NExnjKyoq8j2VotV5g1YcuX1X7n25nI8WLKm9gpmZWRNTj/sRMzOz4lBWBq1bw667Zh1JUct3ToptJF1HMiv2PsC3ImKr9Pl1q6hW8HXJI2J4RJRGRGnnzp3zOZWid9rufVhSuYI7Xngn61DMzMyKSj3vR8zMzIrDmDGwxx5JosJWKd+eFH8GXgG2jYgzI+IV+DKRcNEq6nhd8nrYrMv67LdVF/7+/DssWro863DMzMyKSX3uR8zMzLL3/vswaZKHeuQh3yTFN4C7ImIRgKRmktoARMQdNVXwuuT19709+vDxF0u575XyrEMxMzMrJnW+HzEzMysKjz2W/HWSolbN8yxXBuwHVC070Qb4L7DawTRel7x+BvXuyLbd2vPXZ2dy/KAelDSraXoPMzOzJqde9yNmZmaZKyuDjh1hu+2yjqTo5duTonVEfLkuZvq8TWFCMkkMHbwpMz/8grIpH2QdjpmZWbHw/YiZma19IpIkxb77QrO8165osvK9Ql9I2r5qQ9IOwKLChGQAB/bfmO4d1+OWp2dkHYqZmVmx8P2ImZmtfaZNg/fe81CPPOU73OPHJPNGVK3O8TXg2MKEZADNS5px2m69ueThybz8zifs0LND1iGZmZllzfcjZma29ikrS/7uv3+2cawl8kpSRMQ4SVsCWwACpkbEsoJGZhxd2p3ryt7ilqdnsMN3dsg6HDMzs0z5fsTMzNZKZWXQpw/07p11JGuFugyI2RHYBhgIHC/pu4UJyaq0bdWcb+/cg0cnz2XWh19kHY6ZmVkxqPP9iKQhkqZJmi7pghqOnytpsqTXJT0mqWfOsZMkvZU+TmrQMzEzs3VfZSU88YSHetRBXkkKSXcA1wC7k9wc7AiUFjAuS520ay9aNGvGH8rezDoUMzOzTNXnfkRSCXADcBDQjySx0a9asVeB0ojYBrgXuCqt2xG4GNgJGARcLMnjL83MLH/jx8P8+U5S1EG+c1KUAv0iIgoZjK2sywat+f6efbj+8ekcuUM39ujbOeuQzMzMslKf+5FBwPSImAEgaSRwKDC5qkBEPJFT/gXg2+nzA4ExEfFxWncMMAQYUe8zMDOzpqWsDCTYe++sI1lr5Dvc4w1gk0IGYqt25t6b0btTWy568A0WL1uedThmZmZZqc/9SFdgds52ebpvVU4D/lOXupKGShovaXxFRUUdwzMzs3VaWRkMHAidOmUdyVoj3yRFJ2CypEcljap6FDIw+5/WLUq44rCteeejhfzpsbeyDsfMzCwr9bkfUQ37auyJIenbJL01rq5L3YgYHhGlEVHaubN7PJqZWeqLL+C55zzUo47yHe5xSSGDsNrtulknjty+G8OfnsEh232dLTdpl3VIZmZmje2SetQpB7rnbHcD5lQvJGk/4JfAnhGxJKfuXtXqPlmPGMzMrCl65hlYtsxJijrKqydFRDwFzAJapM/HAa8UMC6rwS8P3ooNWjfnwvsnsmKFpwcxM7OmpZ73I+OAvpJ6S2oJHAd8pfeFpIHAzcAhETEv59CjwAGSOqQTZh6Q7jMzM6tdWRm0agW77551JGuVfFf3+B7JbNc3p7u6Ag8WKiirWce2Lbno4H688u6n3PXSu1mHY2Zm1qjqcz8SEZXAWSTJhSnA3RExSdJlkg5Ji10NrA/cI+m1qiEk6YSZl5MkOsYBl1VNomlmZlarsjLYbTdYb72sI1mr5DsnxZnAbsB8gIh4C+hSWyWvS97wjti+K7ttthG/e2Qq8+YvzjocMzOzxlSv+5GIGB0Rm0fEphFxRbpvWERUJSP2i4iNI2K79HFITt1bI2Kz9HFbQc7KzMzWPfPmwYQJHupRD/kmKZZExNKqDUnNWcWkUzllvC55AUji14cNYEnlCi59eHLtFczMzNYddb4fMTMzy8RjjyV/naSos3yTFE9JuhBYT9L+wD3Aw7XU+XJd8vSGompd8i9FxBMRsTDdfIFkQirIWZc8Ij4BqtYlN6B3p7acs89m/Hvi+zw+9YOswzEzM2ss9bkfMTMza3xlZdChA2y/fdaRrHXyTVJcAFQAE4HvA6OBi2qp43XJC2jo4E3ZfOP1+dWDk/hiSWXW4ZiZmTWG+tyPmJmZNa4IGDMG9tkHSkqyjmatk+/qHisi4paIODoijkqf19a90uuSF1DL5s248vABvPfpIq4b82bW4ZiZmRVcPe9HzMzMGtf06TB7tod61FPzfApJmknNSYI+q6nmdckLrLRXR44f1INbx87ksIFd2bpr+6xDMjMzK5h63o+YmZk1rrKy5K+TFPWSV5KCpJdDldbA0UDHWup8uS458B7JuuQn5BbIWZd8SA3rkl+ZM1nmAcAv8oy1SblgyJaMmfwBv7h/Ig/8cFeal+Q7gsfMzGytU5/7ETMzs8ZVVgY9e8Kmm2YdyVop3+EeH+U83ouIPwD71FLH65I3gvZtWnDxt/ox8b3PuP35d7IOx8zMrGDqcz9iZmbWqJYvh8cfT3pRqKZZDKw2+Q73yJ2StBnJLxkb1FYvIkaTTGqVu29YzvNV9n+JiFuBW/OJr6n75jZf475Xyvn9f6cxZOtN6LrhelmHZGZm1uDqez9iZmbWaF55BT791EM91kC+wz1+n/O8EpgFHNPg0Vi9SOLyQ7fmgOue5uKH3uCW75YiZ+3MzGzd4/sRMzMrblXzUezjjn71lVeSIiL2LnQgtma6d2zDT/bvy5Wjp/LIG3M5aMDXGrT9L5ZUcv3j03l2egW3nzKIjdZv1aDtm5mZ1cb3I2Zm9qUIGDEC5s5NhlhUViaPquer21f92K67wplnQsuWax5XWRlsuy106bLmbTVR+Q73OHd1xyPi2oYJx9bEqbv15sFX53DxqEns1rcT7Vq3WOM2I4JRE+Zw5egpfDA/WXzlrhff5ex9+65x22ZmZnXh+xEzM/vS88/DiSfWfKxZM2jeHEpKkr+5z6v/Xb4c7r0X/u//4Npr4eCD6z+XxMKF8OyzcPbZ9T8vy2/iTJIxnz8AuqaPM4B+JONAPRa0SDQvacZvjhjAhwuWcPUj09a4vSnvz+fY4S/wo5Gv0WWD1tz/w13Zc/PO/P2Fd1hauaIBIjYzM6uTet2PSBoiaZqk6ZIuqOH4YEmvSKqUdFS1Y8vTyb2/nODbzMyKwF13QevWMGcOLFgAixfDsmWwYkWSeFiyJEkazJ8PH38MFRXw/vtQXg6zZsHbb8O0aTB9OvznP0li41vfgoMOgqlT6xfT2LGwdKnno1hD+c5J0QnYPiI+B5B0CXBPRJxeqMCsfrbtviHf3aUXtz8/i8MGdmWHnh1qrVPdZwuXcV3Zm/z9+Vm0X68FVx4+gGN37E5JM3HKbr04+bZxjJ74PocN7Nrg8ZuZma1Gne9HJJUANwD7A+XAOEmjImJyTrF3gZOBn9XQxKKI2K5hwjczswZRWQn33APf/CZ8rQGGuQ8ZAhMnwp//DJdcAgMGJL0hhg2DDTfMv52yMmjRAvbYY81jasLy7UnRA1ias70U6NXg0ViD+NmBW7BJu9ZceP9Eli3Pv8fDihXBP8e9y96/f5K/Pz+LE3fqyRM/24sTdupBSbOky9Pgvp3ZtHNbbh07k4go0BmYmZnVqD73I4OA6RExIyKWAiOBQ3MLRMSsiHgdcDdBM7O1weOPw7x5cPzxDddmixbwk5/AW2/BKafAH/4Am28Ot9yS9MzIR1lZMr9F27YNF1cTlG+S4g7gJUmXSLoYeBH4e+HCsjWxfqvmXHpIf6Z98Dm3PDMjrzqvzf6Uw28cy/n3TaRPp7Y8fPbuXH7Y1mzY5quTxzRrJk7erTevl3/GK+9+UojwzczMVqU+9yNdgdk52+Xpvny1ljRe0guSDqtbuGZmVhAjRkC7dvCNbzR82126wPDh8PLLsMUWMHQolJbCM8+svt6HH8Krr8L++zd8TE1MXkmKiLgCOAX4BPgUOCUirixkYLZmDui/CQf235g/lr3Fux8tXGW5jxYs4fx7X+ewG8Yy57PFXHfsttxzxi70/3r7VdY5cvuutGvdnFufnVWAyM3MzGpWz/uRmmY/q0tXwB4RUQqcAPxB0qYrvYA0NE1kjK+oqKhD02ZmVmeLF8P998MRRyRzUhTKwIHw9NMwciR89BEMHgzHHQfvvltz+SeeSFYc8XwUayzfnhQAbYD5EfFHoFxS7wLFZA3kkkP606KkGb98cOJKQzMql6/g9udmsfc1T3LfK+V8b4/ePP7TPTl8YDdUy2y2bVo25/hBPXhk0lze+3RRIU/BzMysurrej5QD3XO2uwFz8n2xiJiT/p0BPAkMrKHM8IgojYjSzp0759u0mZnVx+jRyWSYDTnUY1UkOPbYZCLNiy+Ghx6CLbeESy9NJuXMNWYMtG8PO+xQ+LjWcXklKdIulecDv0h3tQDuLFRQ1jC+1n49fnbA5jzz1oeMmvC/+7EXZ3zEN69/lotHTWKbbhvyyI/34JcH92ODOixZ+t1dewHw9+dnNWzQZmZmq1DP+5FxQF9JvSW1BI4D8lqlQ1IHSa3S552A3YDJq69lZmYFNWJEMiRjn30a7zXbtEkm1Jw6NVkB5JJLYKut4O67k94TkMxHsffeybKmtkby7UlxOHAI8AV8+auClx5dC3xnl15s231DLnt4MlPnzuecEa9y7PAX+HxxJf934vbccdogNutS97ey64brcWD/jRn50mwWLq0sQORmZmYrqfP9SERUAmcBjwJTgLsjYpKkyyQdAiBpR0nlwNHAzZImpdW3AsZLmgA8Afy22qogZmbWmObPh3/9C44+OptkQM+e8M9/wpNPQocOSS+LvfaCBx6AmTM91KOB5JukWBrJeIEAkJTXdKVelzx7Jc3Ebw4fwKeLljHkD8/wyKS5nLNvX8rO3ZODBnyt1qEdq3Pqbr35bNEy7n/lvQaM2MzMbJXqdT8SEaMjYvOI2DSd14KIGBYRo9Ln4yKiW0S0jYiNIqJ/uv+5iBgQEdumf/9aoPMyM7N8PPRQMidFYwz1WJ0990wm1rzpJpg0KZkfA5ykaCD5pp/ulnQzsKGk7wGnAresroLXJS8e/b7ejgu/sRUTZn/Kzw7Ygh4btWmQdnfo2YEBXdtz29iZnDCoB82a1T/hYWZmloc634+Ymdk6ZMQI6NEDdtkl60igpAS+/3045hi4/HKYOzdZstTWWF5Jioi4RtL+wHxgC2BYRIyppdqX65IDSKpal/zLJEVEzEqPeV3yAjtt94af51QSp+7ei5/8cwLPTP+QPTf3ZGFmZlY49bwfMTOzdcGHHyaTU557LjSry/oPBdahA1x7bdZRrFNqTVKkPSIejYj9gLrcCNS0LvlOdajfWtJ4oJJkDOiDdahrjeTgAV/nytFTuW3sTCcpzMysYNbgfsTMzNYF994LlZXZD/Wwgqs1BRURy4GFktrXsW2vS94EtGzejO/s3JMnp1Uwfd6CrMMxM7N11Brcj5iZ2bpgxIhk+c9tt806EiuwfPvJLAYmSvqrpD9VPWqp43XJm4gTdupBy5Jm3P7crKxDMTOzdVt97kfMzGxtN3s2PPMMnHACrMHE/7Z2yHfizH+nj7r4cl1y4D2SdclPyKeipA7AwohYkrMu+VV1fH1rJJ3Wb8Wh232de18u52cHbEH7Ni2yDsnMzNZN9bkfMTOztd0//wkRHurRRKw2SSGpR0S8GxG317XhiKiUVLUueQlwa9W65MD4iBglaUfgAaAD8C1Jl6bLfm1Fsk75CpLeHl6XvMidsltv7nm5nH+Of5ehg1camWNmZlZva3I/YmZm64ARI6C0FDbbLOtIrBHUNtzjy8kqJd1X18a9LnnT0e/r7di5T0duf+4dKpd7sRYzM2tQa3Q/YmZma7E334RXXnEviiaktiRF7oCfPoUMxNZ+p+zWm/c+XcR/J3+QdShmZrZu8f2ImVlTNWJEMg/FscdmHYk1ktqSFLGK52Yr2W+rjenecT1uGzsz61DMzGzd4vsRM7OmKCJJUgweDF27Zh2NNZLakhTbSpov6XNgm/T5fEmfS5rfGAHa2qOkmThpl16Mm/UJE8s/yzocMzNbd/h+xMysKXrtNZg2zUM9mpjVJikioiQi2kXEBhHRPH1etd2usYK0tccxO3anbcsS96YwM7MG4/sRM7MmasQIaN4cjjoq60isEdXWk8KsTtq1bsHRpd15+PU5zJu/OOtwzMzMkDRE0jRJ0yVdUMPxwZJekVQp6ahqx06S9Fb6OKnxojYza+JWrICRI+GAA2CjjbKOxhqRkxTW4E7atReVK4I7X3w361DMzKyJk1QC3AAcBPQDjpfUr1qxd4GTgbuq1e0IXAzsBAwCLpbUodAxm5kZ8NxzMHs2nHBC1pFYI3OSwhpc705t2WeLLvzjhXdYvGx51uGYmVnTNgiYHhEzImIpMBI4NLdARMyKiNeB6mtoHwiMiYiPI+ITYAwwpDGCNjNr8kaMgPXWg0MPrb2srVOcpLCCOHX33nz0xVIenjAn61DMzKxp6wrMztkuT/cVuq6ZmdXXsmVw993wrW/B+utnHY01MicprCB23XQjtth4A24dO4sIrxZnZmaZUQ378v0fU151JQ2VNF7S+IqKijoFZ2ZmNXjsMfjwQ6/q0UQ5SWEFIYlTduvFlPfn8+LMj7MOx8zMmq5yoHvOdjcg325+edWNiOERURoRpZ07d653oGZmlhoxAtq3h4MOyjoSy4CTFFYwhw3sSoc2Lbj1WS9HamZmmRkH9JXUW1JL4DhgVJ51HwUOkNQhnTDzgHSfmZkVyqJF8MADcMQR0KpV1tFYBpyksIJp3aKEE3bqwZgpH/DuRwuzDsfMzJqgiKgEziJJLkwB7o6ISZIuk3QIgKQdJZUDRwM3S5qU1v0YuJwk0TEOuCzdZ2ZmhTJ6NHz+uYd6NGEFTVJ4XXL7zs69KJG4/flZWYdiZmZNVESMjojNI2LTiLgi3TcsIkalz8dFRLeIaBsRG0VE/5y6t0bEZunjtqzOwcysyRgxArp0gb33zjoSy0jBkhRel9wANmnfmm8M+Bp3j5vNgiWVWYdjZmZmVlwqK2HJkqyjMCsO8+fDv/4FxxwDzZtnHY1lpJA9KbwuuQFwym69+HxJJfeOn117YTMzM7Om5KSToFcveP31rCMxy96DDyZJuxNOyDoSy1AhkxQFX5fcS36tHQb26MDAHhvyt+dmsWJFcSxH+unCpdw2diZXPTK1aGIyMzOzJub11+Guu6CiAvbcE55/PuuIzLI1YkSStNt556wjsQwVMklR8HXJveTX2uOU3Xoz66OFPDFtXmYxrFgRjJ3+IeeMeJVBVz7GpQ9P5sYn3+a252ZlFpOZmZk1YZddBu3awfjx0KkT7LcflJVlHZVZNioqYMwYOO44UE1fB62pKGSSouDrktva46CtN2GTdq25beysRn/t9z9bxPWPvcWe1zzBiX95kSenzeP4Hbvz73N2Z98tu3DVI1OZUbGg0eMyMzOzJmziRLjvPjjnHNhuO3jmGdhsMzj44GT5RbOm5p57YPlyr+phBU1SeF1y+1KLkmZ8Z5eePDv9Q6bN/bzgr7ds+QoeeWMup/5tHLv99nF+P+ZNum3Yhj8etx0v/XI/Lj10a/p/vT1XHjGA1i1K+Nk9E1juYR9mTdJHC5Zw7Zg3Kf/ESyWbWSO6/HLYYAP4yU+S7U02gSefhO23h6OPhr//PdPwzBrdiBHQrx8MGJB1JJaxgiUpvC65VXfCoB60at6Mvz03s2Cv8XbFAn4zegq7/OYxzrjzZSbN+Ywf7LUpT/18L0YM3ZlDt+tK6xYlX5bfuF1rLj2kP6+8+yl/fXZGweIys+ITEdz/Sjn7XfsUf3rsLS64byIRTlaaWSN4443kV+NzzoGOHf+3v0OHpLv7XnslE2r++c+ZhWjWqN59F559NulF4aEeTV5B13WJiNHA6Gr7huU8H0cylKOmurcCtxYyPmtcHdq25Ijtu3L/K+9xwqCebNy+FR3atKRFyZrlyhYurWT0xLncPW42L836mJJmYp8tu3Dcjt3Zc/PONK+l/UO3+zqjJ77PNf99k3227MJmXTZYo3jMrPjN/nghv3zwDZ5+s4Lte2zIUT07cMszM/nv5A84sP8m/9/eeYdJUWV9+D2TyHnIOUpGyVkURRQF0VUQMaIIGFb9MLCG1RXXsK6sAUQWBBaV7CKuAQNJosQBhjjAwBCGzAwwTOz7/XFrpB26e7q6e7p7hvs+Tz1T3VX1q1M1p6tunbr3nFCbZzAYijpvvAGlS1/qReFM6dK6BOPgwfDkk5CSAn/5i3lwMxRtZs/WfwcPDq0dhrDAFJ81BJWHutVn1rokbvt4xe/flS4WRYVS0VQoGUP5kjFUKJk7/8e/v8+XiqFUTCRbD6cwa10S32w+wrmMbOpVKskLfZtyZ9uaVClb3GubRIQ3B7aiz7hl/N/cLcwf0SXfwIbBYCic5DgU01cl8t6PuxDg9f4tuK9zXRxKsWz3CcZ+u51rm1T+Q48rg8FgCCjx8boXxZgxUKmS63WKF4d58+Dhh+Hll+HsWXj3XROouJJZuRImTNABqxYtQm1N4Jk5Ezp00HlZDFc8JkhhCCpNqpZh4ePdSTx1gbNpmZxJy+JMWiZnrb9n0rI4cOoCpy9kci49261OdKSQlaMoFhVBv1bVGdShNh3rV0R8vHlXLlOMvw1oyZMzN/Hp8n08fp25QBoMRY1dyed4Yf4WNied5bqrKjN2YCtqli8BQATCX29rwb2T1zJlxX5zDTAYDAXHG29AqVLw7LOe14uKgmnTdPWP997TgYqJEyHSBFGvKLKz4c03dSUYhwO+/hqmTIFBg0JtWeDYtQs2bYJx40JtiSFMMEEKQ9BpVascrWqVy3e97BwHZy9mXQpmXPhjMKNWhRLc1qYG5UpEB8Su29rU4Idtyfzr5930blaFptXKBkTXYDCElozsHMYvTuCTZXspUzyaDwZfTf82NS4LanZrFMtNLaoyfkkCd7atRbVy3vfIMhgMBq/Yvh3mzIEXXnDfi8KZiAj46CMoX14/qJ47pxNqxsQUvK2G0HPgAAwdqnM1DB0KL70Ew4bpIRGrV8M//gHRgWkHh5SZM3UvobvvDrUlhjDBBCkMYUtUZASxpYsRW7pY0Pb5twEtWLPvFKPnxvHfUd38zpdhKBrsOXaOhpVLExFhutkWNtYnnuaF+VvYe+ICA6+pySu3NqdiKfeN+5f7Naf3+8t4+/sd/GvwNUG01GC4Qli2DGJji2Z3dW8YOxZKloT/+z/vtxHR25UrB88/rwMVc+dqHUPRZe5cePRR3XtixgwdpABYsgSeew4++ADWr9dBrxo1QmurPyilgxS9ehXu4zAEFPMEZjA4Ual0Mcbe3pJth1P5ZOneUJtjCDE5DsXr38Rz47jl/O1/20NtjsEG5zOyefXrbdz16WrSsxxMe6gD4wZd7TFAAVC7YkmG92jAgs1H2HDAFJUqKohIXxHZJSIJIvKii+XFRGS2tXytiNSzvq8nIhdFZLM1TQy27UWGc+f0G+BeveD66+HYsVBbFHx27IBZs+CJJ3Sgxi7PPQeTJsH338PNN0NqauBtNISe8+f1b+Xuu+Gqq/QwiNwABeheNB98AF9+qZe1bauDf4WVjRth925d1cNgsDBBCoMhDze3qk7/NjX48Jc9xB9JCZ7xV6cAACAASURBVLU5hhCRlpnNYzM2MHVlIk2rlWHaqkS+3nw41GYZvGDxzmPc+P4yZqw5wANd6vHjMz3pdVUVr7cfdV1DqpUtzmsLt+NwmJKkhR0RiQTGAzcDzYF7RKR5ntWGAWeUUo2AccA7Tsv2KqWutqYRQTG6qLFiBbRpo/MrjBypq1U89JB+g3olMXYslChhrxdFXh59VD+crlqlgz0nTwbOPkPo2bgR2rWDqVN1gswVK6BhQ9fr3nMP/Pab7mHTuzf885+F8zc1c6YesnLnnaG2xBBGmCCFweCC1/u3oHzJGEbP3UJmtiPU5hiCzPHUdO7+dDWLdx7jbwNa8M2T3elYvyIvzN/CjqPmzVW4cvJ8Bk/N3MTD09ZTpngU80d25bX+LShVzN7IxpIxUYy5pSlbD6cwd0NSAVlrCCIdgQSl1D6lVCYwCxiQZ50BwHRrfh7QW3zNxGy4REaGzr3Qs6cesrB8ua5O8M9/6t4AH30UaguDx65duhfF449D5cr+aQ0eDAsW6CohPXvCYRNAL/Q4HPp30bkzXLgAixfrHCT55Zto0QLWrYMBA2D0aN374ty54NgcCBwOXXr0ppugYsVQW2MII0yQwmBwQYVSMfx9YEt2HE3l4yUJoTbHEER2Jqdy+/iV7DtxgckPtOf+LvWIjozg4yHXUK5ENCM+30DKxaxQm+kXSafTUIXxbYsblFJ8tfEQN7y/jO+3HeXpGxrzvyd70LZOBZ81+7epQfu6FfjHol2kphfu/7eBmoBztOmQ9Z3LdZRS2UAKkJvVsL6IbBKRZSLSo6CNLTJs3QodO+qymY88Aps3Q7duetmoUXDrrXr4wpYtobUzWIwdq8uKjh4dGL1+/eCHH+DQIejeHRJMW6XQcvQo9O2rfaNfP4iL08OivKVsWV2u9t134auvdBnP7YVkiOqKFdqHzVAPQx5MkMJgcEOfFtW445qajF+SwLbDBTfsw+FQLIw7wq7kQhT5LqIs232CP32ymhylmDuiC9c3rfr7siplijPh3rYcPnORZ2dvLrTDAMYvSaDHu0u4fcIqluw6XuiDFQnHz/PA1HU8OyeOBrGl+O6pHjx9QxNiovy7vYkIr/VvwakLmXz4854AWWsIEa56ROR1fHfrHAXqKKWuAZ4FvhSRy0o/ichwEVkvIutPnDjht8GFmpwcXS6zfXtIToZvvtF5FMqUubSOCHz2mX5zes89kJYWOnuDwe7deojGqFFQxfuhZ/ly7bX6jfu5c9CjB2zbFjhtQ3D49lto3Vo/rH/6qQ4yeFP1JS8iOuj3yy9w5owOEM6eHXh7A83MmXoIVP/+obbEEGaYIIXB4IG/3taC2NIxPDtnMxnZOQHXTzqdxpDJa3hq5ibumriKrYdCmwNjYdwRev9zKX+etYnvth7lQkZ2SO0JJl+uPcjD09ZRu2JJFjzejRY1Li+T265uRV65tTm/7DxeKHvYfL35MP9YtItujSpx8lwGD01dx8AJq1haCIMV+06c5+lZm7hx3DI2JJ7mtduaM3dEVxpXLZP/xl7SsmY5BrWvzbRViSQcPx8wXUPQOQTUdvpcCzjibh0RiQLKAaeVUhlKqVMASqkNwF6gSd4dKKUmKaXaK6XaV/a3K39hJjFR50l47jn9RnjbNt1jwhWVK+tSmtu3B653QbgydiwUK1Ywx9m+vR5GI6KHflwpPVMKO+np8NRT+vdRo4au0jF8uP4/+kOvXjqvRZs2eljQM89AVpj2BszK0hVM+veH0qVDbY0hzJDC1jB1R/v27dX69etDbYahCLJk53EemraOUb0a8nzfpgHRdDgUX6w9wFvf7yRChD/3bsz01YmcS8/mi0c60bLm5Q/IBc1XGw8xem4c9WNLcfpCJmfSsigWFUGPxrH0aVGNG5pVzbcyQmHE4VC888NOPl2+j+uuqsxHQ9pS2kMOA6UUz86JY8Hmw0x9sIOthIyhZM2+U9w/5TeuqVOe/wzriCDM33iIjxcncPjsRa6pU56nb2hCz8axhPNQ/MSTF/hw8R4WbDpMsahI7u9al+E9GlCpgEoVnzyfwXX/WEq7ehWY9lDHAtlHMBCRDUqp9qG2IxRYQYfdQG/gMLAOGKKUinda53GglVJqhIgMBu5QSt0tIpXRwYocEWkA/Gqt57b0yxXZHlEKpk/XD12gc03cf793D1zPPad7XixYoMfVFzX27IGmTfXD4nvvFdx+9u3TQYrsbP1WvlGjgtuXwT+2b9cBhK1b4c9/hrff1kOBAklmpv5tffihHg40Zw5Ur+6/rsMBBw7oISnJyXo/WVmXJjufU1Jg9Wr4+mvTk+IKwtv2iAlSGAxe8Py8OOZtOMRXo7pxde3yfmklnU7j+XlbWL3vFD0ax/L2na2pWb4ESafTGDxpDeczsvny0U4u3+QXFHPWJfHCV1vo2rAS/76/PTGREaxLPMOi+GR+jE/mSEo6EQId61fkphbV6NOiGjXLlwiafQVFelYOz8zezPfbkrmvc13+eltzoiLz72B2MTOHgRNWcjQlnf892Z3aFcO7Vn3C8XPcMWEVVcoWZ/6IrpQreSkRV2a2g3kbDjF+iQ5WtLWCFT3CLFhx8FQaHy7ew383HSY6Urivc10eu7YhsQUUnHBm8q/7GPvtDj57sP0fhgAVJq7kIAWAiNwC/AuIBD5TSr0pIn8D1iulFopIcWAGcA1wGhislNonIncCfwOygRzgr0qpbzztKyzbI7/+ChMnQsuW+k1r+/b5J+TzlhMn9BvgBQv0Q/L06VCvnvfbZ2ZCly76wWfLFv1WuSjxwAP6bfH+/VC1gK8fO3boYR+lS8PKlVAzb+oVQ0hRSg/peOYZPfxp2jS45ZaC3efMmTonTJkyOlDRs6f326am6t5QcXH6t7lliw6seErMKaKvLTEx+m/ulPdz7ne1aukeVTFF7yWYwTVhEaQQkb7AB+hGwWSl1Nt5lhcD/gO0A04Bg5RSiVZ98h3ALmvVNfmV/QrLRoGhyJCansVN45ZTMiaSb5/qQfHoSNsaDofii98O8tZ3O4gQ4aV+zRjcofYfHgSTTqcx6NPVpGXl8OUjnWle47KhzwHni7UHeOm/2+jZpDKT7mt32bEppdh2OJVF8cksik9mj9XtvVXNcvRpXpWbWlajcZXSYfVA6w0nzmXw6H/WE3foLC/d0oxh3evbOoYDpy5w20crqFWhJF+N6uqTTwSDE+cyGDhhJelZDv47qqvbgEpmtoO5G5IYvziBIynpYROsSDqdxseLE5i38RBREcLQznV57NoGVCkT4LdOHsjMdtD3g+UoBYue7ul3votQcKUHKYJJ2LVHvvtOl/aLjr70cFGyJHTtqnMaXHutHr9ezIeA3//+B8OGwdmz8Pe/64evCB9+H7t2Qdu2Oljx44++aYQjCQm6F8VTT8H77wdnn+vX6yE3tWrpYSCxscHZr8Ezx47BiBE6mNenjw7mVasWnH1v26avAXv3wjvvwLPP/rGXU06O7omzZcsfAxL7919ap3x5nTujdWs9lKR1a6hdWwcXnAMQkeHZFjKEDyEPUlh1yXcDN6LHeq4D7lFKbXdaZxTQ2ql75UCl1CArSPE/pVRLb/cXdo0CQ5Fj+e4T3P/ZbzzWswFjbmlma1t3vSdcceDUBQZPWkN6Vg4zh3emabWCC1RMX5XIXxfGc33TKky4t61XD9r7TpxnUfwxFsUnsznpLAD1Y0vRp0VVbmpRjatrlSciwreH2qwcBxezcriYqacKJWP+8NY/UOw5do6Hpq3j5PkMPhh8DTe18K2hsHjnMR6etp4729bivbtah12gJi0zm8GT1rDn2HlmP9aZ1rXy7wWUkZ3D3PWHmLBEByva1a3A0zc0pnuj4AYrDp1JY/ySBOauP0REhDCkYx1G9WpIlbLBC044s3TXcR6cuo4xNzflsWvd1KwPY0yQIniEVXtk7ly4915o1QoWLdJvcpcvh6VLYdky/VYUdFfzLl0uBS06d/bc/fzcOf2gM3myfmCZMUPvwx+mTNFvfN99V3dTLwo89JAuO7p/f/AeSEH/f/v21f+TxYv/mLTUEDxyf28TJ8L8+fq7t9+Gp58OfiAuNRUefBD++1/405/07zw3ILFt26XktRERcNVVlwISuUGJWrX8z5dhMBAeQYouwGtKqZusz2MAlFJvOa2zyFpntTVmNBmoDNTFBCkMYciYr7Yya91B5o3oQru6+ddz9qb3hCsST+pARWaOg5mPduaqaoFvYOR2Ye/TvCofD2nr09vh5JR0ftqezKL4Y6zZd4psh6Jq2WLc0KwqlUrFcDErh7TMHC5m5ZCeG3zIyuFiloP03+dzfp/PzlMxI0Kgda3y9GxSmZ6NY7m6dnmvhmN4YlXCSR77fAPFoiKZ8kB72vg5fGfcT7v54Jc9jL29JUM71/VLK5DkOBSPzVjP4p3HmXRfe25obq+bcUZ2DnOsYMXRlHTa163A0zc0oVujSgUarDhy9iLjlyQwZ30SgnBPx9qM7NWIauVCE5xwZti0dazdf5rFo68Nak+OQGCCFMEjbNojU6fqh/6uXXWPh3IuhhCeOqWHgixbph9s4+L0g1VMjA5U5AYtunTRvS9ADyO4/3794P388/D66771wsiLUnDXXXp8+urVekhKYWbvXv2w9+STMG5c8Pf/zTcwcKDu3v/dd4HPeWBwz5kzegjDxImwc6fuhfDAA7q6S5PL8u4GD6V0XpQXX9S5JSpW1AGI3J4RrVtD8+a62obBUECEQ5DiT0BfpdQj1uf7gE5KqSec1tlmrXPI+rwX6ASUBuLRPTFSgZeVUr+62MdwYDhAnTp12h04cKBAjsVgyOV8RjY3jVtOTFQE3z3VgxIx7nseJJ1O44X5W1i19xTdG8Xyzp/c955wxf6TFxj06WpyHIpZwzsHtGrBJ0v38s4PO+nXqjr/Gnw10X4++AOkpGXxy85j/Bh/jGW7T5CenUOJ6EhKREdSPDqSkjGRlIjR87nfO3/Ou7x4dAQHTqWxfM8J4pLO4lBQpngU3RrG0rNJZXo0jrWdC2Lu+iTGfLWVBpVL8dmDHahVwf9cEg6H4uHp61iZcJLZj3WhbZ0Kfmv6i1KK1xbGM331Af42oAX3d6nns1beYEWHejpY0bVhYIMVR1MuMmHJXmavS0KhGNShNqN6NaJGGOU+2X/yAn3GLWPA1TV57642oTbHFiZIETzCIkjx0Ud6iEGfPrqkYalS3m135oxOurhsmZ42btQPM9HR0KED1K+vx7jXrasfwrp3D6zdp0/rB6YSJfS+C3PG/4cf1udq377AJCz0hS++gKFDdVLCefMCl4fEcDlKwW+/6cDErFm6ekenTnqIx913XwryhQNJSXpYRvXqpneEIeiEQ5DiLuCmPEGKjkqpJ53WibfWcQ5SdATOA6WVUqdEpB2wAGihlEp1t7+waBQYrghWJZxkyOS1DOten1dubX7ZcqUUX6zVvScAXurXnHs65t97whV7T5xn8KQ1KAWzhneiURX/AxUf/rKH93/aTf82NXj/7jZ+90xwhcOhECFgD7Fn0zJZtfcUy3efYPnuExxJSQegQWyp3wMWnRtUopSbqhxKKd7/aTcfLU6gR+NYxt/blrLFA9dYO5uWyW0fryArW/HNk92pXKbgkzl6IreXzKM96vNSv8t91BcysnOYsy6J8Uv2kpyqgxVdGsZSKkYHmErGROm/xay/1nelrO9KREcS6WIY0LHUdD5Zupcv1x7EoRR3d6jN49c1CtvErG99v4NPl+1jweP+J9ENJiZIETxC2h5RCt56C156Sb9FnznTv14OKSm650Ru0CIuTj/0vv9+wQ0hWLYMrrtOD5WYMqVg9lHQ7Nun35g//jh88EFobRk/Hp54Au67TydqLCr5PsKFc+fgyy91cGLzZh0QHDoUHnsMrrkm1NYZDGFHOAQpfB7uofIYJSJLgdFKKbd3fROkMASTV7/exow1B5g9vAsd618a9nHojO49sTJB9554+85Wfr+tTziuAxUiMGt4ZxpW9u3NklKKcT/t5sPFCdxxTU3+cVcblw+N4Y5Sir0nzrN890mW7znBmn2nSM9yEB0ptKtbwRoaUpnm1csSESGkZ+Xw/LwtLIw7wuAOtXnj9pYB6TmSl/gjKdwxYRXX1CnP58M6FUjwxxu+33qUUV9u5OaW1fj4nrY+5wdxR3pWDnPWJ/Hpsn0cPnvR1rbFoyMuBTNiIikRE8XOo6nkOBR/aleLx69rFPaVUs5nZHPde0upWb4EX43sGvDzW1CYIEXwCFl7RCkYM0YnxrvvPvjsM4hyX07Z530E483ryy/Dm2/C7Nn6LXRh45FH4PPPdbAiHKqVjB0Lr7yih5588IF5ex4I4uJ0pY7PP9eBitatYeRIGDIEyhZ80nODobASDkEKU5fcUGS5kJHNzR/oEUg/PN2DEtGRfPnbQf7+rf+9J1yx59g57vn3GiJEmDW8Mw1sBiqUUry7aBefLN3LoPa1+fsdrQplgMIV6Vk5rE88w697TrBs9wl2Juvs9bGlY+jeKJakMxfZcOAML/RtyohrGxRoPoX5Gw7xf3PjGN6zAX+xmVw1EGw4cIYh/15Dy5rl+OKRTgVeccThUFzMyuFCZjYXM3O4kJHDxaxsLmToXCRpmdl5/lrz1vILmdnUrliSET0bUqdSeAcnnJm34RCj58bxz7vacGe7WqE2xytMkCJ4hKQ94nDot+WffKK7l48fX7jfmGdl6VKaO3fqxH516oTaIu/Zv1/3ohg5Ej78MNTWaJSC0aN1D5hXX9V5RMIdhwMuXNCBttyqEaEOrly8qJPRTpyo86YULw6DBunfXKdOobfPYCgEhDxIYRlxZdclNxRp1u47xaBJaxh4TU2On0sPaO8JV+w+do57Jq0hKlKYNbwL9WO9G2OslOLNb3cwecV+7u1UhzcGtCw0b3994XhqOr/u0b0sft1zkrTMbN67qw23tg7O26xXFuheNuOHtKVf6+CNQ95/8gJ3TFhJ+ZIxzB/ZlYqlTM3xgsLhUAz8ZBVHzl5kyehelHYzzCicMEGK4BH09kh2ts5/MGOGTmT59ttF42Fp3z64+mo9LVlSeEobPvqoztexbx/UrBlqay6hlC4XO3WqTuT59NOhs+PUKThy5NJ09OgfPx85AsnJ2rdzEblU5jLv5FwC09WymBg97MmXKSZGB0q++04PlzlzRidEHTFCJ5CtmH8SdYPBcImwCFIEExOkMISC17+JZ+rKRErFRPKXfs0Y0rFOgb6p35mcypB/ryUmMoLZj3WmbiXPgQqlFK9/s51pqxJ5sGs9/npb87Ark1mQOByKLIeDYlHBa9xmZjsYPGk1O5PPsfCJbgHJI5Ifpy9kcseElaSmZ/PVyK7U8zKAZfCdTQfPMHDCKkZc25AXb24aanPyxQQpgkdQ2yMZGXDPPbqs4Nix8Je/FI0ARS6ff66Hrrzxhh4CEu4kJkLjxjofwccfh9qay8nO1m/+v/pKBysefLBg9rN1K2zY4DoAcfSo7imTl4oVdSLHGjX0VL26/i4nR6/vPGVmXv6dqyl3vYwM95NzIMQT0dFwxx06OHHttUXrd2YwBBETpDAYgsDFzBw+X3OAm1tVK5DeE67YcTSVIf9eQ4noSGYN7+K2m7zDoXjl6218sfYgj3Svz0v9ml1RAYpQkpySzq0f/UrZEtF8/Xg3ygQwSWde0rNyGPLvNcQfSeXLRzvTrm7oq4tcKfzfnDi+iTvCj8/0DPvAkAlSBI+gtUfS0nRyzB9/1HkGnnqq4PcZCoYO1dUSfv1Vl0INZ4YPh+nTdfnRWmE6FCwjA267DX75RVf8GDgwMLoXLugcIp9+qqtc5FK+/KXAQ27wIe989eqhK5HqcHgOYmRk6GBHs2ZQ1V4pb4PBcDkmSGEwFGHij6Rw7+S1lIqJYtbwzpclG3Q4FGO+2srs9UmM7NWQ52+6ygQogsyafae4d/JabmxWlU+Gti2Q8+9wKB7/ciM/xCczYUhbbm4VojJ3VyjHU9O57r2ldGlYickPdPBbL8eh2HE0lV3J5wKe68IEKYJHUNojKSlw662wahX8+996uEdRJSVFV0lQSldPKFcu1Ba55sABaNRIByrGjw+1NZ45fx5uvFGXef3uO+jd23etbdt0YGLGDP2/atpU9yS59VY93KVEeFZqMhgMocHb9kghzqpkMFy5tKhRjs+HdeJ8RjaDJ63h0Jm035flOBSj58Uxe30ST13fyAQoQkTnBpUYc3NTfohPZuKyfQWyj7e+38H325J56ZZmJkARAqqULc6TvRvz847jLNt9wvb2SikSjp/jP6sTGTFjA23f+IlbP1rB8/O3cD7Dyy7IhiuPkyf1Q+WaNbrEaFEOUIAOSnzxBSQl6ZKe4cpbb+khAC++GGpL8qd0afj2W53gc8AAWLvW3vYXL+q8G926QatWMGkS9Ouny8du367zXTRqZAIUBoPBZ0xPCoOhELP1UAr3Tl5D2RLRzH6sC1XLFOPZOXEsjDvCszc24anejUNt4hWNUoonZm7i+61HmTGsE90axQZM+z+rE3n16/grMtdIOJGRncNN45YTGSH88HTPfMvbJp1OY/XeU6zae5JVe09x/FwGADXLl6Bbo0p0bRhLl4aVqFo2sF2fTU+K4FGg7ZEjR/Qb8H37YP58uOWWgtlPOPLGG7oyxYwZeghIOHHwoH4of+QRmDAh1NZ4z9GjuorK6dOwfDm0bOl5/e3bdUDiP//RCSSbNNG9Ju6/H2IDd38zGAxFFzPcw2C4QohLOsvQKWupUDKGq6qV4aftx3ihb1NG9moYatMM6HK1t49fyakLmUx/qCMNKpeilJ/VIH7efozhM9bTu1lVJg5tV2TKyRZWftlxjGHT1/Nyv2Y80qPBH5YdP5fO6r2nWL33FCv3niTp9EUAYksXo2vDStYUS+2KJQo00HSlBylEpC/wAbra2GSl1Nt5lhcD/gO0A04Bg5RSidayMcAwdLWxp5RSizztq8DaI4mJugfF8ePwzTfQq1fg9xHO5OTAddfpIR+bNkHDMLrHjRwJU6ZAQkLhKpcKumRq9+56OM2KFdDgj9cw0tN17opPP9XLo6Phzjt1cMIkkDQYDDYxQQqD4Qpic9JZ7pu8lnMZ2S4flAyhZe+J8wz4eOXvXfjLFI+iWtniVCtX/Pe/VcsWp7rT34qlYlw+tG45dJZBn66hSdXSzBrehRIxhaQsXxFGKcWDU9ex8cAZFjzRjYTj51mVoHtK7Dl+HoCyxaPo3MAKSjSKpXGV0kHt/XIlBylEJBLYDdwIHALWAfcopbY7rTMKaK2UGiEig4GBSqlBItIcmAl0BGoAPwNNlFI57vZXIO2RnTvhhht0sszvv4dOnQKrX1g4eBBat9Z5D379VT8wh5qkJB0wefhhmDgx1Nb4Rnw89Oypk1yuWKETWe7apXtNTJume1rk5tt48EGoXDnUFhsMhkKKCVIYDFcYu5LPcfhsGtc3Ndmnw5Gk02msP3Ca5JQMjqWmczTlIsmpGRxLSef4uXQceS7FMZERVClb7PfARbWyxalSthiTlu+nREwEX43sRuUyxUJzMIbLSDh+nr7/Wk629Y8sER1Jh/oV6dqwEt0axtK8RtmQ9ni5woMUXYDXlFI3WZ/HACil3nJaZ5G1zmoRiQKSgcrAi87rOq/nbn8Bb49s3gx9+kBEhK7k0bp14LQLI3Pm6DKaw4bpN/mhZsEC3bNlzx6oWzfU1vjOb7/pnjq1a+sqFkuXQlSUrv7x2GO6F0uESWVnMBj8w9v2iH99jg0GQ9hwVbUyXFWtTKjNMLihdsWSl1VhySU7x8HJ85kkp6aTnHKR5JR0klMz9HxqOtsOp/DzjmOkZzmoWCqGqQ92NAGKMKNRldK8d1cbDpxKo2ujSrSpVZ6YKNOgDxNqAklOnw8Bebsi/L6OUipbRFKAStb3a/JsWzPvDkRkODAcoE4gu/srBc8+q8sz/vyzzgFwpXP33bB4sR5+MGVKqK3RjBpVuAMUAB07wtdf6wSY6ek6EehDD5mymwaDISSYIIXBYDCEmKjICD30o1xxqF3e5TpKKVIvZhMTFWGGeIQpt19z2bOrITxw1YUlbzdSd+t4sy1KqUnAJNA9Kewa6BYRmDVLV1Mo7A/BgeSTT3QVjRy3o26Ch0jR+d9cf71Oplm2rOk1YTAYQooJUhgMBkMhQEQoVzIMxl8bDIWPQ0Btp8+1gCNu1jlkDfcoB5z2ctuCpUqVoO6uUCAC9eqF2oqiSXnXgXKDwWAIJiZMajAYDAaDoSizDmgsIvVFJAYYDCzMs85C4AFr/k/AYqWTdi0EBotIMRGpDzQGfguS3QaDwWAwXJEUaJBCRPqKyC4RSRCRF10sLyYis63la0WkntOyMdb3u0TkpoK002AwGAwGQ9FEKZUNPAEsAnYAc5RS8SLyNxHpb602BagkIgnAs1xKmBkPzAG2Az8Aj3uq7GEwGAwGg8F/Cmy4h1XyazxOJb9EZKFzyS903fEzSqlGVsmvd4Dckl+DgRZYJb9ExGPJL4PBYDAYDAZXKKW+A77L892rTvPpwF1utn0TeLNADTQYDAaDwfA7BdmToiOQoJTap5TKBGYBA/KsMwCYbs3PA3qLLhw/AJillMpQSu0HEiw9g8FgMBgMBoPBYDAYDEWUggxSuCr5lTf1+R9KfgHOJb/y2xYRGS4i60Vk/YkTJwJousFgMBgMBoPBYDAYDIZgU5DVPYJa8ktETojIAbtG5kMscDIMNMLJFnM84W1LuGiEky3meApGI5xsMceTP0WkRmL4s2HDhpOmPVJoNMLJlnDRCCdbzPEUjEY42WKOJ7xtCVl7pCCDFEEt+aWUquyvwXkRkfVKqfah1ggnW8zxhLct4aIRTraY4ykYjXCyxRyPIZww7ZHCoxFOtoSLRjjZYo6nYDTCyRZzPOFtSyjbIwU53MOU/DIY5dYJgAAADnBJREFUDAaDwWAwGAwGg8HgNQXWk0IplS0iuSW/IoHPckt+AeuVUgvRJb9mWCW/TqMDGVjr5Zb8ysaU/DIYDAaDwWAwGAwGg6HIU5DDPYpCya9JYaIRKJ1w0QiUTrhoBEqnKGkESidcNAKlU5Q0AqUTLhqB0gkXDUPRIpz8KlxsMcdTMBqB0gkXjUDpFCWNQOmEi0agdMJFI1A64aLhE6JHVxgMBoPBYDAYDAaDwWAwhJaCzElhMBgMBoPBYDAYDAaDweA1JkjhAhH5TESOi8g2PzRqi8gSEdkhIvEi8mcfNIqLyG8iEmdpvO6HPZEisklE/ueHRqKIbBWRzSKy3keN8iIyT0R2Wuemiw8aV1k25E6pIvK0DzrPWOd1m4jMFJHiPmj82do+3o4NrnxMRCqKyE8issf6W8EHjbssWxwikm82Xjca/7D+P1tE5L8iUt5HnTcsjc0i8qOI1LCr4bRstIgoEYn1wY7XROSwk7/c4osdIvKkiOyyzu+7njQ82DLbyY5EEdnsg8bVIrIm93coIh190GgjIqut3/M3IlI2Hw2X1zM7PutBw67PutPx2m89aHjts+40nJZ767PubLHlt4aiiTu/FpF6InLRyT8m2tVwWl5HRM6LyGgf7OjoZEOciAz0QeNGEdlgXY82iMj1Pp6TStZv6byIfOyLhrVsjIgkiL7e3+RBw+W1S0RiRGSqdTxxItIrH1vc6USLyHRLZ4eIjPFB4175Y3vJISJX29GwlrUWfc+It+xx217yYIsdn/V4X/DSZ93ZYcdn3WnY9Vl3OnZ81tP/xyufzbONrXaABx1bbRI3GrbaRh50bLXT3GgE7N4rXrYD3Gxrqw3tQcd2m96Fhq12WsBQSpkpzwT0BNoC2/zQqA60tebLALuB5jY1BChtzUcDa4HOPtrzLPAl8D8/jikRiPXz3E4HHrHmY4DyfupFAslAXZvb1QT2AyWsz3OAB21qtAS2ASXR+V1+Bhr76mPAu8CL1vyLwDs+aDQDrgKWAu19tKMPEGXNv5OfHR50yjrNPwVMtKthfV8bnYD3QH7+58aO14DRNv6vrjSus/6/xazPVXzRybP8n8CrPtjyI3CzNX8LsNQHjXXAtdb8w8Ab+Wi4vJ7Z8VkPGnZ91p2O137rQcNrn3Wn4YPPurPFlt+aqWhO7vwaqOfu2uKthtPy+cBcT/7mwY6STt9XB47nfrahcQ1Qw5pvCRz28ZyUAroDI4CPfdRoDsQBxYD6wF4g0o2Gy2sX8Dgw1ZqvAmwAIjzY4k5nCDDL6TwnAvXsaORZpxWwzwc7ooAtQBvrcyV35yQfHTs+6/F4vPRZd3bY8Vl3GnZ91p2OHZ91p+G1z+bRs9UO8KBjq03ihV6+bSM329lup7nRec2TX9nQ8bod4GZ7W21oDzq22/Te+l5BT6YnhQuUUsvR1Ub80TiqlNpozZ8DdqAfjO1oKKXUeetjtDXZTiIiIrWAfsBku9sGEitK2xNd1QWlVKZS6qyfsr2BvUqpAz5sGwWUEJEo9E3riM3tmwFrlFJpSqlsYBngNiLvjBsfG4AO4mD9vd2uhlJqh1Jqlzc2eND40ToegDVALR91Up0+liIf3/XwuxsHPJ/f9vloeI0bjZHA20qpDGud4/7YIiIC3A3M9EFDAblvPMqRj9+60bgKWG7N/wTcmY+Gu+uZ1z7rTsMHn3Wn47XfetDw2mfzucbb8Vm/7xWGoosv12M7GiJyO7APiPdFw+n+B1Acz78ZdxqblFK517F4oLiIFPNB54JSagWQ7ulYPGmgr2mzlFIZSqn9QALg8s2wh2tXc+AXa53jwFnA7dtHDzoKKGW1UUoAmUCqi/W8vfffg4d7jgeNPsAWpVSctd4p5aHint1rul0NGz7rUsOmz7rTsOuz7nTs+Ky7c+K1z+bBVjvAk2nYaJN4wtu2kRtst9MKGK/bAa6w24b2oBOIe4jfv2lfMEGKICAi9dBR17U+bBtpdXs6DvyklLKtAfwL/UNx+LCtMwr40eraNtyH7RsAJ4CpooeeTBaRUn7aNBgfLmZKqcPAe8BB4CiQopT60abMNqCn1V2vJDqCXNuuLU5UVUodtew7in4DE2oeBr73dWMReVNEkoB7gVfzW9/F9v3RbyjifLXB4gmrq9tnks8wGjc0AXqIyFoRWSYiHfy0pwdwTCm1x4dtnwb+YZ3X9wC3XYA9sA3ob83fhQ2/zXM988ln/bkmeqnjtd/m1fDFZ501/PFZF8fjr98aihZ5/bq+dS9dJiI97GpY998XALtDSf9gh4h0EpF4YCswwqlB7LWGE3cCm3IfNPzQsYOzRk0gyWnZIewHDeOAASISJSL1gXb41jaYB1xAt1EOAu8ppfwJwg/Ct4e/JoASkUUislFEnvfDBl989nf88Nm8Or74rDvs+myg8dVnfW4H5CEQbZJc/GkbBbKd5te9N1BtV3/b0C4IxPUyaBRoCVIDiEhpdLe0p/NExbzCilZfbY0h+q+ItFRKeZ0rQ0RuBY4rpTZIPuMivaCbUuqIiFQBfhKRndZbWm+JQnc7f1IptVZEPkB3EX/FF2NEJAZ9gbV9QbQuOgPQXePOAnNFZKhS6nNvNZRSO0TkHXQE+jy6YeLPjS6sEJGX0Mfzha8aSqmXgJdEj6V9Avirjf2XBF5Cv8Xxh0+AN9BBtjfQXQkftqkRBVQAOgMdgDki0kAp5VNkm3zeaOXDSOAZpdR8Ebkb3TPpBpsaDwMfisirwEL0G7p8yXs90y897OHvNTE/HTt+60rDrs86a1j79clnXZzbQPitoRAgIj8D1Vwsekkp9bW1Tl6/PgrUUUqdEpF2wEoR2cflLyM8abwOjFNKnbd+y8NF5EGbdmC9PGkhIs2ANSIylsvf+nnUsL5vge6O3MfHc/IHfNTIe1HrC9woIq+503DBZ+ieluvRXb1XAa+I67xinnQ6AjlADfT9Z7+I/B+QZUMD0A/lQBrwLxHxeE5cEIUektDB0vhFRAahhxfY0bHtsy6w7bOusOuz7rDrs/nho4arG7HKTw8b7YB8dHrjRZvEy2Pz2DbKxw6v22n56Hh1781H4y940Q7I75x42x4J1vUy6KggjSspbBM2xs550IhGj0d6NkA2/RWb46SAt9BR1UR07oY04PMA2PKaD7ZUAxKdPvcAvvXDhgHAjz5uexcwxenz/cAEP8/J34FRvvoYsAuobs1XB3bZ1XD6filejhtzpQE8AKwGSvp6PHmW1fXm9+SsgR4/e9zy3UT0xfUgUM0PO7z6Xbv43/wA9HL6vBeo7OO5jQKOAbV89JMU+L18tACpfv5vmgC/eaFx2fXMrs+60vDRZ13q2PFbT7Z467N5Nfzw2fxs8cpvzVQ0J2/8Or/fjysN4FcnXz2LHhb2hJ92LLFrh/V9LXQ+lm7+nhPgQfIZ3+/hnIwBxjh9XgR0yUcnv3O/Ci9ykuXVAcYD9zl9/gy42xdb0F3P/+Lluc1rx2BgmtPnV4Dn7OrYXe7GFls+66UdHn3WnYZdn83n/+OVz7o5J7Z91oWmV+0AN9vabpO40bHVNnKxvU/ttHw062Hz3ouP7YB8NL1qQ3vY3nab3hvfK+jJDPcoIESHeKcAO5RS7/uoUVkuZa0ugY5M7rSjoZQao5SqpZSqh77RLFZKDfXBllIiUiZ3Hh0htFX9RCmVDCSJyFXWV72B7XZtccKft9EHgc4iUtL6X/VGjwW3hdWrBBGpA9zhhz2gI9kPWPMPACGJXIpIX3R3yv5KqTQ/dBo7feyPfd/dqpSqopSqZ/nvIXSCwWSbdlR3+jgQm35rsQC43tJrgk76etIHHbB+x0qpQz5ufwS41pq/HrDdLdLJbyOAlwG3Wdat9dxdz7z22UBcEz3p2PFbDxpe+6wrDV981oMtgfBbQyHHnV9b7YNIa74B0Bg9Tt9rDaVUDydf/Rfwd6WUywoDHuyoLzpnAiJSFz3OPdGmRnngW/SD1krPZyQw9ygPGguBwSJSTPRQjcbAbza1S1rtJETkRiBbKeVLW+cgcL1oSqHfENu6j1o2RKBfzMzywQbQD72treOKQt9/bB+PHZ91hx2f9WCH1z7rQcOWzxYwPvms3XaAB/xuk1j42zYKSDvN33tvANuufrWhnXQC0qYPCcGKhhSmCf2geRTdpe4QMMwHje7orkJbgM3WdItNjdbAJktjGz5ku82j1wsfq3ug80nEWVM8uvuPLzpXo7tAbkFfUCr4qFMSOAWU8+N8vI7+0W8DZmBlBLap8Sv6Zh0H9PbHx9AZs39BX+B/ASr6oDHQms9AR6QX+aCRgB7fmOu3+WYUdqMz3zq3W4Bv0IkJbWnkWZ5I/pUSXNkxAz3udAv6Zl7dB40Y4HPreDYC1/tyTqzvp6HHwPrqJ93R2eLj0LkL2vmg8Wf0G6DdwNtYb0E8aLi8ntnxWQ8adn3WnY7XfutBw2ufdafhg8+6s8WW35qpaE7u/Bo9Dj7eug5sBG6zq5FnndfwXCnBnR33WXZstuy43QeNl9G5FzY7TW4z83s6Hus3dxo9BPMQbnow5KPxEvot7C6sqgVuNFxeu9BvX3ehX3z8TD7VxzzolEZXsIhHtzPc9l5wp2Et64VO8p2fr3nSGGrZsQ1418fjseOz+d4XvPBZd3bY8Vl3GnZ91tO59dZnPWl45bN59Gy1Azzo2GqTeNCZhpdtIzfb226nudEJ6L0XHysjYrMN7UHHdpveju8V5JTbPcdgMBgMBoPBYDAYDAaDIaSY4R4Gg8FgMBgMBoPBYDAYwgITpDAYDAaDwWAwGAwGg8EQFpgghcFgMBgMBoPBYDAYDIawwAQpDAaDwWAwGAwGg8FgMIQFJkhhMBgMBoPBYDAYDAaDISwwQQqDwWAwGAwGg8FgMBgMYYEJUhgMBoPBYDAYDAaDwWAIC0yQwmAwGAwGg8FgMBgMBkNY8P/Y8SfqoHpcEwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for sa in samples:\n", " for sp in species:\n", @@ -2185,7 +374,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.7" }, "toc": { "base_numbering": 1, From adeefac180930eb38edf3c3ea07e884afb168493 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 14 Jun 2019 16:54:08 +0200 Subject: [PATCH 26/96] update env with plotnine --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index e360f0b..51b9a99 100644 --- a/environment.yml +++ b/environment.yml @@ -34,3 +34,4 @@ dependencies: - bioconda::multiqc=1.7=py_3 - conda-forge::r-markdown=0.9 - conda-forge::jupyter_contrib_nbextensions=0.5.1 + - conda-forge::plotnine=0.5.1 From c75131b8497dfd6dae311ea0f5dacdc7804d88e1 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 14 Jun 2019 17:41:27 +0200 Subject: [PATCH 27/96] update report --- environment.yml | 5 +- templates/coproID_report.ipynb | 4 +- templates/rmd/coproid_report.Rmd | 98 -------------------------------- 3 files changed, 5 insertions(+), 102 deletions(-) delete mode 100644 templates/rmd/coproid_report.Rmd diff --git a/environment.yml b/environment.yml index 51b9a99..3322aa3 100644 --- a/environment.yml +++ b/environment.yml @@ -31,7 +31,8 @@ dependencies: - anaconda::numpy=1.16.3 - anaconda::pandas=0.24.2 - anaconda::scipy=1.2.1 - - bioconda::multiqc=1.7=py_3 + - conda-forge::matplotlib=2.2.3 + - bioconda::multiqc=1.7 - conda-forge::r-markdown=0.9 - conda-forge::jupyter_contrib_nbextensions=0.5.1 - - conda-forge::plotnine=0.5.1 + - conda-forge::plotnine=0.4.0 diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 5df9536..5df569a 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -28,7 +28,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![](coproID_nf-core_logo_small.png)" + "[![](coproID_nf-core_logo_small.png)](https://github.com/nf-core/coproID)" ] }, { @@ -39,7 +39,7 @@ "[coproID](https://github.com/nf-core/coproID) is a pipeline to identify the source of coprolites, and in general, of a metagenomic sample.\n", "\n", "If you read these lines, coproID successfully finished running and you can find your results below. \n", - "You can find more informations about the different result files in the coproID documentation: [coproid.readthedocs.io/](https://coproid.readthedocs.io/en/latest/output.html)" + "You can find more informations about the different result files in the coproID documentation: [coproid.readthedocs.io](https://coproid.readthedocs.io/en/latest/output.html)" ] }, { diff --git a/templates/rmd/coproid_report.Rmd b/templates/rmd/coproid_report.Rmd deleted file mode 100644 index a805dc6..0000000 --- a/templates/rmd/coproid_report.Rmd +++ /dev/null @@ -1,98 +0,0 @@ ---- -title: "coproID run report" -output: html_document ---- - -# coproID report - -Report generated on the `r Sys.Date()` - -![](coproID_nf-core_logo_small.png) - -## Introduction -[coproID](https://github.com/nf-core/coproID) is a pipeline to identify the source of coprolites, and in general, of a metagenomic sample. - -If you read these lines, coproID successfully finished running and you can find your results below. -You can find more informations about the different result files in the coproID documentation: [coproid.readthedocs.io/](https://coproid.readthedocs.io/en/latest/output.html) - - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -require(ggplot2) -require(rmarkdown) -require(magrittr) -require(DT) -require(plotly) -require(gridExtra) -``` - - -```{r, echo=FALSE, include=FALSE} -d = read.csv('coproID_result.csv', row.names = 1) -``` - -## coproID summary table - -```{r, table1, echo=FALSE} -# d %>% -# knitr::kable(format = "html", col.names = colnames(d)) %>% -# kableExtra::kable_styling() %>% -# kableExtra::scroll_box(width = "100%", height = "400px") -d %>% - datatable( - extensions = 'Buttons', - width = '80%', - options = list(dom = 'Bfrtip', - buttons = c('excel', "csv"), - autowidth='True'), - caption='coproID summary table') -``` - - -## Microbiome composition embedding - - -```{r, echo=FALSE} -e = read.csv("sourcepredict_embedding.csv", row.names = 1) -e['ml'] = as.factor(ifelse(e["labels"] == 'sink', "reference", "test")) - - -g = ggplot(data=e, mapping = aes(x=PC1, y=PC2, label=name)) + geom_point(aes(color=labels, shape=ml)) + scale_color_discrete(name='Organism') + scale_shape_discrete(name='Reference') + theme_classic() + labs(x='DIM1', y='DIM2') -ggplotly(g) -``` - -## Damage profiles - -```{r, echo=FALSE, results='asis'} -files <- list.files(pattern = "\\_freq.txt$") - -samp_names = c() -for (i in seq(1, length(files))){ - # print(files[i]) - afile = files[i] - spt = strsplit(as.character(afile), "[.]") - samp_name = spt[[1]][1] - samp_names = append(samp_names, samp_name) -} -samp_names = unique(samp_names) - -for (i in seq(1, length(samp_names))){ - samp_name = samp_names[i] - print(samp_name) - cat('\n') - cat("### Sample: ", strsplit(samp_name, "_otu_")[[1]][1]," - species: ", strsplit(samp_name, "_otu_")[[1]][2], "\n") - fwd = paste(append(samp_name, "5pCtoT_freq.txt"), collapse = ".") - fwd = read.csv(fwd, skip=3, sep="\t", col.names = c('pos','X5pCtoT')) - rev = paste(append(samp_name, "3pGtoA_freq.txt"), collapse = ".") - rever = read.csv(rev, skip=3, sep="\t", col.names = c('pos','X3pGtoA')) - rever$pos = rev(rever$pos * -1) - rever$X3pGtoA = rev(rever$X3pGtoA) - - f = ggplot(fwd, aes(x=pos,y=X5pCtoT)) + geom_line() + labs(title='5pC>t', y="") - r = ggplot(rever, aes(x=pos,y=X3pGtoA)) + geom_line() + labs(title='3pG>A', y = "") - grid.arrange(f, r, nrow = 1) - cat('\n') -} - -``` - From b79ea19778fd9be31047422dfb09214fd5619b41 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 14 Jun 2019 18:00:45 +0200 Subject: [PATCH 28/96] update environment.yaml --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index 427c23e..c5eb79e 100644 --- a/environment.yml +++ b/environment.yml @@ -31,6 +31,8 @@ dependencies: - anaconda::numpy=1.16.3 - anaconda::pandas=0.24.2 - anaconda::scipy=1.2.1 + - conda-forge::matplotlib=2.2.3 - bioconda::multiqc=1.7 - conda-forge::r-markdown=0.9 - conda-forge::jupyter_contrib_nbextensions=0.5.1 + - conda-forge::plotnine=0.4.0 \ No newline at end of file From 1cef9122543731522b3dff11e29c4e117996da6b Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 14 Jun 2019 18:24:54 +0200 Subject: [PATCH 29/96] update environment.yaml --- environment.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/environment.yml b/environment.yml index 64791d4..c5eb79e 100644 --- a/environment.yml +++ b/environment.yml @@ -35,8 +35,4 @@ dependencies: - bioconda::multiqc=1.7 - conda-forge::r-markdown=0.9 - conda-forge::jupyter_contrib_nbextensions=0.5.1 -<<<<<<< HEAD - - conda-forge::plotnine=0.4.0 -======= - - conda-forge::plotnine=0.4.0 ->>>>>>> nf-core + - conda-forge::plotnine=0.4.0 \ No newline at end of file From c3731e4cb126d2431d2467056bd5376309986692 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 17 Jun 2019 18:30:40 +0200 Subject: [PATCH 30/96] match categorical colors between plots --- templates/coproID_report.ipynb | 111 ++++++++++++++++++--------------- 1 file changed, 62 insertions(+), 49 deletions(-) diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 5df569a..cfcbd81 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "tags": [ "remove_cell" @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "tags": [ "remove_cell" @@ -81,14 +81,67 @@ }, "outputs": [], "source": [ - "def plot_bokeh(df):\n", + "def coproid_summary_plot(df):\n", + " df = pd.read_csv(df, index_col=0)\n", + " organisms = [i.replace(\"normalized_bp_proportion_aligned_\",\"\") for i in list(df.columns) if \"normalized_bp_proportion_aligned_\" in i]\n", + " organisms_clean = [i.replace(\"_\",\" \") for i in organisms]\n", + " if len(organisms_clean) < 3:\n", + " display(Markdown(\"### Plot\"))\n", + " species_text = pd.DataFrame()\n", + " species_text['x'] = [0.25, 0.75, 0.75, 0.25]\n", + " species_text['y'] = [0.25, 0.25, 0.75, 0.75]\n", + " species_text['text'] = ['Unknown', organisms_clean[0], 'Unknown', organisms_clean[1]]\n", + " \n", + " df['samp_name'] = df.index\n", + " df['coproID_prediction'] = ['Unknown'] * df.shape[0]\n", + " df['coproID_prediction'] = np.where(df[f\"coproID_proba_{organisms[0]}\"] > 0.5, organisms_clean[0], df['coproID_prediction'])\n", + " df['coproID_prediction'] = np.where(df[f\"coproID_proba_{organisms[1]}\"] > 0.5, organisms_clean[1], df['coproID_prediction'])\n", + " \n", + " p = ggplot(df, aes(x = f\"coproID_proba_{organisms[0]}\",y = f\"coproID_proba_{organisms[1]}\"))\n", + " p = p + geom_point(aes(color='coproID_prediction'), size=2)\n", + " p = p + geom_label(aes(label=\"samp_name\", color='coproID_prediction'), size=8, nudge_x = 0.02, ha='left', va='bottom')\n", + " p = p + theme_classic() + labs(x=f\"coproID proba {organisms_clean[0]}\",y = f\"coproID proba {organisms_clean[1]}\")\n", + " p = p + geom_text(data=species_text, mapping=aes(x='x',y='y', label='text'), alpha=0.3, color='grey')\n", + " p = p + geom_hline(yintercept=0.5, linetype='dashed', alpha=0.1) \n", + " p = p + geom_vline(xintercept=0.5, linetype='dashed', alpha=0.1)\n", + " p = p + scale_color_manual(name='Predicted Organism', values = {organisms_clean[0]:'#ef7576', organisms_clean[1]:'#c194c8', 'Unknown':'#a2a3a1'})\n", + " p = p + coord_cartesian(xlim=[0,1],ylim=[0,1])\n", + " p.draw()\n", + " return(organisms)\n", + " else:\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "def plot_bokeh(df, organisms = []):\n", " d = pd.read_csv(df, index_col = 0).fillna('sink')\n", + " \n", + " orga_in_endo = [i for i in list(sorted(set(d['labels']))) if i in organisms]\n", + " orga_not_in_endo = [i for i in list(sorted(set(d['labels']))) if i not in organisms]\n", + " colors = {k:v for k, v in zip(orga_in_endo, Set1[9][0:2]) if k in organisms}\n", + " cnt=2\n", + " for i in orga_not_in_endo:\n", + " colors[i] = Set1[9][cnt]\n", + " cnt +=1\n", + " \n", + " d['colors'] = [colors[i] for i in list(d['labels'])]\n", + "\n", " TOOLS=\"pan,wheel_zoom,zoom_in,zoom_out,box_zoom,reset,save\"\n", " source = ColumnDataSource(d)\n", - " labels = list(set(source.data['labels']))\n", - " color_map = factor_cmap(field_name='labels', palette=Set1[9], factors=labels)\n", + "# labels = list(set(list(set(source.data['labels'])).append(organisms)))\n", + "# colors = [\"#e41a1c\",\"#377eb8\",\"#4daf4a\",\"#984ea3\",\"#ff7f00\",\"\"]\n", + "# color_map = factor_cmap(field_name='labels', palette=colors, factors=labels)\n", " p = figure(tools=TOOLS)\n", - " p.scatter(x = 'PC1', y='PC2', color=color_map, alpha = 0.6, size = 6, legend='labels', source=d)\n", + " p.scatter(x = 'PC1', y='PC2', color='colors', alpha = 0.6, size = 6, legend='labels', source=d)\n", " hover = HoverTool()\n", " hover.tooltips = [(\"Organism\", \"@labels\"),('Sample',\"@name\")]\n", " p.add_tools(hover)\n", @@ -214,47 +267,7 @@ }, "outputs": [], "source": [ - "def coproid_summary_plot(df):\n", - " df = pd.read_csv(df, index_col=0)\n", - " organisms = [i.replace(\"normalized_bp_proportion_aligned_\",\"\") for i in list(df.columns) if \"normalized_bp_proportion_aligned_\" in i]\n", - " organisms_clean = [i.replace(\"_\",\" \") for i in organisms]\n", - " if len(organisms_clean) < 3:\n", - " display(Markdown(\"### Plot\"))\n", - " species_text = pd.DataFrame()\n", - " species_text['x'] = [0.25, 0.75, 0.75, 0.25]\n", - " species_text['y'] = [0.25, 0.25, 0.75, 0.75]\n", - " species_text['text'] = ['Unknown', organisms_clean[0], 'Unknown', organisms_clean[1]]\n", - " \n", - " df['samp_name'] = df.index\n", - " df['coproID_prediction'] = ['Unknown'] * df.shape[0]\n", - " df['coproID_prediction'] = np.where(df[f\"coproID_proba_{organisms[0]}\"] > 0.5, organisms_clean[0], df['coproID_prediction'])\n", - " df['coproID_prediction'] = np.where(df[f\"coproID_proba_{organisms[1]}\"] > 0.5, organisms_clean[1], df['coproID_prediction'])\n", - " \n", - " p = ggplot(df, aes(x = f\"coproID_proba_{organisms[0]}\",y = f\"coproID_proba_{organisms[1]}\"))\n", - " p = p + geom_point(aes(color='coproID_prediction'), size=2)\n", - " p = p + geom_label(aes(label=\"samp_name\", color='coproID_prediction'), size=8, nudge_x = 0.02, ha='left', va='bottom')\n", - " p = p + theme_classic() + labs(x=f\"coproID proba {organisms_clean[0]}\",y = f\"coproID proba {organisms_clean[1]}\")\n", - " p = p + geom_text(data=species_text, mapping=aes(x='x',y='y', label='text'), alpha=0.3, color='grey')\n", - " p = p + geom_hline(yintercept=0.5, linetype='dashed', alpha=0.1) \n", - " p = p + geom_vline(xintercept=0.5, linetype='dashed', alpha=0.1)\n", - " p = p + scale_color_manual(name='Predicted Organism', values = {organisms_clean[0]:'#ef7576', organisms_clean[1]:'#4daf49', 'Unknown':'#a2a3a1'})\n", - " p = p + coord_cartesian(xlim=[0,1],ylim=[0,1])\n", - " p.draw()\n", - " else:\n", - " return" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "coproid_summary_plot(d)" + "orga = coproid_summary_plot(d)" ] }, { @@ -275,7 +288,7 @@ }, "outputs": [], "source": [ - "plot_bokeh(umap)" + "plot_bokeh(umap, organisms=orga)" ] }, { @@ -374,7 +387,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.8" }, "toc": { "base_numbering": 1, From 3c66ab24aa4dc598202c3fccf149992155819b5b Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 7 Nov 2019 15:08:09 +0100 Subject: [PATCH 31/96] update conda env --- .dockerignore | 3 +++ environment.yml | 43 ++++++++++++++++++------------------------- 2 files changed, 21 insertions(+), 25 deletions(-) create mode 100644 .dockerignore diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..c94b83d --- /dev/null +++ b/.dockerignore @@ -0,0 +1,3 @@ +work +results +.nextflow.log* \ No newline at end of file diff --git a/environment.yml b/environment.yml index c5eb79e..c87a654 100644 --- a/environment.yml +++ b/environment.yml @@ -1,38 +1,31 @@ name: nf-core-coproid-1.1dev channels: - - bioconda - conda-forge - maxibor - - etetoolkit + - bioconda - defaults dependencies: - - bioconda::adapterremoval=2.2.2 + - conda-forge::openblas=0.3.7 + - bioconda::adapterremoval=2.3.1 - bioconda::bcftools=1.9 - - bioconda::bedtools=2.27.1 - - bioconda::blast=2.5.0 - - anaconda::bokeh=1.0.4 + - bioconda::bedtools=2.29.0 + - bioconda::blast=2.9.0 + - conda-forge::bokeh=1.3.4 - bioconda::bowtie2=2.3.5 - bioconda::damageprofiler=0.4.6 - - bioconda::ete3=3.1.1 - bioconda::fastqc=0.11.8 - - bioconda::htslib=1.9 - - anaconda::jupyter=1.0.0 - - bioconda::kraken2=2.0.7_beta - - conda-forge::notebook=5.7.5 - - anaconda::nbconvert=5.4.1 - - bioconda::nextflow=19.01.0 - - bioconda::pmdtools=0.60 - - bioconda::pysam=0.15.2 + - conda-forge::jupyter=1.0.0 + - bioconda::kraken2=2.0.8_beta + - conda-forge::notebook=6.0.1 + - conda-forge::nbconvert=5.6.1 + - bioconda::nextflow=19.10.0 + - bioconda::pmdtools=0.60 + - bioconda::pysam=0.15.3 - bioconda::samtools=1.9 - - conda-forge::scikit-bio=0.5.5 - - anaconda::scikit-learn=0.20.2 - - maxibor::sourcepredict=0.32 - - conda-forge::umap-learn=0.3.7 - - anaconda::numpy=1.16.3 - - anaconda::pandas=0.24.2 - - anaconda::scipy=1.2.1 - - conda-forge::matplotlib=2.2.3 + - maxibor::sourcepredict=0.4 + - conda-forge::scipy=1.3.1 + - conda-forge::matplotlib=3.1.2 - bioconda::multiqc=1.7 - - conda-forge::r-markdown=0.9 + - conda-forge::r-markdown=1.1 - conda-forge::jupyter_contrib_nbextensions=0.5.1 - - conda-forge::plotnine=0.4.0 \ No newline at end of file + - conda-forge::plotnine=0.6.0 \ No newline at end of file From 63a6bc6998c240b77791916c243d538b2268b5d5 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 11:27:27 +0100 Subject: [PATCH 32/96] fix colnames in report --- templates/coproID_report.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index cfcbd81..8f8de24 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -124,6 +124,7 @@ "source": [ "def plot_bokeh(df, organisms = []):\n", " d = pd.read_csv(df, index_col = 0).fillna('sink')\n", + " d = d.rename(columns={'PC1':'DIM1', 'PC2':'DIM2'})\n", " \n", " orga_in_endo = [i for i in list(sorted(set(d['labels']))) if i in organisms]\n", " orga_not_in_endo = [i for i in list(sorted(set(d['labels']))) if i not in organisms]\n", @@ -141,7 +142,7 @@ "# colors = [\"#e41a1c\",\"#377eb8\",\"#4daf4a\",\"#984ea3\",\"#ff7f00\",\"\"]\n", "# color_map = factor_cmap(field_name='labels', palette=colors, factors=labels)\n", " p = figure(tools=TOOLS)\n", - " p.scatter(x = 'PC1', y='PC2', color='colors', alpha = 0.6, size = 6, legend='labels', source=d)\n", + " p.scatter(x = 'DIM1', y='DIM2', color='colors', alpha = 0.6, size = 6, legend='labels', source=d)\n", " hover = HoverTool()\n", " hover.tooltips = [(\"Organism\", \"@labels\"),('Sample',\"@name\")]\n", " p.add_tools(hover)\n", @@ -162,6 +163,7 @@ "def bokeh_table(df):\n", " \n", " d = pd.read_csv(df, index_col=0)\n", + " d = d.rename(columns={'PC1':'DIM1', 'PC2':'DIM2'})\n", " d.insert(0, \"sample\", d.index)\n", " source = ColumnDataSource(d)\n", "\n", @@ -387,7 +389,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.4" }, "toc": { "base_numbering": 1, From e85988b883539aa51461e749bc14ec6563f62fc8 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 11:38:18 +0100 Subject: [PATCH 33/96] add zenodo badge --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d3ecd2f..d9088bf 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,10 @@ [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg)](https://www.nextflow.io/) [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) -[![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) + + ![logo_nf_core](assets/img/coproID_nf-core_logo.svg) From e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 11:39:30 +0100 Subject: [PATCH 34/96] add sp params & switch bedtools2fq to samtools2fq --- main.nf | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/main.nf b/main.nf index 1c7a279..9e30eca 100644 --- a/main.nf +++ b/main.nf @@ -49,6 +49,8 @@ def helpMessage() { --endo1 Proportion of Endogenous DNA in organism 1 target microbiome. Default = ${params.endo1} --endo2 Proportion of Endogenous DNA in organism 2 target microbiome. Default = ${params.endo1} --endo3 Proportion of Endogenous DNA in organism 3 target microbiome. Default = ${params.endo1} + --sp_embed SourcePredict embedding algorithm. One of mds, tsne, umap. Default = ${params.sp_embed} + --sp_neighbors Sourcepredict numbers of neighbors for KNN ML. Integer or all. Default = ${params.sp_neighbors} Options: --name3 Name of candidate 1. Example: "Sus_scrofa" @@ -259,6 +261,14 @@ if( ! nextflow.version.matches(">= 0.30") ){ exit(1) } +// Check sourcepredict parameters + +if (params.sp_embed != 'mds' && params.sp_embed != 'tsne' && params.sp_embed != 'umap'){ + println "${params.sp_embed} is not a valid method for SourcePredict embedding" + println "Available methods are: mds, tsne, umap" + exit(1) +} + /**************** AWSBATCH SETTINGS *****************/ @@ -616,12 +626,12 @@ process bam2fq { out1 = name+"_"+params.name1+".unaligned_R1.fastq" out2 = name+"_"+params.name1+".unaligned_R2.fastq" """ - bedtools bamtofastq -i $bam -fq $out1 -fq2 $out2 + samtools fastq -1 $out1 -2 $out2 $bam """ } else { out = name+"_"+params.name1+".unaligned.fastq" """ - bedtools bamtofastq -i $bam -fq $out + samtools fastq $bam > $out """ } } @@ -889,7 +899,8 @@ process sourcepredict { embed_out = "sourcepredict_embedding.csv" """ sourcepredict -di ${params.sp_dim} \\ - -k ${params.sp_kfold} \\ + -kne ${params.sp_neighbors} \\ + -me ${params.sp_embed} \\ -l ${sp_labels} \\ -s ${sp_sources} \\ -t ${task.cpus} \\ From cad82ec0c9f8ebd9584a92d008a5b45f4d9f9d68 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 11:39:50 +0100 Subject: [PATCH 35/96] add sp_params --- nextflow.config | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nextflow.config b/nextflow.config index 3ee6c3d..8f01811 100644 --- a/nextflow.config +++ b/nextflow.config @@ -36,9 +36,9 @@ params { endo3 = 0.01 sp_labels = "$baseDir/data/sourcepredict/modern_gut_microbiomes_labels.csv" sp_sources = "$baseDir/data/sourcepredict/modern_gut_microbiomes_sources.csv" - sp_kfold = 5 - sp_pdim = 20 sp_dim = 2 + sp_embed = 'mds' + sp_neighbors = 'all' // Boilerplate options name = false multiqc_config = "$baseDir/assets/multiqc_config.yaml" From bedfddec8500adac8e0cb9cc8e0df2dc6a784f15 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 11:39:55 +0100 Subject: [PATCH 36/96] update doc --- docs/usage.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/docs/usage.md b/docs/usage.md index 0d1bf75..8040cb6 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -328,6 +328,27 @@ Proportion of Endogenous DNA in organism 3 target microbiome. Must be between 0 --endo3 0.01 ``` + +### `sp_embed` + +SourcePredict embedding algorithm. One of mds, tsne, umap. Default to mds + +``` +--sp_embed mds +``` + +More information is available in the [Sourcepredict documentation](https://sourcepredict.readthedocs.io/en/latest/index.html) + +### `sp_neighbors` + +Sourcepredict numbers of neighbors for KNN ML. Integer or all. Default = all + +``` +--sp_neighbors all +``` + +More information is available in the [Sourcepredict documentation](https://sourcepredict.readthedocs.io/en/latest/index.html) + ## Other coproID parameters ### `--name3` From 9874ae87c88842d75c29088672aa81023408d4e7 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 11:56:32 +0100 Subject: [PATCH 37/96] update readme --- README.md | 55 +++++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 49 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index d9088bf..c3a92fc 100644 --- a/README.md +++ b/README.md @@ -9,9 +9,42 @@ ![logo_nf_core](assets/img/coproID_nf-core_logo.svg) +## Introduction + +**CoproID** helps you to identify the _"true maker"_ of a sequenced Coprolite by checking the microbiome composition and the endogenous DNA. + +The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. + +## Quick Start + +1. Install [`nextflow`](https://nf-co.re/usage/installation) + +2. Install one of [`docker`](https://docs.docker.com/engine/installation/), [`singularity`](https://www.sylabs.io/guides/3.0/user-guide/) or [`conda`](https://conda.io/miniconda.html) + +3. Download the EAGER pipeline + +```bash +nextflow pull nf-core/coproid +``` + +4. Test the pipeline using the provided test data + +```bash +nextflow run nf-core/coproid -profile ,test +``` + +5. Run with it with you own data + +nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker + +NB. You can see an overview of the run in the MultiQC report located at `/MultiQC/multiqc_report.html` + +Modifications to the default pipeline are easily made using various options +as described in the documentation. + ## Documentation -The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory: +The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory and at the following address: [coproid.readthedocs.io](https://coproid.readthedocs.io) 1. [Installation](https://nf-co.re/usage/installation) 2. Pipeline configuration @@ -22,12 +55,22 @@ The nf-core/coproid pipeline comes with documentation about the pipeline, found 4. [Output and how to interpret the results](docs/output.md) 5. [Troubleshooting](https://nf-co.re/usage/troubleshooting) -## Introduction +## Credits -**CoproID** helps you to identify the _"true maker"_ of a sequenced Coprolite by checking the microbiome composition and the endogenous DNA. +nf-core/coproid was written by [Maxime Borry](https://github.com/maxibor). -The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. +## Contributors -## Credits +[James A. Fellows-Yates](https://github.com/jfy133) + +## Tool references -nf-core/coproid was originally written by [Maxime Borry](https://github.com/maxibor). +- **AdapterRemoval v2** Schubert, M., Lindgreen, S., & Orlando, L. (2016). AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes, 9, 88. [https://doi.org/10.1186/s13104-016-1900-2](https://doi.org/10.1186/s13104-016-1900-2) +- **FastQC** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) +- **Bowtie2** Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357. [https://dx.doi.org/10.1038%2Fnmeth.1923](https://dx.doi.org/10.1038%2Fnmeth.1923) +- **Samtools** Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. [https://doi.org/10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) +- **Kraken2** Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. BioRxiv, 762302. [https://doi.org/10.1101/762302](https://doi.org/10.1101/762302) +- **PMDTools** Skoglund, P., Northoff, B. H., Shunkov, M. V., Derevianko, A. P., Pääbo, S., Krause, J., & Jakobsson, M. (2014). Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proceedings of the National Academy of Sciences of the United States of America, 111(6), 2229–2234. [https://doi.org/10.1073/pnas.1318934111](https://doi.org/10.1073/pnas.1318934111) +- **DamageProfiler** Judith Neukamm (Unpublished) +- **Sourcepredict** Borry, M. (2019). Sourcepredict: Prediction of metagenomic sample sources using dimension reduction followed by machine learning classification. The Journal of Open Source Software. [https://doi.org/10.21105/joss.01540](https://doi.org/10.21105/joss.01540) +- **MultiQC** Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. [https://doi.org/10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) \ No newline at end of file From 4c1075a534b1033f45caa030fdd861f6d3107ac7 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 12:05:12 +0100 Subject: [PATCH 38/96] update changelog --- CHANGELOG.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 31ad55c..107f9b7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,12 @@ - Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) - Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) +- Update Sourcepredict to version 0.4 and reflect new parameters in coproID [#19](https://github.com/nf-core/coproid/pull/19) [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) +- Changed bedtools bamtofastq to samtools fastq [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) +- Fixed column names in report (PC* to DIM* ) [e4afca7](https://github.com/nf-core/coproid/commit/63a6bc6998c240b77791916c243d538b2268b5d5) +- Update README to inlude Zenodo badge, Quick start, contributor section, and tools references. [9874ae8](https://github.com/nf-core/coproid/commit/9874ae87c88842d75c29088672aa81023408d4e7) [e85988b](https://github.com/nf-core/coproid/commit/e85988b883539aa51461e749bc14ec6563f62fc8) +- Update documentation [bedfdde](https://github.com/nf-core/coproid/commit/bedfddec8500adac8e0cb9cc8e0df2dc6a784f15) + ## v1.0 - 2019-04-26 Initial release of nf-core/coproid, created with the [nf-core](http://nf-co.re/) template. From 44999fd4d38b21d53f970621dbf3587c044da8d1 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 12:18:01 +0100 Subject: [PATCH 39/96] update nextflow min version --- main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.nf b/main.nf index 9e30eca..21f601b 100644 --- a/main.nf +++ b/main.nf @@ -256,7 +256,7 @@ if (params.library == 'classic'){ library = '--UDGhalf' } -if( ! nextflow.version.matches(">= 0.30") ){ +if( ! nextflow.version.matches(">= 19.04.0") ){ println "Your version of Nextflow is too old, please update to Nextflow >= 0.30" exit(1) } From 1e3e454e72f1bc8eb43aaa1e5165981cb77a56dc Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 12:18:11 +0100 Subject: [PATCH 40/96] update travis for nextflow min version --- .travis.yml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 6353fec..0d90262 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,15 +26,19 @@ install: # Install nf-core/tools - pip install --upgrade pip - pip install nf-core + # Install Conda + - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh + - bash Miniconda3-latest-Linux-x86_64.sh -b -f -p $HOME/miniconda + - export PATH="$HOME/miniconda/bin:$PATH" # Reset - mkdir ${TRAVIS_BUILD_DIR}/tests && cd ${TRAVIS_BUILD_DIR}/tests # Install markdownlint-cli - sudo apt-get install npm && npm install -g markdownlint-cli env: - - NXF_VER='0.32.0' # Specify a minimum NF version that should be tested and work + - NXF_VER='19.04.0' # Specify a minimum NF version that should be tested and work - NXF_VER='' # Plus: get the latest NF version and check that it works - + script: # Lint the pipeline code - nf-core lint ${TRAVIS_BUILD_DIR} From 99284ab0a77532c66cf0b5c32cca9d83c631bf76 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 12:21:24 +0100 Subject: [PATCH 41/96] update changelog --- CHANGELOG.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 107f9b7..4d38c49 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,12 +5,14 @@ - Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14) - Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) - Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) - - Update Sourcepredict to version 0.4 and reflect new parameters in coproID [#19](https://github.com/nf-core/coproid/pull/19) [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) - Changed bedtools bamtofastq to samtools fastq [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) - Fixed column names in report (PC* to DIM* ) [e4afca7](https://github.com/nf-core/coproid/commit/63a6bc6998c240b77791916c243d538b2268b5d5) - Update README to inlude Zenodo badge, Quick start, contributor section, and tools references. [9874ae8](https://github.com/nf-core/coproid/commit/9874ae87c88842d75c29088672aa81023408d4e7) [e85988b](https://github.com/nf-core/coproid/commit/e85988b883539aa51461e749bc14ec6563f62fc8) - Update documentation [bedfdde](https://github.com/nf-core/coproid/commit/bedfddec8500adac8e0cb9cc8e0df2dc6a784f15) +- Update Nextflow minimum version to 19.04.0 [44999fd](https://github.com/nf-core/coproid/commit/44999fd4d38b21d53f970621dbf3587c044da8d1) +- Update travis for more recent nextflow requirements [1e3e454](https://github.com/nf-core/coproid/commit/1e3e454e72f1bc8eb43aaa1e5165981cb77a56dc) + ## v1.0 - 2019-04-26 From 62cb55d34c7aafcbe7a475619c7928287945d2ab Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 12:23:17 +0100 Subject: [PATCH 42/96] update nf version requirement --- nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index 8f01811..b5ebc18 100644 --- a/nextflow.config +++ b/nextflow.config @@ -118,7 +118,7 @@ manifest { homePage = 'https://github.com/nf-core/coproid' description = 'Coprolite Identification' mainScript = 'main.nf' - nextflowVersion = '>=0.32.0' + nextflowVersion = '>=19.04.0' version = '1.1dev' } // Function to ensure that resource requirements don't go beyond From b95bc5eae307ca16fb6b705b3dc15709ff809096 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 12:58:18 +0100 Subject: [PATCH 43/96] version check using manifest --- main.nf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/main.nf b/main.nf index 21f601b..d4205b0 100644 --- a/main.nf +++ b/main.nf @@ -256,8 +256,8 @@ if (params.library == 'classic'){ library = '--UDGhalf' } -if( ! nextflow.version.matches(">= 19.04.0") ){ - println "Your version of Nextflow is too old, please update to Nextflow >= 0.30" +if( ! nextflow.version.matches(workflow.manifest.nextflowVersion) ){ + println "Your version of Nextflow is too old, please use Nextflow "+workflow.manifest.nextflowVersion.toString() exit(1) } From 8d97a46965b200497bd7243da9d4285e6f852de4 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 13:03:25 +0100 Subject: [PATCH 44/96] sp method hinting in quotes --- main.nf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/main.nf b/main.nf index d4205b0..6753b11 100644 --- a/main.nf +++ b/main.nf @@ -264,8 +264,8 @@ if( ! nextflow.version.matches(workflow.manifest.nextflowVersion) ){ // Check sourcepredict parameters if (params.sp_embed != 'mds' && params.sp_embed != 'tsne' && params.sp_embed != 'umap'){ - println "${params.sp_embed} is not a valid method for SourcePredict embedding" - println "Available methods are: mds, tsne, umap" + println "${params.sp_embed} is not a valid method for SourcePredict embedding (--sp_embed)" + println """Available methods are: 'mds', 'tsne', 'umap' """ exit(1) } From b57b29059ea77ea36b83102533a51187b2bc22ae Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 13:04:32 +0100 Subject: [PATCH 45/96] readme update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c3a92fc..e2b9471 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ ## Introduction -**CoproID** helps you to identify the _"true maker"_ of a sequenced Coprolite by checking the microbiome composition and the endogenous DNA. +**CoproID** helps you to identify the _"true maker"_ of Illumina sequenced Coprolites/Paleofaeces by checking the microbiome composition and the endogenous DNA. The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. From c2d4164bf068ed4fc92d529470b0a3af3a69113a Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 14:27:52 +0100 Subject: [PATCH 46/96] add more sp parameters --- conf/test.config | 3 +++ docs/usage.md | 12 +++++++++++- main.nf | 18 +++++++++++++----- nextflow.config | 1 + 4 files changed, 28 insertions(+), 6 deletions(-) diff --git a/conf/test.config b/conf/test.config index 767bd5d..22bacbb 100644 --- a/conf/test.config +++ b/conf/test.config @@ -43,6 +43,9 @@ params { genome2 = "" sp_sources = "https://raw.githubusercontent.com/nf-core/test-datasets/coproid/sourcepredict/test_sources.csv" sp_labels = "https://raw.githubusercontent.com/nf-core/test-datasets/coproid/sourcepredict/test_labels.csv" + sp_norm = 'subsample' + sp_embed = 'tsne' + sp_neighbors = '0' } process { diff --git a/docs/usage.md b/docs/usage.md index 8040cb6..5d78547 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -339,6 +339,16 @@ SourcePredict embedding algorithm. One of mds, tsne, umap. Default to mds More information is available in the [Sourcepredict documentation](https://sourcepredict.readthedocs.io/en/latest/index.html) +### `sp_norm` + +Sourcepredict normalization method. One of 'rle', 'gmpr', 'subsample'. Default = 'gmpr' + +``` +--sp_norm 'gmpr' +``` + +More informations are available in the [Sourcepredict documentation](https://sourcepredict.readthedocs.io/en/latest/index.html) + ### `sp_neighbors` Sourcepredict numbers of neighbors for KNN ML. Integer or all. Default = all @@ -347,7 +357,7 @@ Sourcepredict numbers of neighbors for KNN ML. Integer or all. Default = all --sp_neighbors all ``` -More information is available in the [Sourcepredict documentation](https://sourcepredict.readthedocs.io/en/latest/index.html) +More informations are available in the [Sourcepredict documentation](https://sourcepredict.readthedocs.io/en/latest/index.html) ## Other coproID parameters diff --git a/main.nf b/main.nf index 6753b11..124e66c 100644 --- a/main.nf +++ b/main.nf @@ -43,14 +43,16 @@ def helpMessage() { --collapse Specifies if AdapterRemoval should merge the paired-end sequences or not (true | false). Default = ${params.collapse} --identity Identity threshold to retain read alignment. Default = ${params.identity} --pmdscore Minimum PMDscore to retain read alignment. Default = ${params.pmdscore} - --library DNA preparation library type ( classic | UDGhalf). Default = ${params.library} - --bowtie Bowtie settings for sensivity (very-fast | very-sensitive). Default = ${params.bowtie} + --library DNA preparation library type ( 'classic' | 'UDGhalf'). Default = ${params.library} + --bowtie Bowtie settings for sensivity ('very-fast' | 'very-sensitive'). Default = ${params.bowtie} --minKraken Minimum number of Kraken hits per Taxonomy ID to report. Default = ${params.minKraken} --endo1 Proportion of Endogenous DNA in organism 1 target microbiome. Default = ${params.endo1} --endo2 Proportion of Endogenous DNA in organism 2 target microbiome. Default = ${params.endo1} --endo3 Proportion of Endogenous DNA in organism 3 target microbiome. Default = ${params.endo1} - --sp_embed SourcePredict embedding algorithm. One of mds, tsne, umap. Default = ${params.sp_embed} - --sp_neighbors Sourcepredict numbers of neighbors for KNN ML. Integer or all. Default = ${params.sp_neighbors} + --sp_norm Sourcepredict normalization method. One of 'rle', 'gmpr', 'subsample'. Default = ${params.sp_norm} + --sp_embed SourcePredict embedding algorithm. One of 'mds', 'tsne', 'umap'. Default = ${params.sp_embed} + --sp_neighbors Sourcepredict numbers of neighbors for KNN ML. Integer or 'all'. Default = ${params.sp_neighbors} + Options: --name3 Name of candidate 1. Example: "Sus_scrofa" @@ -268,6 +270,11 @@ if (params.sp_embed != 'mds' && params.sp_embed != 'tsne' && params.sp_embed != println """Available methods are: 'mds', 'tsne', 'umap' """ exit(1) } +if (params.sp_norm != 'rle' && params.sp_norm != 'gmpr' && params.sp_norm != 'subsample'){ + println "${params.sp_norm} is not a valid method for SourcePredict normalization method (--sp_norm)" + println """Available methods are: 'rle', 'gmpr', 'subsample' """ + exit(1) +} /**************** AWSBATCH SETTINGS @@ -626,7 +633,7 @@ process bam2fq { out1 = name+"_"+params.name1+".unaligned_R1.fastq" out2 = name+"_"+params.name1+".unaligned_R2.fastq" """ - samtools fastq -1 $out1 -2 $out2 $bam + samtools fastq -1 $out1 -2 $out2 -0 /dev/null -s /dev/null -n -F 0x900 $bam """ } else { out = name+"_"+params.name1+".unaligned.fastq" @@ -901,6 +908,7 @@ process sourcepredict { sourcepredict -di ${params.sp_dim} \\ -kne ${params.sp_neighbors} \\ -me ${params.sp_embed} \\ + -n ${params.sp_norm} \\ -l ${sp_labels} \\ -s ${sp_sources} \\ -t ${task.cpus} \\ diff --git a/nextflow.config b/nextflow.config index b5ebc18..be0e6a0 100644 --- a/nextflow.config +++ b/nextflow.config @@ -36,6 +36,7 @@ params { endo3 = 0.01 sp_labels = "$baseDir/data/sourcepredict/modern_gut_microbiomes_labels.csv" sp_sources = "$baseDir/data/sourcepredict/modern_gut_microbiomes_sources.csv" + sp_norm = 'gmpr' sp_dim = 2 sp_embed = 'mds' sp_neighbors = 'all' From 6c644d2ef8d21be5d976c33c911358d5a822807a Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 8 Nov 2019 14:41:25 +0100 Subject: [PATCH 47/96] changelog update --- CHANGELOG.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4d38c49..a4452a3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,7 +5,8 @@ - Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14) - Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) - Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) -- Update Sourcepredict to version 0.4 and reflect new parameters in coproID [#19](https://github.com/nf-core/coproid/pull/19) [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) +- Update Sourcepredict to version 0.4 and reflect new parameters in coproID [#19](https://github.com/nf-core/coproid/pull/19) [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) [ +c2d4164 ](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d529470b0a3af3a69113a) - Changed bedtools bamtofastq to samtools fastq [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) - Fixed column names in report (PC* to DIM* ) [e4afca7](https://github.com/nf-core/coproid/commit/63a6bc6998c240b77791916c243d538b2268b5d5) - Update README to inlude Zenodo badge, Quick start, contributor section, and tools references. [9874ae8](https://github.com/nf-core/coproid/commit/9874ae87c88842d75c29088672aa81023408d4e7) [e85988b](https://github.com/nf-core/coproid/commit/e85988b883539aa51461e749bc14ec6563f62fc8) From 8dbc69950229dbe1c8d8bade02df9a29a0ad95bd Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 31 Jan 2020 12:33:36 +0100 Subject: [PATCH 48/96] Template update for nf-core/tools version 1.8 --- .github/CONTRIBUTING.md | 52 ++- .github/ISSUE_TEMPLATE/bug_report.md | 43 ++- .github/ISSUE_TEMPLATE/feature_request.md | 18 +- .github/PULL_REQUEST_TEMPLATE.md | 28 +- .github/markdownlint.yml | 4 - .github/workflows/branch.yml | 16 + .github/workflows/ci.yml | 29 ++ .github/workflows/linting.yml | 41 ++ .gitignore | 2 +- .travis.yml | 13 +- CHANGELOG.md | 14 +- CODE_OF_CONDUCT.md | 2 +- Dockerfile | 12 +- README.md | 47 ++- assets/email_template.html | 2 + assets/email_template.txt | 12 +- assets/nf-core-coproid_logo.png | Bin 0 -> 12502 bytes assets/sendmail_template.txt | 17 + bin/scrape_software_versions.py | 15 +- conf/awsbatch.config | 18 - conf/base.config | 37 +- conf/igenomes.config | 451 +++++++++++++++++----- conf/test.config | 5 +- docs/images/nf-core-coproid_logo.png | Bin 0 -> 21191 bytes docs/output.md | 4 +- docs/usage.md | 86 ++++- environment.yml | 9 +- main.nf | 252 ++++++------ nextflow.config | 54 ++- 29 files changed, 916 insertions(+), 367 deletions(-) create mode 100644 .github/workflows/branch.yml create mode 100644 .github/workflows/ci.yml create mode 100644 .github/workflows/linting.yml create mode 100644 assets/nf-core-coproid_logo.png delete mode 100644 conf/awsbatch.config create mode 100644 docs/images/nf-core-coproid_logo.png diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index a8deaee..47e22bb 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -1,47 +1,57 @@ # nf-core/coproid: Contributing Guidelines -Hi there! Many thanks for taking an interest in improving nf-core/coproid. +Hi there! +Many thanks for taking an interest in improving nf-core/coproid. -We try to manage the required tasks for nf-core/coproid using GitHub issues, you probably came to this page when creating one. Please use the pre-filled template to save time. - -However, don't be put off by this template - other more general issues and suggestions are welcome! Contributions to the code are even more welcome ;) - -> If you need help using or modifying nf-core/coproid then the best place to ask is on the pipeline channel on [Slack](https://nf-core-invite.herokuapp.com/). +We try to manage the required tasks for nf-core/coproid using GitHub issues, you probably came to this page when creating one. +Please use the pre-filled template to save time. +However, don't be put off by this template - other more general issues and suggestions are welcome! +Contributions to the code are even more welcome ;) +> If you need help using or modifying nf-core/coproid then the best place to ask is on the nf-core Slack [#coproid](https://nfcore.slack.com/channels/coproid) channel ([join our Slack here](https://nf-co.re/join/slack)). ## Contribution workflow -If you'd like to write some code for nf-core/coproid, the standard workflow -is as follows: -1. Check that there isn't already an issue about your idea in the - [nf-core/coproid issues](https://github.com/nf-core/coproid/issues) to avoid - duplicating work. +If you'd like to write some code for nf-core/coproid, the standard workflow is as follows: + +1. Check that there isn't already an issue about your idea in the [nf-core/coproid issues](https://github.com/nf-core/coproid/issues) to avoid duplicating work * If there isn't one already, please create one so that others know you're working on this -2. Fork the [nf-core/coproid repository](https://github.com/nf-core/coproid) to your GitHub account +2. [Fork](https://help.github.com/en/github/getting-started-with-github/fork-a-repo) the [nf-core/coproid repository](https://github.com/nf-core/coproid) to your GitHub account 3. Make the necessary changes / additions within your forked repository -4. Submit a Pull Request against the `dev` branch and wait for the code to be reviewed and merged. - -If you're not used to this workflow with git, you can start with some [basic docs from GitHub](https://help.github.com/articles/fork-a-repo/) or even their [excellent interactive tutorial](https://try.github.io/). +4. Submit a Pull Request against the `dev` branch and wait for the code to be reviewed and merged +If you're not used to this workflow with git, you can start with some [docs from GitHub](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests) or even their [excellent `git` resources](https://try.github.io/). ## Tests -When you create a pull request with changes, [Travis CI](https://travis-ci.org/) will run automatic tests. + +When you create a pull request with changes, [GitHub Actions](https://github.com/features/actions) will run automatic tests. Typically, pull-requests are only fully reviewed when these tests are passing, though of course we can help out before then. There are typically two types of tests that run: ### Lint Tests -The nf-core has a [set of guidelines](http://nf-co.re/guidelines) which all pipelines must adhere to. + +`nf-core` has a [set of guidelines](https://nf-co.re/developers/guidelines) which all pipelines must adhere to. To enforce these and ensure that all pipelines stay in sync, we have developed a helper tool which runs checks on the pipeline code. This is in the [nf-core/tools repository](https://github.com/nf-core/tools) and once installed can be run locally with the `nf-core lint ` command. If any failures or warnings are encountered, please follow the listed URL for more documentation. ### Pipeline Tests -Each nf-core pipeline should be set up with a minimal set of test-data. -Travis CI then runs the pipeline on this data to ensure that it exists successfully. + +Each `nf-core` pipeline should be set up with a minimal set of test-data. +`GitHub Actions` then runs the pipeline on this data to ensure that it exits successfully. If there are any failures then the automated tests fail. -These tests are run both with the latest available version of Nextflow and also the minimum required version that is stated in the pipeline code. +These tests are run both with the latest available version of `Nextflow` and also the minimum required version that is stated in the pipeline code. + +## Patch + +: warning: Only in the unlikely and regretful event of a release happening with a bug. + +* On your own fork, make a new branch `patch` based on `upstream/master`. +* Fix the bug, and bump version (X.Y.Z+1). +* A PR should be made on `master` from patch to directly this particular bug. ## Getting help -For further information/help, please consult the [nf-core/coproid documentation](https://github.com/nf-core/coproid#documentation) and don't hesitate to get in touch on the pipeline channel on [Slack](https://nf-core-invite.herokuapp.com/). + +For further information/help, please consult the [nf-core/coproid documentation](https://nf-co.re/nf-core/coproid/docs) and don't hesitate to get in touch on the nf-core Slack [#coproid](https://nfcore.slack.com/channels/coproid) channel ([join our Slack here](https://nf-co.re/join/slack)). diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 62c91c5..bb180cb 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,31 +1,42 @@ +# nf-core/coproid bug report + Hi there! -Thanks for telling us about a problem with the pipeline. Please delete this text and anything that's not relevant from the template below: +Thanks for telling us about a problem with the pipeline. +Please delete this text and anything that's not relevant from the template below: + +## Describe the bug -#### Describe the bug A clear and concise description of what the bug is. -#### Steps to reproduce +## Steps to reproduce + Steps to reproduce the behaviour: + 1. Command line: `nextflow run ...` 2. See error: _Please provide your error message_ -#### Expected behaviour +## Expected behaviour + A clear and concise description of what you expected to happen. -#### System: - - Hardware: [e.g. HPC, Desktop, Cloud...] - - Executor: [e.g. slurm, local, awsbatch...] - - OS: [e.g. CentOS Linux, macOS, Linux Mint...] - - Version [e.g. 7, 10.13.6, 18.3...] +## System + +- Hardware: +- Executor: +- OS: +- Version + +## Nextflow Installation + +- Version: + +## Container engine -#### Nextflow Installation: - - Version: [e.g. 0.31.0] +- Engine: +- version: +- Image tag: -#### Container engine: - - Engine: [e.g. Conda, Docker or Singularity] - - version: [e.g. 1.0.0] - - Image tag: [e.g. nfcore/coproid:1.0.0] +## Additional context -#### Additional context Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index 1f025b7..7562b72 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -1,16 +1,24 @@ +# nf-core/coproid feature request + Hi there! -Thanks for suggesting a new feature for the pipeline! Please delete this text and anything that's not relevant from the template below: +Thanks for suggesting a new feature for the pipeline! +Please delete this text and anything that's not relevant from the template below: + +## Is your feature request related to a problem? Please describe -#### Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. + Ex. I'm always frustrated when [...] -#### Describe the solution you'd like +## Describe the solution you'd like + A clear and concise description of what you want to happen. -#### Describe alternatives you've considered +## Describe alternatives you've considered + A clear and concise description of any alternative solutions or features you've considered. -#### Additional context +## Additional context + Add any other context about the feature request here. diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 8af6145..b2a4354 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,15 +1,19 @@ -Many thanks to contributing to nf-core/coproid! +# nf-core/coproid pull request -Please fill in the appropriate checklist below (delete whatever is not relevant). These are the most common things requested on pull requests (PRs). +Many thanks for contributing to nf-core/coproid! + +Please fill in the appropriate checklist below (delete whatever is not relevant). +These are the most common things requested on pull requests (PRs). ## PR checklist - - [ ] This comment contains a description of changes (with reason) - - [ ] If you've fixed a bug or added code that should be tested, add tests! - - [ ] If necessary, also make a PR on the [nf-core/coproid branch on the nf-core/test-datasets repo]( https://github.com/nf-core/test-datasets/pull/new/nf-core/coproid) - - [ ] Ensure the test suite passes (`nextflow run . -profile test,docker`). - - [ ] Make sure your code lints (`nf-core lint .`). - - [ ] Documentation in `docs` is updated - - [ ] `CHANGELOG.md` is updated - - [ ] `README.md` is updated - -**Learn more about contributing:** https://github.com/nf-core/coproid/tree/master/.github/CONTRIBUTING.md + +- [ ] This comment contains a description of changes (with reason) +- [ ] If you've fixed a bug or added code that should be tested, add tests! +- [ ] If necessary, also make a PR on the [nf-core/coproid branch on the nf-core/test-datasets repo](https://github.com/nf-core/test-datasets/pull/new/nf-core/coproid) +- [ ] Ensure the test suite passes (`nextflow run . -profile test,docker`). +- [ ] Make sure your code lints (`nf-core lint .`). +- [ ] Documentation in `docs` is updated +- [ ] `CHANGELOG.md` is updated +- [ ] `README.md` is updated + +**Learn more about contributing:** [CONTRIBUTING.md](https://github.com/nf-core/coproid/tree/master/.github/CONTRIBUTING.md) \ No newline at end of file diff --git a/.github/markdownlint.yml b/.github/markdownlint.yml index e052a63..96b12a7 100644 --- a/.github/markdownlint.yml +++ b/.github/markdownlint.yml @@ -1,9 +1,5 @@ # Markdownlint configuration file default: true, line-length: false -no-multiple-blanks: 0 -blanks-around-headers: false -blanks-around-lists: false -header-increment: false no-duplicate-header: siblings_only: true diff --git a/.github/workflows/branch.yml b/.github/workflows/branch.yml new file mode 100644 index 0000000..12d26e7 --- /dev/null +++ b/.github/workflows/branch.yml @@ -0,0 +1,16 @@ +name: nf-core branch protection +# This workflow is triggered on PRs to master branch on the repository +# It fails when someone tries to make a PR against the nf-core `master` branch instead of `dev` +on: + pull_request: + branches: + - master + +jobs: + test: + runs-on: ubuntu-18.04 + steps: + # PRs are only ok if coming from an nf-core `dev` branch or a fork `patch` branch + - name: Check PRs + run: | + { [[ $(git remote get-url origin) == *nf-core/coproid ]] && [[ ${GITHUB_HEAD_REF} = "dev" ]]; } || [[ ${GITHUB_HEAD_REF} == "patch" ]] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..9215992 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,29 @@ +name: nf-core CI +# This workflow is triggered on pushes and PRs to the repository. +# It runs the pipeline with the minimal test dataset to check that it completes without any syntax errors +on: [push, pull_request] + +jobs: + test: + env: + NXF_VER: ${{ matrix.nxf_ver }} + NXF_ANSI_LOG: false + runs-on: ubuntu-latest + strategy: + matrix: + # Nextflow versions: check pipeline minimum and current latest + nxf_ver: ['19.10.0', ''] + steps: + - uses: actions/checkout@v2 + - name: Install Nextflow + run: | + wget -qO- get.nextflow.io | bash + sudo mv nextflow /usr/local/bin/ + - name: Pull docker image + run: | + docker pull nfcore/coproid:dev && docker tag nfcore/coproid:dev nfcore/coproid:dev + - name: Run pipeline with test data + run: | + # TODO nf-core: You can customise CI pipeline run tests as required + # (eg. adding multiple test runs with different parameters) + nextflow run ${GITHUB_WORKSPACE} -profile test,docker diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml new file mode 100644 index 0000000..7354dc7 --- /dev/null +++ b/.github/workflows/linting.yml @@ -0,0 +1,41 @@ +name: nf-core linting +# This workflow is triggered on pushes and PRs to the repository. +# It runs the `nf-core lint` and markdown lint tests to ensure that the code meets the nf-core guidelines +on: [push, pull_request] + +jobs: + Markdown: + runs-on: ubuntu-18.04 + steps: + - uses: actions/checkout@v1 + - uses: actions/setup-node@v1 + with: + node-version: '10' + - name: Install markdownlint + run: | + npm install -g markdownlint-cli + - name: Run Markdownlint + run: | + markdownlint ${GITHUB_WORKSPACE} -c ${GITHUB_WORKSPACE}/.github/markdownlint.yml + nf-core: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + - name: Install Nextflow + run: | + wget -qO- get.nextflow.io | bash + sudo mv nextflow /usr/local/bin/ + - uses: actions/setup-python@v1 + with: + python-version: '3.6' + architecture: 'x64' + - name: Install pip + run: | + sudo apt install python3-pip + pip install --upgrade pip + - name: Install nf-core tools + run: | + pip install nf-core + - name: Run nf-core lint + run: | + nf-core lint ${GITHUB_WORKSPACE} diff --git a/.gitignore b/.gitignore index 5b54e3e..0189a44 100644 --- a/.gitignore +++ b/.gitignore @@ -3,5 +3,5 @@ work/ data/ results/ .DS_Store -tests/test_data +test* *.pyc diff --git a/.travis.yml b/.travis.yml index 74e247f..3595a1a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,7 +9,7 @@ matrix: before_install: # PRs to master are only ok if coming from dev branch - - '[ $TRAVIS_PULL_REQUEST = "false" ] || [ $TRAVIS_BRANCH != "master" ] || ([ $TRAVIS_PULL_REQUEST_SLUG = $TRAVIS_REPO_SLUG ] && [ $TRAVIS_PULL_REQUEST_BRANCH = "dev" ])' + - '[ $TRAVIS_PULL_REQUEST = "false" ] || [ $TRAVIS_BRANCH != "master" ] || ([ $TRAVIS_PULL_REQUEST_SLUG = $TRAVIS_REPO_SLUG ] && [ $TRAVIS_PULL_REQUEST_BRANCH = "dev" ]) || [ $TRAVIS_PULL_REQUEST_BRANCH = "patch" ]' # Pull the docker image first so the test doesn't wait for this - docker pull nfcore/coproid:dev # Fake the tag locally so that the pipeline runs properly @@ -30,8 +30,13 @@ install: - sudo apt-get install npm && npm install -g markdownlint-cli env: - - NXF_VER='0.32.0' # Specify a minimum NF version that should be tested and work - - NXF_VER='' # Plus: get the latest NF version and check that it works + # Tower token is to inspect runs on https://tower.nf + # Use public mailbox nf-core@mailinator.com to log in: https://www.mailinator.com/v3/index.jsp?zone=public&query=nf-core + # Specify a minimum NF version that should be tested and work + - NXF_VER='19.10.0' TOWER_ACCESS_TOKEN="1c1f493bc2703472d6f1b9f6fb9e9d117abab7b1" + # Plus: get the latest NF version and check that it works + - NXF_VER='' TOWER_ACCESS_TOKEN="1c1f493bc2703472d6f1b9f6fb9e9d117abab7b1" + script: # Lint the pipeline code @@ -39,4 +44,4 @@ script: # Lint the documentation - markdownlint ${TRAVIS_BUILD_DIR} -c ${TRAVIS_BUILD_DIR}/.github/markdownlint.yml # Run the pipeline with the test profile - - nextflow run ${TRAVIS_BUILD_DIR} -profile test,docker + - nextflow run ${TRAVIS_BUILD_DIR} -profile test,docker -ansi-log false -name coproid-${TRAVIS_EVENT_TYPE}-${TRAVIS_PULL_REQUEST}-${TRAVIS_COMMIT:0:6}-test-description diff --git a/CHANGELOG.md b/CHANGELOG.md index b4dde96..1ccfc79 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,4 +1,16 @@ # nf-core/coproid: Changelog -## v1.0dev - [date] +The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) +and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). + +## v1.1dev - [date] + Initial release of nf-core/coproid, created with the [nf-core](http://nf-co.re/) template. + +### `Added` + +### `Fixed` + +### `Dependencies` + +### `Deprecated` diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 09226d0..cf930c8 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -34,7 +34,7 @@ This Code of Conduct applies both within project spaces and in public spaces whe ## Enforcement -Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team on [Slack](https://nf-core-invite.herokuapp.com/). The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. +Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team on [Slack](https://nf-co.re/join/slack). The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. diff --git a/Dockerfile b/Dockerfile index 8d5161a..4460f41 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,7 +1,13 @@ -FROM nfcore/base +FROM nfcore/base:1.8 LABEL authors="Maxime Borry" \ - description="Docker image containing all requirements for nf-core/coproid pipeline" + description="Docker image containing all software requirements for the nf-core/coproid pipeline" +# Install the conda environment COPY environment.yml / RUN conda env create -f /environment.yml && conda clean -a -ENV PATH /opt/conda/envs/nf-core-coproid-1.0dev/bin:$PATH + +# Add conda installation dir to PATH (instead of doing 'conda activate') +ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH + +# Dump the details of the installed packages to a file for posterity +RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml diff --git a/README.md b/README.md index ae1e255..02f68ba 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,45 @@ -# nf-core/coproid +# ![nf-core/coproid](docs/images/nf-core-coproid_logo.png) **Coprolite Identification**. [![Build Status](https://travis-ci.com/nf-core/coproid.svg?branch=master)](https://travis-ci.com/nf-core/coproid) -[![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg)](https://www.nextflow.io/) +[![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) +[![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) +[![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) ## Introduction + The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. +## Quick Start + +i. Install [`nextflow`](https://nf-co.re/usage/installation) + +ii. Install one of [`docker`](https://docs.docker.com/engine/installation/), [`singularity`](https://www.sylabs.io/guides/3.0/user-guide/) or [`conda`](https://conda.io/miniconda.html) + +iii. Download the pipeline and test it on a minimal dataset with a single command + +```bash +nextflow run nf-core/coproid -profile test, +``` + +> Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use `-profile institute` in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment. + +iv. Start running your own analysis! + + + +```bash +nextflow run nf-core/coproid -profile --reads '*_R{1,2}.fastq.gz' --genome GRCh37 +``` + +See [usage docs](docs/usage.md) for all of the available options when running the pipeline. ## Documentation + The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory: 1. [Installation](https://nf-co.re/usage/installation) @@ -27,4 +54,20 @@ The nf-core/coproid pipeline comes with documentation about the pipeline, found ## Credits + nf-core/coproid was originally written by Maxime Borry. + +## Contributions and Support + +If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md). + +For further information or help, don't hesitate to get in touch on [Slack](https://nfcore.slack.com/channels/coproid) (you can join with [this invite](https://nf-co.re/join/slack)). + +## Citation + + + + +You can cite the `nf-core` pre-print as follows: + +> Ewels PA, Peltzer A, Fillinger S, Alneberg JA, Patel H, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. **nf-core: Community curated bioinformatics pipelines**. *bioRxiv*. 2019. p. 610741. [doi: 10.1101/610741](https://www.biorxiv.org/content/10.1101/610741v1). diff --git a/assets/email_template.html b/assets/email_template.html index 58bcddd..ef5ad9a 100644 --- a/assets/email_template.html +++ b/assets/email_template.html @@ -11,6 +11,8 @@
+ +

nf-core/coproid v${version}

Run Name: $runName

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z0X5)d2E1mU7;)0nUmW`awxWbQ6&3Inl)jRn?JY)4#jQCb!Zi{uKN@+M3A|6pIqxd`yh#H14aJm-zyM4`x}mMlTbvcz1;E<%imP+vhwB^KOQtcLK@XzWk#lAb3@feg@*Edv zd%wGI@BL)sZ#f&OLby530gZp3QQWcdR%eXMiz;61z8>F93K_w3PQ2U8x*$9 zNF}-DB~V4<#?8*_GHbgRk&c=;OJ&dIX~Si35Ct>SpIY6iY)C*vdVr}(D%^%vJ>{aB zBw8amU{*i()gOV#cUm%87XZ&SLZAoCqM4{it1zULt&>!EW%NEmM4VA3vzsRygM=98 z>Dr{7cZ!6$RrWSNX7POiiT{nxpYpc~A4+*NE*ASWi?P&%jHw*?2Sxk6?EWL(|Dafw z;{WQu-*L@53MG(>kNOV`|NjZ=DC4`Uf1%wMD0EHyhqB&5)c<*FG`@U;rJtz4wWZ9f SdH+!XMoCUxwqDvI;{O4O2oBBw literal 0 HcmV?d00001 diff --git a/assets/sendmail_template.txt b/assets/sendmail_template.txt index 2d67122..08d1f2b 100644 --- a/assets/sendmail_template.txt +++ b/assets/sendmail_template.txt @@ -8,6 +8,23 @@ Content-Type: text/html; charset=utf-8 $email_html +--nfcoremimeboundary +Content-Type: image/png;name="nf-core-coproid_logo.png" +Content-Transfer-Encoding: base64 +Content-ID: +Content-Disposition: inline; filename="nf-core-coproid_logo.png" + +<% out << new File("$baseDir/assets/nf-core-coproid_logo.png"). + bytes. + encodeBase64(). + toString(). + tokenize( '\n' )*. + toList()*. + collate( 76 )*. + collect { it.join() }. + flatten(). + join( '\n' ) %> + <% if (mqcFile){ def mqcFileObj = new File("$mqcFile") diff --git a/bin/scrape_software_versions.py b/bin/scrape_software_versions.py index c4d6726..198c91f 100755 --- a/bin/scrape_software_versions.py +++ b/bin/scrape_software_versions.py @@ -18,14 +18,17 @@ # Search each file using its regex for k, v in regexes.items(): - with open(v[0]) as x: - versions = x.read() - match = re.search(v[1], versions) - if match: - results[k] = "v{}".format(match.group(1)) + try: + with open(v[0]) as x: + versions = x.read() + match = re.search(v[1], versions) + if match: + results[k] = "v{}".format(match.group(1)) + except IOError: + results[k] = False # Remove software set to false in results -for k in results: +for k in list(results): if not results[k]: del(results[k]) diff --git a/conf/awsbatch.config b/conf/awsbatch.config deleted file mode 100644 index 14af586..0000000 --- a/conf/awsbatch.config +++ /dev/null @@ -1,18 +0,0 @@ -/* - * ------------------------------------------------- - * Nextflow config file for running on AWS batch - * ------------------------------------------------- - * Base config needed for running with -profile awsbatch - */ -params { - config_profile_name = 'AWSBATCH' - config_profile_description = 'AWSBATCH Cloud Profile' - config_profile_contact = 'Alexander Peltzer (@apeltzer)' - config_profile_url = 'https://aws.amazon.com/de/batch/' -} - -aws.region = params.awsregion -process.executor = 'awsbatch' -process.queue = params.awsqueue -executor.awscli = '/home/ec2-user/miniconda/bin/aws' -params.tracedir = './' diff --git a/conf/base.config b/conf/base.config index 07d65e8..93f74bf 100644 --- a/conf/base.config +++ b/conf/base.config @@ -13,22 +13,39 @@ process { // TODO nf-core: Check the defaults for all processes cpus = { check_max( 1 * task.attempt, 'cpus' ) } - memory = { check_max( 8.GB * task.attempt, 'memory' ) } - time = { check_max( 2.h * task.attempt, 'time' ) } + memory = { check_max( 7.GB * task.attempt, 'memory' ) } + time = { check_max( 4.h * task.attempt, 'time' ) } errorStrategy = { task.exitStatus in [143,137,104,134,139] ? 'retry' : 'finish' } maxRetries = 1 maxErrors = '-1' // Process-specific resource requirements + // NOTE - Only one of the labels below are used in the fastqc process in the main script. + // If possible, it would be nice to keep the same label naming convention when + // adding in your processes. // TODO nf-core: Customise requirements for specific processes. // See https://www.nextflow.io/docs/latest/config.html#config-process-selectors -} - -params { - // Defaults only, expecting to be overwritten - max_memory = 128.GB - max_cpus = 16 - max_time = 240.h - igenomes_base = 's3://ngi-igenomes/igenomes/' + withLabel:process_low { + cpus = { check_max( 2 * task.attempt, 'cpus' ) } + memory = { check_max( 14.GB * task.attempt, 'memory' ) } + time = { check_max( 6.h * task.attempt, 'time' ) } + } + withLabel:process_medium { + cpus = { check_max( 6 * task.attempt, 'cpus' ) } + memory = { check_max( 42.GB * task.attempt, 'memory' ) } + time = { check_max( 8.h * task.attempt, 'time' ) } + } + withLabel:process_high { + cpus = { check_max( 12 * task.attempt, 'cpus' ) } + memory = { check_max( 84.GB * task.attempt, 'memory' ) } + time = { check_max( 10.h * task.attempt, 'time' ) } + } + withLabel:process_long { + time = { check_max( 20.h * task.attempt, 'time' ) } + } + withName:get_software_versions { + cache = false + } + } diff --git a/conf/igenomes.config b/conf/igenomes.config index d19e61f..2de9242 100644 --- a/conf/igenomes.config +++ b/conf/igenomes.config @@ -9,139 +9,412 @@ params { // illumina iGenomes reference file paths - // TODO nf-core: Add new reference types and strip out those that are not needed genomes { 'GRCh37' { - bed12 = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/README.txt" + mito_name = "MT" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/GRCh37-blacklist.bed" + } + 'GRCh38' { + fasta = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Annotation/Genes/genes.bed" + mito_name = "chrM" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/hg38-blacklist.bed" } 'GRCm38' { - bed12 = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/README.txt" + mito_name = "MT" + macs_gsize = "1.87e9" + blacklist = "${baseDir}/assets/blacklists/GRCm38-blacklist.bed" } 'TAIR10' { - bed12 = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/README.txt" + mito_name = "Mt" } 'EB2' { - bed12 = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/README.txt" } 'UMD3.1' { - bed12 = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/README.txt" + mito_name = "MT" } 'WBcel235' { - bed12 = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.bed" + mito_name = "MtDNA" + macs_gsize = "9e7" } 'CanFam3.1' { - bed12 = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/README.txt" + mito_name = "MT" } 'GRCz10' { - bed12 = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.bed" + mito_name = "MT" } 'BDGP6' { - bed12 = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.bed" + mito_name = "M" + macs_gsize = "1.2e8" } 'EquCab2' { - bed12 = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/README.txt" + mito_name = "MT" } 'EB1' { - bed12 = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/README.txt" } 'Galgal4' { - bed12 = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.bed" + mito_name = "MT" } 'Gm01' { - bed12 = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/README.txt" } 'Mmul_1' { - bed12 = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/README.txt" + mito_name = "MT" } 'IRGSP-1.0' { - bed12 = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.bed" + mito_name = "Mt" } 'CHIMP2.1.4' { - bed12 = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/README.txt" + mito_name = "MT" } 'Rnor_6.0' { - bed12 = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.bed" + mito_name = "MT" } 'R64-1-1' { - bed12 = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.bed" + mito_name = "MT" + macs_gsize = "1.2e7" } 'EF2' { - bed12 = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/README.txt" + mito_name = "MT" + macs_gsize = "1.21e7" } 'Sbi1' { - bed12 = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/README.txt" } 'Sscrofa10.2' { - bed12 = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/README.txt" + mito_name = "MT" } 'AGPv3' { - bed12 = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.bed" + mito_name = "Mt" + } + 'hg38' { + fasta = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.bed" + mito_name = "chrM" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/hg38-blacklist.bed" + } + 'hg19' { + fasta = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/hg19-blacklist.bed" + } + 'mm10' { + fasta = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "1.87e9" + blacklist = "${baseDir}/assets/blacklists/mm10-blacklist.bed" + } + 'bosTau8' { + fasta = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Annotation/Genes/genes.bed" + mito_name = "chrM" + } + 'ce10' { + fasta = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "9e7" + } + 'canFam3' { + fasta = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/README.txt" + mito_name = "chrM" + } + 'danRer10' { + fasta = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Annotation/Genes/genes.bed" + mito_name = "chrM" + } + 'dm6' { + fasta = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Annotation/Genes/genes.bed" + mito_name = "chrM" + macs_gsize = "1.2e8" + } + 'equCab2' { + fasta = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/README.txt" + mito_name = "chrM" + } + 'galGal4' { + fasta = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/README.txt" + mito_name = "chrM" + } + 'panTro4' { + fasta = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/README.txt" + mito_name = "chrM" + } + 'rn6' { + fasta = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Annotation/Genes/genes.bed" + mito_name = "chrM" + } + 'sacCer3' { + fasta = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/BismarkIndex/" + readme = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "1.2e7" + } + 'susScr3' { + fasta = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/README.txt" + mito_name = "chrM" } } } diff --git a/conf/test.config b/conf/test.config index ecf5333..858eeed 100644 --- a/conf/test.config +++ b/conf/test.config @@ -4,7 +4,7 @@ * ------------------------------------------------- * Defines bundled input files and everything required * to run a fast and simple test. Use as follows: - * nextflow run nf-core/coproid -profile test + * nextflow run nf-core/coproid -profile test, */ params { @@ -14,10 +14,11 @@ params { max_cpus = 2 max_memory = 6.GB max_time = 48.h + // Input data // TODO nf-core: Specify the paths to your test data on nf-core/test-datasets // TODO nf-core: Give any required params for the test so that command line flags are not needed - singleEnd = false + single_end = false readPaths = [ ['Testdata', ['https://github.com/nf-core/test-datasets/raw/exoseq/testdata/Testdata_R1.tiny.fastq.gz', 'https://github.com/nf-core/test-datasets/raw/exoseq/testdata/Testdata_R2.tiny.fastq.gz']], ['SRR389222', ['https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub1.fastq.gz', 'https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub2.fastq.gz']] diff --git a/docs/images/nf-core-coproid_logo.png b/docs/images/nf-core-coproid_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..571c71d95ac979cd48e6d87dd37c90189fb2684c GIT binary patch literal 21191 zcmdqJ^;2Bi^970%Jh(%U1b26b;O;KL26xv0!4upygS$I}C%6voE`z&1?!BMyPw)K& zuWF`F)zq2U-MzY3ckO-FM5-vspdk|>LqS2I$$phogMxz5fr5gD`+)HN$#t*X*!u;^ z>8q|Q6cl>@KX2${26SR5s83L`l42TOStp%=eg>M^Z*PKD4qpyA6(x{>CZ+k`)sVF+ ze*E~6ECnY2@dGEYFyu!FGyQSA;Wsp6;i~e0=U_&1DR}oeDf&x!sZ=RZNl8&rkeVD_ zV*@ZLqsS&h=?llc-y5$hM~@!3Hld#=M!3_i})B&FlEHONDye$E)Y#Ftdqj8M?C z$6T3DX4Y3p6Qqz&A3~r&<@n@9vWLv@G_ch*HE?OEazH(3`^qsw^G6&zI4$TvRwuwQ z2(+i4^Qv!XuS`T)^G_5_iLNrVyRcTT3{)l+t)R#oofz|&^1s%YXv(tbpvall($1&B zb|p^iGe7rXE2sOANMKk^qa_Od-*Adkxso_xC*N54gXDv3Qp;_w{86lJcm45}DzLSQV|RTq z1kXh~6*KF8fUw>pNR*C3zz_ynsIVZ4VhGYW9>qgBtINNWD@_LYdVXCiBft#i(mXs3 zPZdG*pTOrB!maOV^z8j>T-xmZj-BcxQkK@H=M`x8B0a$%g<0==NMX>xFnkj+0Mb}* zHDNhZr0Cw{a6}0Mps#U(M?qM!29LGnJxwu*q4aQ&qE4I6)bE?vM`w=bJrW!&mO1eH zMvLE|p%L2o6s7aEglRgqSFOoVd|F(eZ1gNnzvd zFU@kY>S|MU5=Typk(ZpeBWMF0^)82~;-CO24AIiMI7eMMc3L?PY*aAi zn1`oDSLaqC2GRVHmdq+CiGMQjoZxxI@+%kykUoM15s!r#<_Z`MX=lDw29TB<2Q%FW ze0neTGCtTe{Y}q#nC51MJX*jJ==LHt#0uvrmjK^pK z*DNkA4#wYdZz2Y@^&OQJt1!dp)f6I}?|4N*-Lsk=rgUC071gF1(REImE$8cWEY?-= z5tKFSs*}A(KzyEwJZ!aH54q_~E#~1v3SN*I$jk<(#xC0>mdmL9(+6=_7ZP9{ldn1P z`8I*QEGG&cBghONx@{I?0j*u_;gOI_@ni7Z2fxF_g`ONf`pJ0}RvHP_Pm4kWYK@S0 z*wMlVftDq+zBlSwe=<@=9UW!T^Q%~eBz|>?2t6tYjb-3z_=F<6?@adjaLAPd*lu?c zch! 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Most of the plots a ## Pipeline overview + The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps: @@ -12,6 +13,7 @@ and processes data using the following steps: * [MultiQC](#multiqc) - aggregate report, describing results of the whole pipeline ## FastQC + [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your reads. It provides information about the quality score distribution across your reads, the per base sequence content (%T/A/G/C). You get information about adapter contamination and other overrepresented sequences. For further reading and documentation see the [FastQC help](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/). @@ -25,8 +27,8 @@ For further reading and documentation see the [FastQC help](http://www.bioinform * `zips/sample_fastqc.zip` * zip file containing the FastQC report, tab-delimited data file and plot images - ## MultiQC + [MultiQC](http://multiqc.info) is a visualisation tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in within the report data directory. The pipeline has special steps which allow the software versions used to be reported in the MultiQC output for future traceability. diff --git a/docs/usage.md b/docs/usage.md index 5d58e0e..703eab2 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -2,8 +2,6 @@ ## Table of contents - - * [Table of contents](#table-of-contents) * [Introduction](#introduction) * [Running the pipeline](#running-the-pipeline) @@ -12,20 +10,23 @@ * [Main arguments](#main-arguments) * [`-profile`](#-profile) * [`--reads`](#--reads) - * [`--singleEnd`](#--singleend) + * [`--single_end`](#--single_end) * [Reference genomes](#reference-genomes) * [`--genome` (using iGenomes)](#--genome-using-igenomes) * [`--fasta`](#--fasta) - * [`--igenomesIgnore`](#--igenomesignore) + * [`--igenomes_ignore`](#--igenomes_ignore) * [Job resources](#job-resources) * [Automatic resubmission](#automatic-resubmission) * [Custom resource requests](#custom-resource-requests) * [AWS Batch specific parameters](#aws-batch-specific-parameters) * [`--awsqueue`](#--awsqueue) * [`--awsregion`](#--awsregion) + * [`--awscli`](#--awscli) * [Other command line parameters](#other-command-line-parameters) * [`--outdir`](#--outdir) * [`--email`](#--email) + * [`--email_on_fail`](#--email_on_fail) + * [`--max_multiqc_email_size`](#--max_multiqc_email_size) * [`-name`](#-name) * [`-resume`](#-resume) * [`-c`](#-c) @@ -37,10 +38,9 @@ * [`--plaintext_email`](#--plaintext_email) * [`--monochrome_logs`](#--monochrome_logs) * [`--multiqc_config`](#--multiqc_config) - - ## Introduction + Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through `screen` / `tmux` or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler. It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in `~/.bashrc` or `~./bash_profile`): @@ -52,6 +52,7 @@ NXF_OPTS='-Xms1g -Xmx4g' ## Running the pipeline + The typical command for running the pipeline is as follows: ```bash @@ -70,6 +71,7 @@ results # Finished results (configurable, see below) ``` ### Updating the pipeline + When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline: ```bash @@ -77,22 +79,28 @@ nextflow pull nf-core/coproid ``` ### Reproducibility + It's a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since. First, go to the [nf-core/coproid releases page](https://github.com/nf-core/coproid/releases) and find the latest version number - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. - ## Main arguments ### `-profile` -Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. Note that multiple profiles can be loaded, for example: `-profile docker` - the order of arguments is important! -If `-profile` is not specified at all the pipeline will be run locally and expects all software to be installed and available on the `PATH`. +Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. + +Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Conda) - see below. + +The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation). + +Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important! +They are loaded in sequence, so later profiles can overwrite earlier profiles. + +If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended. -* `awsbatch` - * A generic configuration profile to be used with AWS Batch. * `conda` * A generic configuration profile to be used with [conda](https://conda.io/docs/) * Pulls most software from [Bioconda](https://bioconda.github.io/) @@ -109,6 +117,7 @@ If `-profile` is not specified at all the pipeline will be run locally and expec ### `--reads` + Use this to specify the location of your input FastQ files. For example: ```bash @@ -123,21 +132,22 @@ Please note the following requirements: If left unspecified, a default pattern is used: `data/*{1,2}.fastq.gz` -### `--singleEnd` -By default, the pipeline expects paired-end data. If you have single-end data, you need to specify `--singleEnd` on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for `--reads`. For example: +### `--single_end` + +By default, the pipeline expects paired-end data. If you have single-end data, you need to specify `--single_end` on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for `--reads`. For example: ```bash ---singleEnd --reads '*.fastq' +--single_end --reads '*.fastq' ``` It is not possible to run a mixture of single-end and paired-end files in one run. - ## Reference genomes The pipeline config files come bundled with paths to the illumina iGenomes reference index files. If running with docker or AWS, the configuration is set up to use the [AWS-iGenomes](https://ewels.github.io/AWS-iGenomes/) resource. ### `--genome` (using iGenomes) + There are 31 different species supported in the iGenomes references. To run the pipeline, you must specify which to use with the `--genome` flag. You can find the keys to specify the genomes in the [iGenomes config file](../conf/igenomes.config). Common genomes that are supported are: @@ -171,33 +181,48 @@ params { ``` + ### `--fasta` + If you prefer, you can specify the full path to your reference genome when you run the pipeline: ```bash --fasta '[path to Fasta reference]' ``` -### `--igenomesIgnore` +### `--igenomes_ignore` + Do not load `igenomes.config` when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in `igenomes.config`. ## Job resources + ### Automatic resubmission + Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with an error code of `143` (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped. ### Custom resource requests + Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at [`nf-core/configs`](https://github.com/nf-core/configs/tree/master/conf) for examples. If you are likely to be running `nf-core` pipelines regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter (see definition below). You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile. -If you have any questions or issues please send us a message on [Slack](https://nf-core-invite.herokuapp.com/). +If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack). ## AWS Batch specific parameters -Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use the `-awsbatch` profile and then specify all of the following parameters. + +Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use [`-profile awsbatch`](https://github.com/nf-core/configs/blob/master/conf/awsbatch.config) and then specify all of the following parameters. + ### `--awsqueue` + The JobQueue that you intend to use on AWS Batch. + ### `--awsregion` -The AWS region to run your job in. Default is set to `eu-west-1` but can be adjusted to your needs. + +The AWS region in which to run your job. Default is set to `eu-west-1` but can be adjusted to your needs. + +### `--awscli` + +The [AWS CLI](https://www.nextflow.io/docs/latest/awscloud.html#aws-cli-installation) path in your custom AMI. Default: `/home/ec2-user/miniconda/bin/aws`. Please make sure to also set the `-w/--work-dir` and `--outdir` parameters to a S3 storage bucket of your choice - you'll get an error message notifying you if you didn't. @@ -206,12 +231,23 @@ Please make sure to also set the `-w/--work-dir` and `--outdir` parameters to a ### `--outdir` + The output directory where the results will be saved. ### `--email` + Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (`~/.nextflow/config`) then you don't need to specify this on the command line for every run. +### `--email_on_fail` + +This works exactly as with `--email`, except emails are only sent if the workflow is not successful. + +### `--max_multiqc_email_size` + +Threshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB). + ### `-name` + Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic. This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always). @@ -219,6 +255,7 @@ This is used in the MultiQC report (if not default) and in the summary HTML / e- **NB:** Single hyphen (core Nextflow option) ### `-resume` + Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names. @@ -226,6 +263,7 @@ You can also supply a run name to resume a specific run: `-resume [run-name]`. U **NB:** Single hyphen (core Nextflow option) ### `-c` + Specify the path to a specific config file (this is a core NextFlow command). **NB:** Single hyphen (core Nextflow option) @@ -233,7 +271,8 @@ Specify the path to a specific config file (this is a core NextFlow command). Note - you can use this to override pipeline defaults. ### `--custom_config_version` -Provide git commit id for custom Institutional configs hosted at `nf-core/configs`. This was implemented for reproducibility purposes. Default is set to `master`. + +Provide git commit id for custom Institutional configs hosted at `nf-core/configs`. This was implemented for reproducibility purposes. Default: `master`. ```bash ## Download and use config file with following git commid id @@ -241,6 +280,7 @@ Provide git commit id for custom Institutional configs hosted at `nf-core/config ``` ### `--custom_config_base` + If you're running offline, nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell nextflow where to find them with the @@ -261,22 +301,28 @@ nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs > files + singularity containers + institutional configs in one go for you, to make this process easier. ### `--max_memory` + Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. `--max_memory '8.GB'` ### `--max_time` + Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. `--max_time '2.h'` ### `--max_cpus` + Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. `--max_cpus 1` ### `--plaintext_email` + Set to receive plain-text e-mails instead of HTML formatted. ### `--monochrome_logs` + Set to disable colourful command line output and live life in monochrome. ### `--multiqc_config` + Specify a path to a custom MultiQC configuration file. diff --git a/environment.yml b/environment.yml index 18d220d..0271e5f 100644 --- a/environment.yml +++ b/environment.yml @@ -1,11 +1,14 @@ # You can use this file to create a conda environment for this pipeline: # conda env create -f environment.yml -name: nf-core-coproid-1.0dev +name: nf-core-coproid-1.1dev channels: - conda-forge - bioconda - defaults dependencies: + - conda-forge::python=3.7.3 # TODO nf-core: Add required software dependencies here - - fastqc=0.11.8 - - multiqc=1.7 + - bioconda::fastqc=0.11.8 + - bioconda::multiqc=1.7 + - conda-forge::r-markdown=1.1 + - conda-forge::r-base=3.6.1 diff --git a/main.nf b/main.nf index c009ef3..0f616b1 100644 --- a/main.nf +++ b/main.nf @@ -9,7 +9,6 @@ ---------------------------------------------------------------------------------------- */ - def helpMessage() { // TODO nf-core: Add to this help message with new command line parameters log.info nfcoreHeader() @@ -22,39 +21,41 @@ def helpMessage() { nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' -profile docker Mandatory arguments: - --reads Path to input data (must be surrounded with quotes) - -profile Configuration profile to use. Can use multiple (comma separated) - Available: conda, docker, singularity, awsbatch, test and more. + --reads [file] Path to input data (must be surrounded with quotes) + -profile [str] Configuration profile to use. Can use multiple (comma separated) + Available: conda, docker, singularity, test, awsbatch and more Options: - --genome Name of iGenomes reference - --singleEnd Specifies that the input is single end reads + --genome [str] Name of iGenomes reference + --single_end [bool] Specifies that the input is single-end reads - References If not specified in the configuration file or you wish to overwrite any of the references. - --fasta Path to Fasta reference + References If not specified in the configuration file or you wish to overwrite any of the references + --fasta [file] Path to fasta reference Other options: - --outdir The output directory where the results will be saved - --email Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits - --maxMultiqcEmailFileSize Theshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB) - -name Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic. + --outdir [file] The output directory where the results will be saved + --email [email] Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits + --email_on_fail [email] Same as --email, except only send mail if the workflow is not successful + --max_multiqc_email_size [str] Theshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB) + -name [str] Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic AWSBatch options: - --awsqueue The AWSBatch JobQueue that needs to be set when running on AWSBatch - --awsregion The AWS Region for your AWS Batch job to run on + --awsqueue [str] The AWSBatch JobQueue that needs to be set when running on AWSBatch + --awsregion [str] The AWS Region for your AWS Batch job to run on + --awscli [str] Path to the AWS CLI tool """.stripIndent() } -/* - * SET UP CONFIGURATION VARIABLES - */ - -// Show help emssage -if (params.help){ +// Show help message +if (params.help) { helpMessage() exit 0 } +/* + * SET UP CONFIGURATION VARIABLES + */ + // Check if genome exists in the config file if (params.genomes && params.genome && !params.genomes.containsKey(params.genome)) { exit 1, "The provided genome '${params.genome}' is not available in the iGenomes file. Currently the available genomes are ${params.genomes.keySet().join(", ")}" @@ -62,96 +63,92 @@ if (params.genomes && params.genome && !params.genomes.containsKey(params.genome // TODO nf-core: Add any reference files that are needed // Configurable reference genomes -fasta = params.genome ? params.genomes[ params.genome ].fasta ?: false : false -if ( params.fasta ){ - fasta = file(params.fasta) - if( !fasta.exists() ) exit 1, "Fasta file not found: ${params.fasta}" -} // // NOTE - THIS IS NOT USED IN THIS PIPELINE, EXAMPLE ONLY -// If you want to use the above in a process, define the following: +// If you want to use the channel below in a process, define the following: // input: -// file fasta from fasta +// file fasta from ch_fasta // - +params.fasta = params.genome ? params.genomes[ params.genome ].fasta ?: false : false +if (params.fasta) { ch_fasta = file(params.fasta, checkIfExists: true) } // Has the run name been specified by the user? // this has the bonus effect of catching both -name and --name custom_runName = params.name -if( !(workflow.runName ==~ /[a-z]+_[a-z]+/) ){ - custom_runName = workflow.runName +if (!(workflow.runName ==~ /[a-z]+_[a-z]+/)) { + custom_runName = workflow.runName } - -if( workflow.profile == 'awsbatch') { - // AWSBatch sanity checking - if (!params.awsqueue || !params.awsregion) exit 1, "Specify correct --awsqueue and --awsregion parameters on AWSBatch!" - // Check outdir paths to be S3 buckets if running on AWSBatch - // related: https://github.com/nextflow-io/nextflow/issues/813 - if (!params.outdir.startsWith('s3:')) exit 1, "Outdir not on S3 - specify S3 Bucket to run on AWSBatch!" - // Prevent trace files to be stored on S3 since S3 does not support rolling files. - if (workflow.tracedir.startsWith('s3:')) exit 1, "Specify a local tracedir or run without trace! S3 cannot be used for tracefiles." +if (workflow.profile.contains('awsbatch')) { + // AWSBatch sanity checking + if (!params.awsqueue || !params.awsregion) exit 1, "Specify correct --awsqueue and --awsregion parameters on AWSBatch!" + // Check outdir paths to be S3 buckets if running on AWSBatch + // related: https://github.com/nextflow-io/nextflow/issues/813 + if (!params.outdir.startsWith('s3:')) exit 1, "Outdir not on S3 - specify S3 Bucket to run on AWSBatch!" + // Prevent trace files to be stored on S3 since S3 does not support rolling files. + if (params.tracedir.startsWith('s3:')) exit 1, "Specify a local tracedir or run without trace! S3 cannot be used for tracefiles." } // Stage config files -ch_multiqc_config = Channel.fromPath(params.multiqc_config) -ch_output_docs = Channel.fromPath("$baseDir/docs/output.md") +ch_multiqc_config = file(params.multiqc_config, checkIfExists: true) +ch_output_docs = file("$baseDir/docs/output.md", checkIfExists: true) /* * Create a channel for input read files */ -if(params.readPaths){ - if(params.singleEnd){ +if (params.readPaths) { + if (params.single_end) { Channel .from(params.readPaths) - .map { row -> [ row[0], [file(row[1][0])]] } + .map { row -> [ row[0], [ file(row[1][0], checkIfExists: true) ] ] } .ifEmpty { exit 1, "params.readPaths was empty - no input files supplied" } - .into { read_files_fastqc; read_files_trimming } + .into { ch_read_files_fastqc; ch_read_files_trimming } } else { Channel .from(params.readPaths) - .map { row -> [ row[0], [file(row[1][0]), file(row[1][1])]] } + .map { row -> [ row[0], [ file(row[1][0], checkIfExists: true), file(row[1][1], checkIfExists: true) ] ] } .ifEmpty { exit 1, "params.readPaths was empty - no input files supplied" } - .into { read_files_fastqc; read_files_trimming } + .into { ch_read_files_fastqc; ch_read_files_trimming } } } else { Channel - .fromFilePairs( params.reads, size: params.singleEnd ? 1 : 2 ) - .ifEmpty { exit 1, "Cannot find any reads matching: ${params.reads}\nNB: Path needs to be enclosed in quotes!\nIf this is single-end data, please specify --singleEnd on the command line." } - .into { read_files_fastqc; read_files_trimming } + .fromFilePairs(params.reads, size: params.single_end ? 1 : 2) + .ifEmpty { exit 1, "Cannot find any reads matching: ${params.reads}\nNB: Path needs to be enclosed in quotes!\nIf this is single-end data, please specify --single_end on the command line." } + .into { ch_read_files_fastqc; ch_read_files_trimming } } - // Header log info log.info nfcoreHeader() def summary = [:] -if(workflow.revision) summary['Pipeline Release'] = workflow.revision +if (workflow.revision) summary['Pipeline Release'] = workflow.revision summary['Run Name'] = custom_runName ?: workflow.runName // TODO nf-core: Report custom parameters here summary['Reads'] = params.reads summary['Fasta Ref'] = params.fasta -summary['Data Type'] = params.singleEnd ? 'Single-End' : 'Paired-End' +summary['Data Type'] = params.single_end ? 'Single-End' : 'Paired-End' summary['Max Resources'] = "$params.max_memory memory, $params.max_cpus cpus, $params.max_time time per job" -if(workflow.containerEngine) summary['Container'] = "$workflow.containerEngine - $workflow.container" +if (workflow.containerEngine) summary['Container'] = "$workflow.containerEngine - $workflow.container" summary['Output dir'] = params.outdir summary['Launch dir'] = workflow.launchDir summary['Working dir'] = workflow.workDir summary['Script dir'] = workflow.projectDir summary['User'] = workflow.userName -if(workflow.profile == 'awsbatch'){ - summary['AWS Region'] = params.awsregion - summary['AWS Queue'] = params.awsqueue +if (workflow.profile.contains('awsbatch')) { + summary['AWS Region'] = params.awsregion + summary['AWS Queue'] = params.awsqueue + summary['AWS CLI'] = params.awscli } summary['Config Profile'] = workflow.profile -if(params.config_profile_description) summary['Config Description'] = params.config_profile_description -if(params.config_profile_contact) summary['Config Contact'] = params.config_profile_contact -if(params.config_profile_url) summary['Config URL'] = params.config_profile_url -if(params.email) { - summary['E-mail Address'] = params.email - summary['MultiQC maxsize'] = params.maxMultiqcEmailFileSize +if (params.config_profile_description) summary['Config Description'] = params.config_profile_description +if (params.config_profile_contact) summary['Config Contact'] = params.config_profile_contact +if (params.config_profile_url) summary['Config URL'] = params.config_profile_url +if (params.email || params.email_on_fail) { + summary['E-mail Address'] = params.email + summary['E-mail on failure'] = params.email_on_fail + summary['MultiQC maxsize'] = params.max_multiqc_email_size } log.info summary.collect { k,v -> "${k.padRight(18)}: $v" }.join("\n") -log.info "\033[2m----------------------------------------------------\033[0m" +log.info "-\033[2m--------------------------------------------------\033[0m-" // Check the hostnames against configured profiles checkHostname() @@ -173,19 +170,18 @@ ${summary.collect { k,v -> "

$k
${v ?: ' - if (filename.indexOf(".csv") > 0) filename - else null - } + saveAs: { filename -> + if (filename.indexOf(".csv") > 0) filename + else null + } output: - file 'software_versions_mqc.yaml' into software_versions_yaml + file 'software_versions_mqc.yaml' into ch_software_versions_yaml file "software_versions.csv" script: @@ -199,30 +195,29 @@ process get_software_versions { """ } - - /* * STEP 1 - FastQC */ process fastqc { tag "$name" + label 'process_medium' publishDir "${params.outdir}/fastqc", mode: 'copy', - saveAs: {filename -> filename.indexOf(".zip") > 0 ? "zips/$filename" : "$filename"} + saveAs: { filename -> + filename.indexOf(".zip") > 0 ? "zips/$filename" : "$filename" + } input: - set val(name), file(reads) from read_files_fastqc + set val(name), file(reads) from ch_read_files_fastqc output: - file "*_fastqc.{zip,html}" into fastqc_results + file "*_fastqc.{zip,html}" into ch_fastqc_results script: """ - fastqc -q $reads + fastqc --quiet --threads $task.cpus $reads """ } - - /* * STEP 2 - MultiQC */ @@ -232,12 +227,12 @@ process multiqc { input: file multiqc_config from ch_multiqc_config // TODO nf-core: Add in log files from your new processes for MultiQC to find! - file ('fastqc/*') from fastqc_results.collect().ifEmpty([]) - file ('software_versions/*') from software_versions_yaml.collect() + file ('fastqc/*') from ch_fastqc_results.collect().ifEmpty([]) + file ('software_versions/*') from ch_software_versions_yaml.collect() file workflow_summary from create_workflow_summary(summary) output: - file "*multiqc_report.html" into multiqc_report + file "*multiqc_report.html" into ch_multiqc_report file "*_data" file "multiqc_plots" @@ -250,8 +245,6 @@ process multiqc { """ } - - /* * STEP 3 - Output Description HTML */ @@ -270,8 +263,6 @@ process output_documentation { """ } - - /* * Completion e-mail notification */ @@ -279,8 +270,8 @@ workflow.onComplete { // Set up the e-mail variables def subject = "[nf-core/coproid] Successful: $workflow.runName" - if(!workflow.success){ - subject = "[nf-core/coproid] FAILED: $workflow.runName" + if (!workflow.success) { + subject = "[nf-core/coproid] FAILED: $workflow.runName" } def email_fields = [:] email_fields['version'] = workflow.manifest.version @@ -298,21 +289,20 @@ workflow.onComplete { email_fields['summary']['Date Completed'] = workflow.complete email_fields['summary']['Pipeline script file path'] = workflow.scriptFile email_fields['summary']['Pipeline script hash ID'] = workflow.scriptId - if(workflow.repository) email_fields['summary']['Pipeline repository Git URL'] = workflow.repository - if(workflow.commitId) email_fields['summary']['Pipeline repository Git Commit'] = workflow.commitId - if(workflow.revision) email_fields['summary']['Pipeline Git branch/tag'] = workflow.revision - if(workflow.container) email_fields['summary']['Docker image'] = workflow.container + if (workflow.repository) email_fields['summary']['Pipeline repository Git URL'] = workflow.repository + if (workflow.commitId) email_fields['summary']['Pipeline repository Git Commit'] = workflow.commitId + if (workflow.revision) email_fields['summary']['Pipeline Git branch/tag'] = workflow.revision email_fields['summary']['Nextflow Version'] = workflow.nextflow.version email_fields['summary']['Nextflow Build'] = workflow.nextflow.build email_fields['summary']['Nextflow Compile Timestamp'] = workflow.nextflow.timestamp - // TODO nf-core: If not using MultiQC, strip out this code (including params.maxMultiqcEmailFileSize) + // TODO nf-core: If not using MultiQC, strip out this code (including params.max_multiqc_email_size) // On success try attach the multiqc report def mqc_report = null try { if (workflow.success) { - mqc_report = multiqc_report.getVal() - if (mqc_report.getClass() == ArrayList){ + mqc_report = ch_multiqc_report.getVal() + if (mqc_report.getClass() == ArrayList) { log.warn "[nf-core/coproid] Found multiple reports from process 'multiqc', will use only one" mqc_report = mqc_report[0] } @@ -321,6 +311,12 @@ workflow.onComplete { log.warn "[nf-core/coproid] Could not attach MultiQC report to summary email" } + // Check if we are only sending emails on failure + email_address = params.email + if (!params.email && params.email_on_fail && !workflow.success) { + email_address = params.email_on_fail + } + // Render the TXT template def engine = new groovy.text.GStringTemplateEngine() def tf = new File("$baseDir/assets/email_template.txt") @@ -333,89 +329,89 @@ workflow.onComplete { def email_html = html_template.toString() // Render the sendmail template - def smail_fields = [ email: params.email, subject: subject, email_txt: email_txt, email_html: email_html, baseDir: "$baseDir", mqcFile: mqc_report, mqcMaxSize: params.maxMultiqcEmailFileSize.toBytes() ] + def smail_fields = [ email: email_address, subject: subject, email_txt: email_txt, email_html: email_html, baseDir: "$baseDir", mqcFile: mqc_report, mqcMaxSize: params.max_multiqc_email_size.toBytes() ] def sf = new File("$baseDir/assets/sendmail_template.txt") def sendmail_template = engine.createTemplate(sf).make(smail_fields) def sendmail_html = sendmail_template.toString() // Send the HTML e-mail - if (params.email) { + if (email_address) { try { - if( params.plaintext_email ){ throw GroovyException('Send plaintext e-mail, not HTML') } - // Try to send HTML e-mail using sendmail - [ 'sendmail', '-t' ].execute() << sendmail_html - log.info "[nf-core/coproid] Sent summary e-mail to $params.email (sendmail)" + if (params.plaintext_email) { throw GroovyException('Send plaintext e-mail, not HTML') } + // Try to send HTML e-mail using sendmail + [ 'sendmail', '-t' ].execute() << sendmail_html + log.info "[nf-core/coproid] Sent summary e-mail to $email_address (sendmail)" } catch (all) { - // Catch failures and try with plaintext - [ 'mail', '-s', subject, params.email ].execute() << email_txt - log.info "[nf-core/coproid] Sent summary e-mail to $params.email (mail)" + // Catch failures and try with plaintext + [ 'mail', '-s', subject, email_address ].execute() << email_txt + log.info "[nf-core/coproid] Sent summary e-mail to $email_address (mail)" } } // Write summary e-mail HTML to a file - def output_d = new File( "${params.outdir}/pipeline_info/" ) - if( !output_d.exists() ) { - output_d.mkdirs() + def output_d = new File("${params.outdir}/pipeline_info/") + if (!output_d.exists()) { + output_d.mkdirs() } - def output_hf = new File( output_d, "pipeline_report.html" ) + def output_hf = new File(output_d, "pipeline_report.html") output_hf.withWriter { w -> w << email_html } - def output_tf = new File( output_d, "pipeline_report.txt" ) + def output_tf = new File(output_d, "pipeline_report.txt") output_tf.withWriter { w -> w << email_txt } - c_reset = params.monochrome_logs ? '' : "\033[0m"; - c_purple = params.monochrome_logs ? '' : "\033[0;35m"; c_green = params.monochrome_logs ? '' : "\033[0;32m"; + c_purple = params.monochrome_logs ? '' : "\033[0;35m"; c_red = params.monochrome_logs ? '' : "\033[0;31m"; + c_reset = params.monochrome_logs ? '' : "\033[0m"; - if (workflow.stats.ignoredCountFmt > 0 && workflow.success) { - log.info "${c_purple}Warning, pipeline completed, but with errored process(es) ${c_reset}" - log.info "${c_red}Number of ignored errored process(es) : ${workflow.stats.ignoredCountFmt} ${c_reset}" - log.info "${c_green}Number of successfully ran process(es) : ${workflow.stats.succeedCountFmt} ${c_reset}" + if (workflow.stats.ignoredCount > 0 && workflow.success) { + log.info "-${c_purple}Warning, pipeline completed, but with errored process(es) ${c_reset}-" + log.info "-${c_red}Number of ignored errored process(es) : ${workflow.stats.ignoredCount} ${c_reset}-" + log.info "-${c_green}Number of successfully ran process(es) : ${workflow.stats.succeedCount} ${c_reset}-" } - if(workflow.success){ - log.info "${c_purple}[nf-core/coproid]${c_green} Pipeline completed successfully${c_reset}" + if (workflow.success) { + log.info "-${c_purple}[nf-core/coproid]${c_green} Pipeline completed successfully${c_reset}-" } else { checkHostname() - log.info "${c_purple}[nf-core/coproid]${c_red} Pipeline completed with errors${c_reset}" + log.info "-${c_purple}[nf-core/coproid]${c_red} Pipeline completed with errors${c_reset}-" } } -def nfcoreHeader(){ +def nfcoreHeader() { // Log colors ANSI codes - c_reset = params.monochrome_logs ? '' : "\033[0m"; - c_dim = params.monochrome_logs ? '' : "\033[2m"; c_black = params.monochrome_logs ? '' : "\033[0;30m"; - c_green = params.monochrome_logs ? '' : "\033[0;32m"; - c_yellow = params.monochrome_logs ? '' : "\033[0;33m"; c_blue = params.monochrome_logs ? '' : "\033[0;34m"; - c_purple = params.monochrome_logs ? '' : "\033[0;35m"; c_cyan = params.monochrome_logs ? '' : "\033[0;36m"; + c_dim = params.monochrome_logs ? '' : "\033[2m"; + c_green = params.monochrome_logs ? '' : "\033[0;32m"; + c_purple = params.monochrome_logs ? '' : "\033[0;35m"; + c_reset = params.monochrome_logs ? '' : "\033[0m"; c_white = params.monochrome_logs ? '' : "\033[0;37m"; + c_yellow = params.monochrome_logs ? '' : "\033[0;33m"; - return """ ${c_dim}----------------------------------------------------${c_reset} + return """ -${c_dim}--------------------------------------------------${c_reset}- ${c_green},--.${c_black}/${c_green},-.${c_reset} ${c_blue} ___ __ __ __ ___ ${c_green}/,-._.--~\'${c_reset} ${c_blue} |\\ | |__ __ / ` / \\ |__) |__ ${c_yellow}} {${c_reset} ${c_blue} | \\| | \\__, \\__/ | \\ |___ ${c_green}\\`-._,-`-,${c_reset} ${c_green}`._,._,\'${c_reset} ${c_purple} nf-core/coproid v${workflow.manifest.version}${c_reset} - ${c_dim}----------------------------------------------------${c_reset} + -${c_dim}--------------------------------------------------${c_reset}- """.stripIndent() } -def checkHostname(){ +def checkHostname() { def c_reset = params.monochrome_logs ? '' : "\033[0m" def c_white = params.monochrome_logs ? '' : "\033[0;37m" def c_red = params.monochrome_logs ? '' : "\033[1;91m" def c_yellow_bold = params.monochrome_logs ? '' : "\033[1;93m" - if(params.hostnames){ + if (params.hostnames) { def hostname = "hostname".execute().text.trim() params.hostnames.each { prof, hnames -> hnames.each { hname -> - if(hostname.contains(hname) && !workflow.profile.contains(prof)){ + if (hostname.contains(hname) && !workflow.profile.contains(prof)) { log.error "====================================================\n" + " ${c_red}WARNING!${c_reset} You are running with `-profile $workflow.profile`\n" + " but your machine hostname is ${c_white}'$hostname'${c_reset}\n" + diff --git a/nextflow.config b/nextflow.config index 6a919ec..dc43a31 100644 --- a/nextflow.config +++ b/nextflow.config @@ -10,29 +10,35 @@ params { // Workflow flags // TODO nf-core: Specify your pipeline's command line flags + genome = false reads = "data/*{1,2}.fastq.gz" - singleEnd = false + single_end = false outdir = './results' // Boilerplate options name = false multiqc_config = "$baseDir/assets/multiqc_config.yaml" email = false - maxMultiqcEmailFileSize = 25.MB + email_on_fail = false + max_multiqc_email_size = 25.MB plaintext_email = false monochrome_logs = false help = false - igenomes_base = "./iGenomes" + igenomes_base = 's3://ngi-igenomes/igenomes/' tracedir = "${params.outdir}/pipeline_info" - awsqueue = false - awsregion = 'eu-west-1' - igenomesIgnore = false + igenomes_ignore = false custom_config_version = 'master' custom_config_base = "https://raw.githubusercontent.com/nf-core/configs/${params.custom_config_version}" hostnames = false config_profile_description = false config_profile_contact = false config_profile_url = false + + // Defaults only, expecting to be overwritten + max_memory = 128.GB + max_cpus = 16 + max_time = 240.h + } // Container slug. Stable releases should specify release tag! @@ -50,19 +56,33 @@ try { } profiles { - awsbatch { includeConfig 'conf/awsbatch.config' } conda { process.conda = "$baseDir/environment.yml" } debug { process.beforeScript = 'echo $HOSTNAME' } - docker { docker.enabled = true } - singularity { singularity.enabled = true } + docker { + docker.enabled = true + // Avoid this error: + // WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap. + // Testing this in nf-core after discussion here https://github.com/nf-core/tools/pull/351 + // once this is established and works well, nextflow might implement this behavior as new default. + docker.runOptions = '-u \$(id -u):\$(id -g)' + } + singularity { + singularity.enabled = true + singularity.autoMounts = true + } test { includeConfig 'conf/test.config' } } // Load igenomes.config if required -if(!params.igenomesIgnore){ +if (!params.igenomes_ignore) { includeConfig 'conf/igenomes.config' } +// Export this variable to prevent local Python libraries from conflicting with those in the container +env { + PYTHONNOUSERSITE = 1 +} + // Capture exit codes from upstream processes when piping process.shell = ['/bin/bash', '-euo', 'pipefail'] @@ -89,16 +109,16 @@ manifest { homePage = 'https://github.com/nf-core/coproid' description = 'Coprolite Identification' mainScript = 'main.nf' - nextflowVersion = '>=0.32.0' - version = '1.0dev' + nextflowVersion = '>=19.10.0' + version = '1.1dev' } // Function to ensure that resource requirements don't go beyond // a maximum limit def check_max(obj, type) { - if(type == 'memory'){ + if (type == 'memory') { try { - if(obj.compareTo(params.max_memory as nextflow.util.MemoryUnit) == 1) + if (obj.compareTo(params.max_memory as nextflow.util.MemoryUnit) == 1) return params.max_memory as nextflow.util.MemoryUnit else return obj @@ -106,9 +126,9 @@ def check_max(obj, type) { println " ### ERROR ### Max memory '${params.max_memory}' is not valid! Using default value: $obj" return obj } - } else if(type == 'time'){ + } else if (type == 'time') { try { - if(obj.compareTo(params.max_time as nextflow.util.Duration) == 1) + if (obj.compareTo(params.max_time as nextflow.util.Duration) == 1) return params.max_time as nextflow.util.Duration else return obj @@ -116,7 +136,7 @@ def check_max(obj, type) { println " ### ERROR ### Max time '${params.max_time}' is not valid! Using default value: $obj" return obj } - } else if(type == 'cpus'){ + } else if (type == 'cpus') { try { return Math.min( obj, params.max_cpus as int ) } catch (all) { From e7674e591a544bbfef2d65c2fbb2f8c270e69e3c Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Feb 2020 10:51:00 +0100 Subject: [PATCH 49/96] back to default docker run options --- nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index 39af96e..3a18852 100644 --- a/nextflow.config +++ b/nextflow.config @@ -86,7 +86,7 @@ profiles { // WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap. // Testing this in nf-core after discussion here https://github.com/nf-core/tools/pull/351 // once this is established and works well, nextflow might implement this behavior as new default. - docker.runOptions = '-u \$(id -u):\$(id -g)' + // docker.runOptions = '-u \$(id -u):\$(id -g)' } singularity { singularity.enabled = true From 778ef41270ca15454b3b0f7bdcf8b0edf4b91f33 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Feb 2020 11:15:09 +0100 Subject: [PATCH 50/96] remove md linting --- .github/workflows/ci.yml | 2 +- .github/workflows/linting.yml | 26 +++++++++++++------------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d7892b4..629d94f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -4,7 +4,7 @@ name: nf-core CI on: [push, pull_request] jobs: - test: + nf_core_ci: env: NXF_VER: ${{ matrix.nxf_ver }} NXF_ANSI_LOG: false diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml index 7354dc7..251c2e2 100644 --- a/.github/workflows/linting.yml +++ b/.github/workflows/linting.yml @@ -4,19 +4,19 @@ name: nf-core linting on: [push, pull_request] jobs: - Markdown: - runs-on: ubuntu-18.04 - steps: - - uses: actions/checkout@v1 - - uses: actions/setup-node@v1 - with: - node-version: '10' - - name: Install markdownlint - run: | - npm install -g markdownlint-cli - - name: Run Markdownlint - run: | - markdownlint ${GITHUB_WORKSPACE} -c ${GITHUB_WORKSPACE}/.github/markdownlint.yml + # Markdown: + # runs-on: ubuntu-18.04 + # steps: + # - uses: actions/checkout@v1 + # - uses: actions/setup-node@v1 + # with: + # node-version: '10' + # - name: Install markdownlint + # run: | + # npm install -g markdownlint-cli + # - name: Run Markdownlint + # run: | + # markdownlint ${GITHUB_WORKSPACE} -c ${GITHUB_WORKSPACE}/.github/markdownlint.yml nf-core: runs-on: ubuntu-latest steps: From 2f42a75d682b1def5ffa595b0f74d3231946a87d Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Feb 2020 11:15:50 +0100 Subject: [PATCH 51/96] remove travis --- .travis.yml | 53 ----------------------------------------------------- 1 file changed, 53 deletions(-) delete mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 831cda6..0000000 --- a/.travis.yml +++ /dev/null @@ -1,53 +0,0 @@ -sudo: required -language: python -jdk: openjdk8 -services: docker -python: '3.6' -cache: pip -matrix: - fast_finish: true - -before_install: - # PRs to master are only ok if coming from dev branch - - '[ $TRAVIS_PULL_REQUEST = "false" ] || [ $TRAVIS_BRANCH != "master" ] || ([ $TRAVIS_PULL_REQUEST_SLUG = $TRAVIS_REPO_SLUG ] && [ $TRAVIS_PULL_REQUEST_BRANCH = "dev" ]) || [ $TRAVIS_PULL_REQUEST_BRANCH = "patch" ]' - # Pull the docker image first so the test doesn't wait for this - # - docker pull nfcore/coproid:dev - - docker pull nfcore/coproid:dev - # Fake the tag locally so that the pipeline runs properly - # Looks weird when this is :dev to :dev, but makes sense when testing code for a release (:dev to :1.0.1) - # - docker tag nfcore/coproid:dev nfcore/coproid:dev - - docker tag nfcore/coproid:dev nfcore/coproid:dev - -install: - # Install Nextflow - - mkdir /tmp/nextflow && cd /tmp/nextflow - - wget -qO- get.nextflow.io | bash - - sudo ln -s /tmp/nextflow/nextflow /usr/local/bin/nextflow - # Install nf-core/tools - - pip install --upgrade pip - - pip install nf-core - # Install Conda - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh - - bash Miniconda3-latest-Linux-x86_64.sh -b -f -p $HOME/miniconda - - export PATH="$HOME/miniconda/bin:$PATH" - # Reset - - mkdir ${TRAVIS_BUILD_DIR}/tests && cd ${TRAVIS_BUILD_DIR}/tests - # Install markdownlint-cli - - sudo apt-get install npm && npm install -g markdownlint-cli - -env: - # Tower token is to inspect runs on https://tower.nf - # Use public mailbox nf-core@mailinator.com to log in: https://www.mailinator.com/v3/index.jsp?zone=public&query=nf-core - # Specify a minimum NF version that should be tested and work - - NXF_VER='19.10.0' TOWER_ACCESS_TOKEN="1c1f493bc2703472d6f1b9f6fb9e9d117abab7b1" - # Plus: get the latest NF version and check that it works - - NXF_VER='' TOWER_ACCESS_TOKEN="1c1f493bc2703472d6f1b9f6fb9e9d117abab7b1" - - -script: - # Lint the pipeline code - - nf-core lint ${TRAVIS_BUILD_DIR} - # Lint the documentation - # Run the pipeline with the test profile - - nextflow run ${TRAVIS_BUILD_DIR} -profile test,docker -ansi-log false -name coproid-${TRAVIS_EVENT_TYPE}-${TRAVIS_PULL_REQUEST}-${TRAVIS_COMMIT:0:6}-single_end --single_end - - nextflow run ${TRAVIS_BUILD_DIR} -profile test,docker -ansi-log false -name coproid-${TRAVIS_EVENT_TYPE}-${TRAVIS_PULL_REQUEST}-${TRAVIS_COMMIT:0:6}-paired_end \ No newline at end of file From aa8b2c79041a07e62560d8b17672b214fe70541e Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Feb 2020 11:18:16 +0100 Subject: [PATCH 52/96] remove travis --- .github/workflows/ci.yml | 2 +- .github/workflows/linting.yml | 26 +++++++++++++------------- README.md | 1 - 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 629d94f..d7892b4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -4,7 +4,7 @@ name: nf-core CI on: [push, pull_request] jobs: - nf_core_ci: + test: env: NXF_VER: ${{ matrix.nxf_ver }} NXF_ANSI_LOG: false diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml index 251c2e2..7354dc7 100644 --- a/.github/workflows/linting.yml +++ b/.github/workflows/linting.yml @@ -4,19 +4,19 @@ name: nf-core linting on: [push, pull_request] jobs: - # Markdown: - # runs-on: ubuntu-18.04 - # steps: - # - uses: actions/checkout@v1 - # - uses: actions/setup-node@v1 - # with: - # node-version: '10' - # - name: Install markdownlint - # run: | - # npm install -g markdownlint-cli - # - name: Run Markdownlint - # run: | - # markdownlint ${GITHUB_WORKSPACE} -c ${GITHUB_WORKSPACE}/.github/markdownlint.yml + Markdown: + runs-on: ubuntu-18.04 + steps: + - uses: actions/checkout@v1 + - uses: actions/setup-node@v1 + with: + node-version: '10' + - name: Install markdownlint + run: | + npm install -g markdownlint-cli + - name: Run Markdownlint + run: | + markdownlint ${GITHUB_WORKSPACE} -c ${GITHUB_WORKSPACE}/.github/markdownlint.yml nf-core: runs-on: ubuntu-latest steps: diff --git a/README.md b/README.md index 0ea58b4..0c0111c 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,6 @@ **Coprolite Identification**. -[![Build Status](https://travis-ci.com/nf-core/coproid.svg?branch=master)](https://travis-ci.com/nf-core/coproid) [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) From 50e516736253246545dd63d08c92df377d42abb0 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Feb 2020 11:27:01 +0100 Subject: [PATCH 53/96] move multiqc in output doc --- docs/output.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/output.md b/docs/output.md index 7da4c16..295c990 100644 --- a/docs/output.md +++ b/docs/output.md @@ -2,8 +2,6 @@ This document describes the output produced by the coproID pipeline. -## multiqc_report.html - ## Pipeline overview The pipeline is built using [Nextflow](https://www.nextflow.io/) @@ -65,7 +63,7 @@ This directory contains the merged OTU count for all samples of the run, as coun This directory contains all the output files of DamageProfiler (see multiqc section above) -## MultiQC +## multiqc_report.html [MultiQC](http://multiqc.info) is a visualisation tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in within the report data directory. From 077e3403b169096f828dc6a2abfd55660e33c94a Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 3 Feb 2020 11:30:05 +0100 Subject: [PATCH 54/96] add changelog --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4ce8016..3531a4a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,7 +13,7 @@ c2d4164 ](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92 - Update documentation [bedfdde](https://github.com/nf-core/coproid/commit/bedfddec8500adac8e0cb9cc8e0df2dc6a784f15) - Update Nextflow minimum version to 19.04.0 [44999fd](https://github.com/nf-core/coproid/commit/44999fd4d38b21d53f970621dbf3587c044da8d1) - Update travis for more recent nextflow requirements [1e3e454](https://github.com/nf-core/coproid/commit/1e3e454e72f1bc8eb43aaa1e5165981cb77a56dc) - +- Adapt coproID to nf-core tools 1.8 release [#21](https://github.com/nf-core/coproid/pull/21) ## v1.0 - 2019-04-26 From 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b/assets/nf-core-coproid_social_preview.svg @@ -0,0 +1,448 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + Coprolite host Identification pipeline + coproid + + + + + + + + + + + + + + + + + + + + + + + + + From 4e66dd2832442d976cb5fcb3e023a05fe605b1de Mon Sep 17 00:00:00 2001 From: MaxUlysse Date: Wed, 12 Feb 2020 12:04:27 +0100 Subject: [PATCH 56/96] update CHANGELOG --- CHANGELOG.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a4452a3..bbff1f4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,18 +2,18 @@ ## v1.1dev -- Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14) -- Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) -- Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) +- Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14) +- Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) +- Update logo to match font [#13](https://github.com/nf-core/coproid/pull/13) - Update Sourcepredict to version 0.4 and reflect new parameters in coproID [#19](https://github.com/nf-core/coproid/pull/19) [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) [ -c2d4164 ](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d529470b0a3af3a69113a) +c2d4164](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d529470b0a3af3a69113a) - Changed bedtools bamtofastq to samtools fastq [e4afca7](https://github.com/nf-core/coproid/commit/e4afca7059c00ebbc753dd02d4aed3f3a1b3b7b8) - Fixed column names in report (PC* to DIM* ) [e4afca7](https://github.com/nf-core/coproid/commit/63a6bc6998c240b77791916c243d538b2268b5d5) - Update README to inlude Zenodo badge, Quick start, contributor section, and tools references. [9874ae8](https://github.com/nf-core/coproid/commit/9874ae87c88842d75c29088672aa81023408d4e7) [e85988b](https://github.com/nf-core/coproid/commit/e85988b883539aa51461e749bc14ec6563f62fc8) - Update documentation [bedfdde](https://github.com/nf-core/coproid/commit/bedfddec8500adac8e0cb9cc8e0df2dc6a784f15) - Update Nextflow minimum version to 19.04.0 [44999fd](https://github.com/nf-core/coproid/commit/44999fd4d38b21d53f970621dbf3587c044da8d1) - Update travis for more recent nextflow requirements [1e3e454](https://github.com/nf-core/coproid/commit/1e3e454e72f1bc8eb43aaa1e5165981cb77a56dc) - +- Add social preview image [#22](https://github.com/nf-core/coproid/pull/22) ## v1.0 - 2019-04-26 @@ -22,14 +22,14 @@ Adapting [CoproID](https://github.com/maxibor/coproID/tree/dev) to nf-core templ ### Improvements over [coproID version 0.6](https://github.com/maxibor/coproID/releases/tag/v0.6.0) -- Support for 3 organism comparison -- Adding [sourcepredict](https://github.com/maxibor/sourcepredict) -- Updating reports to have interactive plotting -- Updated to use Kraken2 instead of Kraken1 -- Adding docker +- Support for 3 organism comparison +- Adding [sourcepredict](https://github.com/maxibor/sourcepredict) +- Updating reports to have interactive plotting +- Updated to use Kraken2 instead of Kraken1 +- Adding docker ### Adaptions to port to _nf-core_ -- Major redefinition of the channels creation to adapt to iGenomes and profiles -- Added and adapted all the nf-core boilerplate code for support of configs and containers -- Improved documentation +- Major redefinition of the channels creation to adapt to iGenomes and profiles +- Added and adapted all the nf-core boilerplate code for support of configs and containers +- Improved documentation From 8ada752a7f58d320690869e00592fea325fcd349 Mon Sep 17 00:00:00 2001 From: runner Date: Thu, 20 Feb 2020 15:30:34 +0000 Subject: [PATCH 57/96] Template update for nf-core/tools version 1.9 --- .github/CONTRIBUTING.md | 52 ++- .github/ISSUE_TEMPLATE/bug_report.md | 43 ++- .github/ISSUE_TEMPLATE/feature_request.md | 18 +- .github/PULL_REQUEST_TEMPLATE.md | 28 +- .github/markdownlint.yml | 4 - .github/workflows/branch.yml | 16 + .github/workflows/ci.yml | 30 ++ .github/workflows/linting.yml | 50 +++ .gitignore | 3 +- .travis.yml | 42 -- CHANGELOG.md | 14 +- CODE_OF_CONDUCT.md | 2 +- Dockerfile | 12 +- README.md | 53 ++- assets/email_template.html | 2 + assets/email_template.txt | 12 +- assets/multiqc_config.yaml | 4 +- assets/nf-core-coproid_logo.png | Bin 0 -> 12502 bytes assets/sendmail_template.txt | 17 + bin/markdown_to_html.py | 100 +++++ bin/markdown_to_html.r | 51 --- bin/scrape_software_versions.py | 15 +- conf/awsbatch.config | 18 - conf/base.config | 37 +- conf/igenomes.config | 451 +++++++++++++++++----- conf/test.config | 7 +- docs/images/nf-core-coproid_logo.png | Bin 0 -> 21191 bytes docs/output.md | 4 +- docs/usage.md | 95 +++-- environment.yml | 10 +- main.nf | 278 +++++++------ nextflow.config | 56 ++- 32 files changed, 1049 insertions(+), 475 deletions(-) create mode 100644 .github/workflows/branch.yml create mode 100644 .github/workflows/ci.yml create mode 100644 .github/workflows/linting.yml delete mode 100644 .travis.yml create mode 100644 assets/nf-core-coproid_logo.png create mode 100755 bin/markdown_to_html.py delete mode 100755 bin/markdown_to_html.r delete mode 100644 conf/awsbatch.config create mode 100644 docs/images/nf-core-coproid_logo.png diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index a8deaee..47e22bb 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -1,47 +1,57 @@ # nf-core/coproid: Contributing Guidelines -Hi there! Many thanks for taking an interest in improving nf-core/coproid. +Hi there! +Many thanks for taking an interest in improving nf-core/coproid. -We try to manage the required tasks for nf-core/coproid using GitHub issues, you probably came to this page when creating one. Please use the pre-filled template to save time. - -However, don't be put off by this template - other more general issues and suggestions are welcome! Contributions to the code are even more welcome ;) - -> If you need help using or modifying nf-core/coproid then the best place to ask is on the pipeline channel on [Slack](https://nf-core-invite.herokuapp.com/). +We try to manage the required tasks for nf-core/coproid using GitHub issues, you probably came to this page when creating one. +Please use the pre-filled template to save time. +However, don't be put off by this template - other more general issues and suggestions are welcome! +Contributions to the code are even more welcome ;) +> If you need help using or modifying nf-core/coproid then the best place to ask is on the nf-core Slack [#coproid](https://nfcore.slack.com/channels/coproid) channel ([join our Slack here](https://nf-co.re/join/slack)). ## Contribution workflow -If you'd like to write some code for nf-core/coproid, the standard workflow -is as follows: -1. Check that there isn't already an issue about your idea in the - [nf-core/coproid issues](https://github.com/nf-core/coproid/issues) to avoid - duplicating work. +If you'd like to write some code for nf-core/coproid, the standard workflow is as follows: + +1. Check that there isn't already an issue about your idea in the [nf-core/coproid issues](https://github.com/nf-core/coproid/issues) to avoid duplicating work * If there isn't one already, please create one so that others know you're working on this -2. Fork the [nf-core/coproid repository](https://github.com/nf-core/coproid) to your GitHub account +2. [Fork](https://help.github.com/en/github/getting-started-with-github/fork-a-repo) the [nf-core/coproid repository](https://github.com/nf-core/coproid) to your GitHub account 3. Make the necessary changes / additions within your forked repository -4. Submit a Pull Request against the `dev` branch and wait for the code to be reviewed and merged. - -If you're not used to this workflow with git, you can start with some [basic docs from GitHub](https://help.github.com/articles/fork-a-repo/) or even their [excellent interactive tutorial](https://try.github.io/). +4. Submit a Pull Request against the `dev` branch and wait for the code to be reviewed and merged +If you're not used to this workflow with git, you can start with some [docs from GitHub](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests) or even their [excellent `git` resources](https://try.github.io/). ## Tests -When you create a pull request with changes, [Travis CI](https://travis-ci.org/) will run automatic tests. + +When you create a pull request with changes, [GitHub Actions](https://github.com/features/actions) will run automatic tests. Typically, pull-requests are only fully reviewed when these tests are passing, though of course we can help out before then. There are typically two types of tests that run: ### Lint Tests -The nf-core has a [set of guidelines](http://nf-co.re/guidelines) which all pipelines must adhere to. + +`nf-core` has a [set of guidelines](https://nf-co.re/developers/guidelines) which all pipelines must adhere to. To enforce these and ensure that all pipelines stay in sync, we have developed a helper tool which runs checks on the pipeline code. This is in the [nf-core/tools repository](https://github.com/nf-core/tools) and once installed can be run locally with the `nf-core lint ` command. If any failures or warnings are encountered, please follow the listed URL for more documentation. ### Pipeline Tests -Each nf-core pipeline should be set up with a minimal set of test-data. -Travis CI then runs the pipeline on this data to ensure that it exists successfully. + +Each `nf-core` pipeline should be set up with a minimal set of test-data. +`GitHub Actions` then runs the pipeline on this data to ensure that it exits successfully. If there are any failures then the automated tests fail. -These tests are run both with the latest available version of Nextflow and also the minimum required version that is stated in the pipeline code. +These tests are run both with the latest available version of `Nextflow` and also the minimum required version that is stated in the pipeline code. + +## Patch + +: warning: Only in the unlikely and regretful event of a release happening with a bug. + +* On your own fork, make a new branch `patch` based on `upstream/master`. +* Fix the bug, and bump version (X.Y.Z+1). +* A PR should be made on `master` from patch to directly this particular bug. ## Getting help -For further information/help, please consult the [nf-core/coproid documentation](https://github.com/nf-core/coproid#documentation) and don't hesitate to get in touch on the pipeline channel on [Slack](https://nf-core-invite.herokuapp.com/). + +For further information/help, please consult the [nf-core/coproid documentation](https://nf-co.re/nf-core/coproid/docs) and don't hesitate to get in touch on the nf-core Slack [#coproid](https://nfcore.slack.com/channels/coproid) channel ([join our Slack here](https://nf-co.re/join/slack)). diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 62c91c5..bb180cb 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,31 +1,42 @@ +# nf-core/coproid bug report + Hi there! -Thanks for telling us about a problem with the pipeline. Please delete this text and anything that's not relevant from the template below: +Thanks for telling us about a problem with the pipeline. +Please delete this text and anything that's not relevant from the template below: + +## Describe the bug -#### Describe the bug A clear and concise description of what the bug is. -#### Steps to reproduce +## Steps to reproduce + Steps to reproduce the behaviour: + 1. Command line: `nextflow run ...` 2. See error: _Please provide your error message_ -#### Expected behaviour +## Expected behaviour + A clear and concise description of what you expected to happen. -#### System: - - Hardware: [e.g. HPC, Desktop, Cloud...] - - Executor: [e.g. slurm, local, awsbatch...] - - OS: [e.g. CentOS Linux, macOS, Linux Mint...] - - Version [e.g. 7, 10.13.6, 18.3...] +## System + +- Hardware: +- Executor: +- OS: +- Version + +## Nextflow Installation + +- Version: + +## Container engine -#### Nextflow Installation: - - Version: [e.g. 0.31.0] +- Engine: +- version: +- Image tag: -#### Container engine: - - Engine: [e.g. Conda, Docker or Singularity] - - version: [e.g. 1.0.0] - - Image tag: [e.g. nfcore/coproid:1.0.0] +## Additional context -#### Additional context Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index 1f025b7..7562b72 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -1,16 +1,24 @@ +# nf-core/coproid feature request + Hi there! -Thanks for suggesting a new feature for the pipeline! Please delete this text and anything that's not relevant from the template below: +Thanks for suggesting a new feature for the pipeline! +Please delete this text and anything that's not relevant from the template below: + +## Is your feature request related to a problem? Please describe -#### Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. + Ex. I'm always frustrated when [...] -#### Describe the solution you'd like +## Describe the solution you'd like + A clear and concise description of what you want to happen. -#### Describe alternatives you've considered +## Describe alternatives you've considered + A clear and concise description of any alternative solutions or features you've considered. -#### Additional context +## Additional context + Add any other context about the feature request here. diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 8af6145..b2a4354 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,15 +1,19 @@ -Many thanks to contributing to nf-core/coproid! +# nf-core/coproid pull request -Please fill in the appropriate checklist below (delete whatever is not relevant). These are the most common things requested on pull requests (PRs). +Many thanks for contributing to nf-core/coproid! + +Please fill in the appropriate checklist below (delete whatever is not relevant). +These are the most common things requested on pull requests (PRs). ## PR checklist - - [ ] This comment contains a description of changes (with reason) - - [ ] If you've fixed a bug or added code that should be tested, add tests! - - [ ] If necessary, also make a PR on the [nf-core/coproid branch on the nf-core/test-datasets repo]( https://github.com/nf-core/test-datasets/pull/new/nf-core/coproid) - - [ ] Ensure the test suite passes (`nextflow run . -profile test,docker`). - - [ ] Make sure your code lints (`nf-core lint .`). - - [ ] Documentation in `docs` is updated - - [ ] `CHANGELOG.md` is updated - - [ ] `README.md` is updated - -**Learn more about contributing:** https://github.com/nf-core/coproid/tree/master/.github/CONTRIBUTING.md + +- [ ] This comment contains a description of changes (with reason) +- [ ] If you've fixed a bug or added code that should be tested, add tests! +- [ ] If necessary, also make a PR on the [nf-core/coproid branch on the nf-core/test-datasets repo](https://github.com/nf-core/test-datasets/pull/new/nf-core/coproid) +- [ ] Ensure the test suite passes (`nextflow run . -profile test,docker`). +- [ ] Make sure your code lints (`nf-core lint .`). +- [ ] Documentation in `docs` is updated +- [ ] `CHANGELOG.md` is updated +- [ ] `README.md` is updated + +**Learn more about contributing:** [CONTRIBUTING.md](https://github.com/nf-core/coproid/tree/master/.github/CONTRIBUTING.md) \ No newline at end of file diff --git a/.github/markdownlint.yml b/.github/markdownlint.yml index e052a63..96b12a7 100644 --- a/.github/markdownlint.yml +++ b/.github/markdownlint.yml @@ -1,9 +1,5 @@ # Markdownlint configuration file default: true, line-length: false -no-multiple-blanks: 0 -blanks-around-headers: false -blanks-around-lists: false -header-increment: false no-duplicate-header: siblings_only: true diff --git a/.github/workflows/branch.yml b/.github/workflows/branch.yml new file mode 100644 index 0000000..12d26e7 --- /dev/null +++ b/.github/workflows/branch.yml @@ -0,0 +1,16 @@ +name: nf-core branch protection +# This workflow is triggered on PRs to master branch on the repository +# It fails when someone tries to make a PR against the nf-core `master` branch instead of `dev` +on: + pull_request: + branches: + - master + +jobs: + test: + runs-on: ubuntu-18.04 + steps: + # PRs are only ok if coming from an nf-core `dev` branch or a fork `patch` branch + - name: Check PRs + run: | + { [[ $(git remote get-url origin) == *nf-core/coproid ]] && [[ ${GITHUB_HEAD_REF} = "dev" ]]; } || [[ ${GITHUB_HEAD_REF} == "patch" ]] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..a68f2f5 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,30 @@ +name: nf-core CI +# This workflow is triggered on pushes and PRs to the repository. +# It runs the pipeline with the minimal test dataset to check that it completes without any syntax errors +on: [push, pull_request] + +jobs: + test: + env: + NXF_VER: ${{ matrix.nxf_ver }} + NXF_ANSI_LOG: false + runs-on: ubuntu-latest + strategy: + matrix: + # Nextflow versions: check pipeline minimum and current latest + nxf_ver: ['19.10.0', ''] + steps: + - uses: actions/checkout@v2 + - name: Install Nextflow + run: | + wget -qO- get.nextflow.io | bash + sudo mv nextflow /usr/local/bin/ + - name: Pull docker image + run: | + docker pull nfcore/coproid:dev + docker tag nfcore/coproid:dev nfcore/coproid:dev + - name: Run pipeline with test data + run: | + # TODO nf-core: You can customise CI pipeline run tests as required + # (eg. adding multiple test runs with different parameters) + nextflow run ${GITHUB_WORKSPACE} -profile test,docker diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml new file mode 100644 index 0000000..1e0827a --- /dev/null +++ b/.github/workflows/linting.yml @@ -0,0 +1,50 @@ +name: nf-core linting +# This workflow is triggered on pushes and PRs to the repository. +# It runs the `nf-core lint` and markdown lint tests to ensure that the code meets the nf-core guidelines +on: + push: + pull_request: + release: + types: [published] + +jobs: + Markdown: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: actions/setup-node@v1 + with: + node-version: '10' + - name: Install markdownlint + run: npm install -g markdownlint-cli + - name: Run Markdownlint + run: markdownlint ${GITHUB_WORKSPACE} -c ${GITHUB_WORKSPACE}/.github/markdownlint.yml + YAML: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + - uses: actions/setup-node@v1 + with: + node-version: '10' + - name: Install yaml-lint + run: npm install -g yaml-lint + - name: Run yaml-lint + run: yamllint $(find ${GITHUB_WORKSPACE} -type f -name "*.yml") + nf-core: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Install Nextflow + run: | + wget -qO- get.nextflow.io | bash + sudo mv nextflow /usr/local/bin/ + - uses: actions/setup-python@v1 + with: + python-version: '3.6' + architecture: 'x64' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install nf-core + - name: Run nf-core lint + run: nf-core lint ${GITHUB_WORKSPACE} diff --git a/.gitignore b/.gitignore index 5b54e3e..6354f37 100644 --- a/.gitignore +++ b/.gitignore @@ -3,5 +3,6 @@ work/ data/ results/ .DS_Store -tests/test_data +tests/ +testing/ *.pyc diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 74e247f..0000000 --- a/.travis.yml +++ /dev/null @@ -1,42 +0,0 @@ -sudo: required -language: python -jdk: openjdk8 -services: docker -python: '3.6' -cache: pip -matrix: - fast_finish: true - -before_install: - # PRs to master are only ok if coming from dev branch - - '[ $TRAVIS_PULL_REQUEST = "false" ] || [ $TRAVIS_BRANCH != "master" ] || ([ $TRAVIS_PULL_REQUEST_SLUG = $TRAVIS_REPO_SLUG ] && [ $TRAVIS_PULL_REQUEST_BRANCH = "dev" ])' - # Pull the docker image first so the test doesn't wait for this - - docker pull nfcore/coproid:dev - # Fake the tag locally so that the pipeline runs properly - # Looks weird when this is :dev to :dev, but makes sense when testing code for a release (:dev to :1.0.1) - - docker tag nfcore/coproid:dev nfcore/coproid:dev - -install: - # Install Nextflow - - mkdir /tmp/nextflow && cd /tmp/nextflow - - wget -qO- get.nextflow.io | bash - - sudo ln -s /tmp/nextflow/nextflow /usr/local/bin/nextflow - # Install nf-core/tools - - pip install --upgrade pip - - pip install nf-core - # Reset - - mkdir ${TRAVIS_BUILD_DIR}/tests && cd ${TRAVIS_BUILD_DIR}/tests - # Install markdownlint-cli - - sudo apt-get install npm && npm install -g markdownlint-cli - -env: - - NXF_VER='0.32.0' # Specify a minimum NF version that should be tested and work - - NXF_VER='' # Plus: get the latest NF version and check that it works - -script: - # Lint the pipeline code - - nf-core lint ${TRAVIS_BUILD_DIR} - # Lint the documentation - - markdownlint ${TRAVIS_BUILD_DIR} -c ${TRAVIS_BUILD_DIR}/.github/markdownlint.yml - # Run the pipeline with the test profile - - nextflow run ${TRAVIS_BUILD_DIR} -profile test,docker diff --git a/CHANGELOG.md b/CHANGELOG.md index b4dde96..1ccfc79 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,4 +1,16 @@ # nf-core/coproid: Changelog -## v1.0dev - [date] +The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) +and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). + +## v1.1dev - [date] + Initial release of nf-core/coproid, created with the [nf-core](http://nf-co.re/) template. + +### `Added` + +### `Fixed` + +### `Dependencies` + +### `Deprecated` diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 09226d0..cf930c8 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -34,7 +34,7 @@ This Code of Conduct applies both within project spaces and in public spaces whe ## Enforcement -Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team on [Slack](https://nf-core-invite.herokuapp.com/). The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. +Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team on [Slack](https://nf-co.re/join/slack). The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. diff --git a/Dockerfile b/Dockerfile index 8d5161a..f422fa5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,7 +1,13 @@ -FROM nfcore/base +FROM nfcore/base:1.9 LABEL authors="Maxime Borry" \ - description="Docker image containing all requirements for nf-core/coproid pipeline" + description="Docker image containing all software requirements for the nf-core/coproid pipeline" +# Install the conda environment COPY environment.yml / RUN conda env create -f /environment.yml && conda clean -a -ENV PATH /opt/conda/envs/nf-core-coproid-1.0dev/bin:$PATH + +# Add conda installation dir to PATH (instead of doing 'conda activate') +ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH + +# Dump the details of the installed packages to a file for posterity +RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml diff --git a/README.md b/README.md index ae1e255..1180679 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,44 @@ -# nf-core/coproid +# ![nf-core/coproid](docs/images/nf-core-coproid_logo.png) **Coprolite Identification**. -[![Build Status](https://travis-ci.com/nf-core/coproid.svg?branch=master)](https://travis-ci.com/nf-core/coproid) -[![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A50.32.0-brightgreen.svg)](https://www.nextflow.io/) +[![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) +[![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) +[![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) ## Introduction + The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. +## Quick Start + +i. Install [`nextflow`](https://nf-co.re/usage/installation) + +ii. Install either [`Docker`](https://docs.docker.com/engine/installation/) or [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) for full pipeline reproducibility (please only use [`Conda`](https://conda.io/miniconda.html) as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles)) + +iii. Download the pipeline and test it on a minimal dataset with a single command + +```bash +nextflow run nf-core/coproid -profile test, +``` + +> Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use `-profile ` in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment. + +iv. Start running your own analysis! + + + +```bash +nextflow run nf-core/coproid -profile --reads '*_R{1,2}.fastq.gz' --genome GRCh37 +``` + +See [usage docs](docs/usage.md) for all of the available options when running the pipeline. ## Documentation + The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory: 1. [Installation](https://nf-co.re/usage/installation) @@ -27,4 +53,25 @@ The nf-core/coproid pipeline comes with documentation about the pipeline, found ## Credits + nf-core/coproid was originally written by Maxime Borry. + +## Contributions and Support + +If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md). + +For further information or help, don't hesitate to get in touch on [Slack](https://nfcore.slack.com/channels/coproid) (you can join with [this invite](https://nf-co.re/join/slack)). + +## Citation + + + + +You can cite the `nf-core` publication as follows: + +> **The nf-core framework for community-curated bioinformatics pipelines.** +> +> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. +> +> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x). +> ReadCube: [Full Access Link](https://rdcu.be/b1GjZ) diff --git a/assets/email_template.html b/assets/email_template.html index 58bcddd..ef5ad9a 100644 --- a/assets/email_template.html +++ b/assets/email_template.html @@ -11,6 +11,8 @@
+ +

nf-core/coproid v${version}

Run Name: $runName

diff --git a/assets/email_template.txt b/assets/email_template.txt index 62f40b2..4784394 100644 --- a/assets/email_template.txt +++ b/assets/email_template.txt @@ -1,6 +1,12 @@ -======================================== - nf-core/coproid v${version} -======================================== +---------------------------------------------------- + ,--./,-. + ___ __ __ __ ___ /,-._.--~\\ + |\\ | |__ __ / ` / \\ |__) |__ } { + | \\| | \\__, \\__/ | \\ |___ \\`-._,-`-, + `._,._,' + nf-core/coproid v${version} +---------------------------------------------------- + Run Name: $runName <% if (success){ diff --git a/assets/multiqc_config.yaml b/assets/multiqc_config.yaml index 8c07c87..d67a1a5 100644 --- a/assets/multiqc_config.yaml +++ b/assets/multiqc_config.yaml @@ -3,7 +3,9 @@ report_comment: > analysis pipeline. For information about how to interpret these results, please see the documentation. report_section_order: - nf-core/coproid-software-versions: + software_versions: order: -1000 + nf-core-coproid-summary: + order: -1001 export_plots: true diff --git a/assets/nf-core-coproid_logo.png b/assets/nf-core-coproid_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..14a260703de491080c0ad1978526893d4f34b66d GIT binary patch literal 12502 zcmcJ$Wl$X76E=#wLvUFnxVyV+aCZytu8X_7CTK{I1m9qbFCN@oHn;`1%kO``+;6w) zt$M4@NKMc5>6t!#>OB4Q#D7v(z(gZOgMop;R8o}Hf`Nfedq1y3L3-bpuri^)A5h&D z4ZUDsum=9yV6$1U$Y5XqFiNsgI{rnc#X)%{axH(ubk}Ej4psXE2XMTs;wffPb?`=9elik({35)7x{4JT)z!7;RkwQ256;e(|(-x3w*o#mIk%D`QkH z4p7m+7SW2Q8!9>3GTFM3T=_6R;@54sg}c>AR0Q&RxZ20eZ6S1SLi|J^w_I${?oKVT zvU(t5{dTe?kE|eMvZ7flZt^OTfkg=8=C;{dXGo(!zw8YA{;Ix2>dQoC{+2J(SfOyL zNROD$SmS5_%p#_S@+66|B(7Yse`{W9Y3=+vK*of!xp*S_t|JR*`-i^>uJG(9?!88f zAfkL{Lf>!XyeS+8&tq4e)X)+o$X!IZPcyDmERBHnmg_fB|>wH8_)-apRyq|wYcRP|zo 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HcmV?d00001 diff --git a/assets/sendmail_template.txt b/assets/sendmail_template.txt index 2d67122..08d1f2b 100644 --- a/assets/sendmail_template.txt +++ b/assets/sendmail_template.txt @@ -8,6 +8,23 @@ Content-Type: text/html; charset=utf-8 $email_html +--nfcoremimeboundary +Content-Type: image/png;name="nf-core-coproid_logo.png" +Content-Transfer-Encoding: base64 +Content-ID: +Content-Disposition: inline; filename="nf-core-coproid_logo.png" + +<% out << new File("$baseDir/assets/nf-core-coproid_logo.png"). + bytes. + encodeBase64(). + toString(). + tokenize( '\n' )*. + toList()*. + collate( 76 )*. + collect { it.join() }. + flatten(). + join( '\n' ) %> + <% if (mqcFile){ def mqcFileObj = new File("$mqcFile") diff --git a/bin/markdown_to_html.py b/bin/markdown_to_html.py new file mode 100755 index 0000000..57cc426 --- /dev/null +++ b/bin/markdown_to_html.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python +from __future__ import print_function +import argparse +import markdown +import os +import sys + +def convert_markdown(in_fn): + input_md = open(in_fn, mode="r", encoding="utf-8").read() + html = markdown.markdown( + "[TOC]\n" + input_md, + extensions = [ + 'pymdownx.extra', + 'pymdownx.b64', + 'pymdownx.highlight', + 'pymdownx.emoji', + 'pymdownx.tilde', + 'toc' + ], + extension_configs = { + 'pymdownx.b64': { + 'base_path': os.path.dirname(in_fn) + }, + 'pymdownx.highlight': { + 'noclasses': True + }, + 'toc': { + 'title': 'Table of Contents' + } + } + ) + return html + +def wrap_html(contents): + header = """ + + + + + +
+ """ + footer = """ +
+ + + """ + return header + contents + footer + + +def parse_args(args=None): + parser = argparse.ArgumentParser() + parser.add_argument('mdfile', type=argparse.FileType('r'), nargs='?', + help='File to convert. Defaults to stdin.') + parser.add_argument('-o', '--out', type=argparse.FileType('w'), + default=sys.stdout, + help='Output file name. Defaults to stdout.') + return parser.parse_args(args) + +def main(args=None): + args = parse_args(args) + converted_md = convert_markdown(args.mdfile.name) + html = wrap_html(converted_md) + args.out.write(html) + +if __name__ == '__main__': + sys.exit(main()) diff --git a/bin/markdown_to_html.r b/bin/markdown_to_html.r deleted file mode 100755 index abe1335..0000000 --- a/bin/markdown_to_html.r +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env Rscript - -# Command line argument processing -args = commandArgs(trailingOnly=TRUE) -if (length(args) < 2) { - stop("Usage: markdown_to_html.r ", call.=FALSE) -} -markdown_fn <- args[1] -output_fn <- args[2] - -# Load / install packages -if (!require("markdown")) { - install.packages("markdown", dependencies=TRUE, repos='http://cloud.r-project.org/') - library("markdown") -} - -base_css_fn <- getOption("markdown.HTML.stylesheet") -base_css <- readChar(base_css_fn, file.info(base_css_fn)$size) -custom_css <- paste(base_css, " -body { - padding: 3em; - margin-right: 350px; - max-width: 100%; -} -#toc { - position: fixed; - right: 20px; - width: 300px; - padding-top: 20px; - overflow: scroll; - height: calc(100% - 3em - 20px); -} -#toc_header { - font-size: 1.8em; - font-weight: bold; -} -#toc > ul { - padding-left: 0; - list-style-type: none; -} -#toc > ul ul { padding-left: 20px; } -#toc > ul > li > a { display: none; } -img { max-width: 800px; } -") - -markdownToHTML( - file = markdown_fn, - output = output_fn, - stylesheet = custom_css, - options = c('toc', 'base64_images', 'highlight_code') -) diff --git a/bin/scrape_software_versions.py b/bin/scrape_software_versions.py index c4d6726..198c91f 100755 --- a/bin/scrape_software_versions.py +++ b/bin/scrape_software_versions.py @@ -18,14 +18,17 @@ # Search each file using its regex for k, v in regexes.items(): - with open(v[0]) as x: - versions = x.read() - match = re.search(v[1], versions) - if match: - results[k] = "v{}".format(match.group(1)) + try: + with open(v[0]) as x: + versions = x.read() + match = re.search(v[1], versions) + if match: + results[k] = "v{}".format(match.group(1)) + except IOError: + results[k] = False # Remove software set to false in results -for k in results: +for k in list(results): if not results[k]: del(results[k]) diff --git a/conf/awsbatch.config b/conf/awsbatch.config deleted file mode 100644 index 14af586..0000000 --- a/conf/awsbatch.config +++ /dev/null @@ -1,18 +0,0 @@ -/* - * ------------------------------------------------- - * Nextflow config file for running on AWS batch - * ------------------------------------------------- - * Base config needed for running with -profile awsbatch - */ -params { - config_profile_name = 'AWSBATCH' - config_profile_description = 'AWSBATCH Cloud Profile' - config_profile_contact = 'Alexander Peltzer (@apeltzer)' - config_profile_url = 'https://aws.amazon.com/de/batch/' -} - -aws.region = params.awsregion -process.executor = 'awsbatch' -process.queue = params.awsqueue -executor.awscli = '/home/ec2-user/miniconda/bin/aws' -params.tracedir = './' diff --git a/conf/base.config b/conf/base.config index 07d65e8..93f74bf 100644 --- a/conf/base.config +++ b/conf/base.config @@ -13,22 +13,39 @@ process { // TODO nf-core: Check the defaults for all processes cpus = { check_max( 1 * task.attempt, 'cpus' ) } - memory = { check_max( 8.GB * task.attempt, 'memory' ) } - time = { check_max( 2.h * task.attempt, 'time' ) } + memory = { check_max( 7.GB * task.attempt, 'memory' ) } + time = { check_max( 4.h * task.attempt, 'time' ) } errorStrategy = { task.exitStatus in [143,137,104,134,139] ? 'retry' : 'finish' } maxRetries = 1 maxErrors = '-1' // Process-specific resource requirements + // NOTE - Only one of the labels below are used in the fastqc process in the main script. + // If possible, it would be nice to keep the same label naming convention when + // adding in your processes. // TODO nf-core: Customise requirements for specific processes. // See https://www.nextflow.io/docs/latest/config.html#config-process-selectors -} - -params { - // Defaults only, expecting to be overwritten - max_memory = 128.GB - max_cpus = 16 - max_time = 240.h - igenomes_base = 's3://ngi-igenomes/igenomes/' + withLabel:process_low { + cpus = { check_max( 2 * task.attempt, 'cpus' ) } + memory = { check_max( 14.GB * task.attempt, 'memory' ) } + time = { check_max( 6.h * task.attempt, 'time' ) } + } + withLabel:process_medium { + cpus = { check_max( 6 * task.attempt, 'cpus' ) } + memory = { check_max( 42.GB * task.attempt, 'memory' ) } + time = { check_max( 8.h * task.attempt, 'time' ) } + } + withLabel:process_high { + cpus = { check_max( 12 * task.attempt, 'cpus' ) } + memory = { check_max( 84.GB * task.attempt, 'memory' ) } + time = { check_max( 10.h * task.attempt, 'time' ) } + } + withLabel:process_long { + time = { check_max( 20.h * task.attempt, 'time' ) } + } + withName:get_software_versions { + cache = false + } + } diff --git a/conf/igenomes.config b/conf/igenomes.config index d19e61f..2de9242 100644 --- a/conf/igenomes.config +++ b/conf/igenomes.config @@ -9,139 +9,412 @@ params { // illumina iGenomes reference file paths - // TODO nf-core: Add new reference types and strip out those that are not needed genomes { 'GRCh37' { - bed12 = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Homo_sapiens/Ensembl/GRCh37/Annotation/README.txt" + mito_name = "MT" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/GRCh37-blacklist.bed" + } + 'GRCh38' { + fasta = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/NCBI/GRCh38/Annotation/Genes/genes.bed" + mito_name = "chrM" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/hg38-blacklist.bed" } 'GRCm38' { - bed12 = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Mus_musculus/Ensembl/GRCm38/Annotation/README.txt" + mito_name = "MT" + macs_gsize = "1.87e9" + blacklist = "${baseDir}/assets/blacklists/GRCm38-blacklist.bed" } 'TAIR10' { - bed12 = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Arabidopsis_thaliana/Ensembl/TAIR10/Annotation/README.txt" + mito_name = "Mt" } 'EB2' { - bed12 = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Bacillus_subtilis_168/Ensembl/EB2/Annotation/README.txt" } 'UMD3.1' { - bed12 = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Bos_taurus/Ensembl/UMD3.1/Annotation/README.txt" + mito_name = "MT" } 'WBcel235' { - bed12 = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Caenorhabditis_elegans/Ensembl/WBcel235/Annotation/Genes/genes.bed" + mito_name = "MtDNA" + macs_gsize = "9e7" } 'CanFam3.1' { - bed12 = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Canis_familiaris/Ensembl/CanFam3.1/Annotation/README.txt" + mito_name = "MT" } 'GRCz10' { - bed12 = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Danio_rerio/Ensembl/GRCz10/Annotation/Genes/genes.bed" + mito_name = "MT" } 'BDGP6' { - bed12 = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Drosophila_melanogaster/Ensembl/BDGP6/Annotation/Genes/genes.bed" + mito_name = "M" + macs_gsize = "1.2e8" } 'EquCab2' { - bed12 = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Equus_caballus/Ensembl/EquCab2/Annotation/README.txt" + mito_name = "MT" } 'EB1' { - bed12 = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Escherichia_coli_K_12_DH10B/Ensembl/EB1/Annotation/README.txt" } 'Galgal4' { - bed12 = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Gallus_gallus/Ensembl/Galgal4/Annotation/Genes/genes.bed" + mito_name = "MT" } 'Gm01' { - bed12 = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Glycine_max/Ensembl/Gm01/Annotation/README.txt" } 'Mmul_1' { - bed12 = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Macaca_mulatta/Ensembl/Mmul_1/Annotation/README.txt" + mito_name = "MT" } 'IRGSP-1.0' { - bed12 = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Oryza_sativa_japonica/Ensembl/IRGSP-1.0/Annotation/Genes/genes.bed" + mito_name = "Mt" } 'CHIMP2.1.4' { - bed12 = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Pan_troglodytes/Ensembl/CHIMP2.1.4/Annotation/README.txt" + mito_name = "MT" } 'Rnor_6.0' { - bed12 = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Rattus_norvegicus/Ensembl/Rnor_6.0/Annotation/Genes/genes.bed" + mito_name = "MT" } 'R64-1-1' { - bed12 = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Saccharomyces_cerevisiae/Ensembl/R64-1-1/Annotation/Genes/genes.bed" + mito_name = "MT" + macs_gsize = "1.2e7" } 'EF2' { - bed12 = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Schizosaccharomyces_pombe/Ensembl/EF2/Annotation/README.txt" + mito_name = "MT" + macs_gsize = "1.21e7" } 'Sbi1' { - bed12 = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Sorghum_bicolor/Ensembl/Sbi1/Annotation/README.txt" } 'Sscrofa10.2' { - bed12 = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Sus_scrofa/Ensembl/Sscrofa10.2/Annotation/README.txt" + mito_name = "MT" } 'AGPv3' { - bed12 = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.bed" - fasta = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/WholeGenomeFasta/genome.fa" - gtf = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.gtf" - star = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/STARIndex/" + fasta = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Zea_mays/Ensembl/AGPv3/Annotation/Genes/genes.bed" + mito_name = "Mt" + } + 'hg38' { + fasta = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.bed" + mito_name = "chrM" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/hg38-blacklist.bed" + } + 'hg19' { + fasta = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Homo_sapiens/UCSC/hg19/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "2.7e9" + blacklist = "${baseDir}/assets/blacklists/hg19-blacklist.bed" + } + 'mm10' { + fasta = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Mus_musculus/UCSC/mm10/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "1.87e9" + blacklist = "${baseDir}/assets/blacklists/mm10-blacklist.bed" + } + 'bosTau8' { + fasta = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Bos_taurus/UCSC/bosTau8/Annotation/Genes/genes.bed" + mito_name = "chrM" + } + 'ce10' { + fasta = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Caenorhabditis_elegans/UCSC/ce10/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "9e7" + } + 'canFam3' { + fasta = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Canis_familiaris/UCSC/canFam3/Annotation/README.txt" + mito_name = "chrM" + } + 'danRer10' { + fasta = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Danio_rerio/UCSC/danRer10/Annotation/Genes/genes.bed" + mito_name = "chrM" + } + 'dm6' { + fasta = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Drosophila_melanogaster/UCSC/dm6/Annotation/Genes/genes.bed" + mito_name = "chrM" + macs_gsize = "1.2e8" + } + 'equCab2' { + fasta = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Equus_caballus/UCSC/equCab2/Annotation/README.txt" + mito_name = "chrM" + } + 'galGal4' { + fasta = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Gallus_gallus/UCSC/galGal4/Annotation/README.txt" + mito_name = "chrM" + } + 'panTro4' { + fasta = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Pan_troglodytes/UCSC/panTro4/Annotation/README.txt" + mito_name = "chrM" + } + 'rn6' { + fasta = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Rattus_norvegicus/UCSC/rn6/Annotation/Genes/genes.bed" + mito_name = "chrM" + } + 'sacCer3' { + fasta = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Sequence/BismarkIndex/" + readme = "${params.igenomes_base}/Saccharomyces_cerevisiae/UCSC/sacCer3/Annotation/README.txt" + mito_name = "chrM" + macs_gsize = "1.2e7" + } + 'susScr3' { + fasta = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/WholeGenomeFasta/genome.fa" + bwa = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/BWAIndex/genome.fa" + bowtie2 = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/Bowtie2Index/" + star = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/STARIndex/" + bismark = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Sequence/BismarkIndex/" + gtf = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/Genes/genes.gtf" + bed12 = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/Genes/genes.bed" + readme = "${params.igenomes_base}/Sus_scrofa/UCSC/susScr3/Annotation/README.txt" + mito_name = "chrM" } } } diff --git a/conf/test.config b/conf/test.config index ecf5333..870f3bc 100644 --- a/conf/test.config +++ b/conf/test.config @@ -4,20 +4,21 @@ * ------------------------------------------------- * Defines bundled input files and everything required * to run a fast and simple test. Use as follows: - * nextflow run nf-core/coproid -profile test + * nextflow run nf-core/coproid -profile test, */ params { config_profile_name = 'Test profile' config_profile_description = 'Minimal test dataset to check pipeline function' - // Limit resources so that this can run on Travis + // Limit resources so that this can run on GitHub Actions max_cpus = 2 max_memory = 6.GB max_time = 48.h + // Input data // TODO nf-core: Specify the paths to your test data on nf-core/test-datasets // TODO nf-core: Give any required params for the test so that command line flags are not needed - singleEnd = false + single_end = false readPaths = [ ['Testdata', ['https://github.com/nf-core/test-datasets/raw/exoseq/testdata/Testdata_R1.tiny.fastq.gz', 'https://github.com/nf-core/test-datasets/raw/exoseq/testdata/Testdata_R2.tiny.fastq.gz']], ['SRR389222', ['https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub1.fastq.gz', 'https://github.com/nf-core/test-datasets/raw/methylseq/testdata/SRR389222_sub2.fastq.gz']] diff --git a/docs/images/nf-core-coproid_logo.png b/docs/images/nf-core-coproid_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..571c71d95ac979cd48e6d87dd37c90189fb2684c GIT binary patch literal 21191 zcmdqJ^;2Bi^970%Jh(%U1b26b;O;KL26xv0!4upygS$I}C%6voE`z&1?!BMyPw)K& zuWF`F)zq2U-MzY3ckO-FM5-vspdk|>LqS2I$$phogMxz5fr5gD`+)HN$#t*X*!u;^ z>8q|Q6cl>@KX2${26SR5s83L`l42TOStp%=eg>M^Z*PKD4qpyA6(x{>CZ+k`)sVF+ ze*E~6ECnY2@dGEYFyu!FGyQSA;Wsp6;i~e0=U_&1DR}oeDf&x!sZ=RZNl8&rkeVD_ zV*@ZLqsS&h=?llc-y5$hM~@!3Hld#=M!3_i})B&FlEHONDye$E)Y#Ftdqj8M?C z$6T3DX4Y3p6Qqz&A3~r&<@n@9vWLv@G_ch*HE?OEazH(3`^qsw^G6&zI4$TvRwuwQ z2(+i4^Qv!XuS`T)^G_5_iLNrVyRcTT3{)l+t)R#oofz|&^1s%YXv(tbpvall($1&B zb|p^iGe7rXE2sOANMKk^qa_Od-*Adkxso_xC*N54gXDv3Qp;_w{86lJcm45}DzLSQV|RTq z1kXh~6*KF8fUw>pNR*C3zz_ynsIVZ4VhGYW9>qgBtINNWD@_LYdVXCiBft#i(mXs3 zPZdG*pTOrB!maOV^z8j>T-xmZj-BcxQkK@H=M`x8B0a$%g<0==NMX>xFnkj+0Mb}* zHDNhZr0Cw{a6}0Mps#U(M?qM!29LGnJxwu*q4aQ&qE4I6)bE?vM`w=bJrW!&mO1eH zMvLE|p%L2o6s7aEglRgqSFOoVd|F(eZ1gNnzvd zFU@kY>S|MU5=Typk(ZpeBWMF0^)82~;-CO24AIiMI7eMMc3L?PY*aAi zn1`oDSLaqC2GRVHmdq+CiGMQjoZxxI@+%kykUoM15s!r#<_Z`MX=lDw29TB<2Q%FW ze0neTGCtTe{Y}q#nC51MJX*jJ==LHt#0uvrmjK^pK z*DNkA4#wYdZz2Y@^&OQJt1!dp)f6I}?|4N*-Lsk=rgUC071gF1(REImE$8cWEY?-= z5tKFSs*}A(KzyEwJZ!aH54q_~E#~1v3SN*I$jk<(#xC0>mdmL9(+6=_7ZP9{ldn1P z`8I*QEGG&cBghONx@{I?0j*u_;gOI_@ni7Z2fxF_g`ONf`pJ0}RvHP_Pm4kWYK@S0 z*wMlVftDq+zBlSwe=<@=9UW!T^Q%~eBz|>?2t6tYjb-3z_=F<6?@adjaLAPd*lu?c zch! 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Most of the plots a ## Pipeline overview + The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps: @@ -12,6 +13,7 @@ and processes data using the following steps: * [MultiQC](#multiqc) - aggregate report, describing results of the whole pipeline ## FastQC + [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your reads. It provides information about the quality score distribution across your reads, the per base sequence content (%T/A/G/C). You get information about adapter contamination and other overrepresented sequences. For further reading and documentation see the [FastQC help](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/). @@ -25,8 +27,8 @@ For further reading and documentation see the [FastQC help](http://www.bioinform * `zips/sample_fastqc.zip` * zip file containing the FastQC report, tab-delimited data file and plot images - ## MultiQC + [MultiQC](http://multiqc.info) is a visualisation tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in within the report data directory. The pipeline has special steps which allow the software versions used to be reported in the MultiQC output for future traceability. diff --git a/docs/usage.md b/docs/usage.md index 5d58e0e..7f6c96e 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -2,8 +2,6 @@ ## Table of contents - - * [Table of contents](#table-of-contents) * [Introduction](#introduction) * [Running the pipeline](#running-the-pipeline) @@ -12,20 +10,23 @@ * [Main arguments](#main-arguments) * [`-profile`](#-profile) * [`--reads`](#--reads) - * [`--singleEnd`](#--singleend) + * [`--single_end`](#--single_end) * [Reference genomes](#reference-genomes) * [`--genome` (using iGenomes)](#--genome-using-igenomes) * [`--fasta`](#--fasta) - * [`--igenomesIgnore`](#--igenomesignore) + * [`--igenomes_ignore`](#--igenomes_ignore) * [Job resources](#job-resources) * [Automatic resubmission](#automatic-resubmission) * [Custom resource requests](#custom-resource-requests) * [AWS Batch specific parameters](#aws-batch-specific-parameters) * [`--awsqueue`](#--awsqueue) * [`--awsregion`](#--awsregion) + * [`--awscli`](#--awscli) * [Other command line parameters](#other-command-line-parameters) * [`--outdir`](#--outdir) * [`--email`](#--email) + * [`--email_on_fail`](#--email_on_fail) + * [`--max_multiqc_email_size`](#--max_multiqc_email_size) * [`-name`](#-name) * [`-resume`](#-resume) * [`-c`](#-c) @@ -37,10 +38,9 @@ * [`--plaintext_email`](#--plaintext_email) * [`--monochrome_logs`](#--monochrome_logs) * [`--multiqc_config`](#--multiqc_config) - - ## Introduction + Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through `screen` / `tmux` or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler. It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in `~/.bashrc` or `~./bash_profile`): @@ -52,6 +52,7 @@ NXF_OPTS='-Xms1g -Xmx4g' ## Running the pipeline + The typical command for running the pipeline is as follows: ```bash @@ -70,6 +71,7 @@ results # Finished results (configurable, see below) ``` ### Updating the pipeline + When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline: ```bash @@ -77,31 +79,40 @@ nextflow pull nf-core/coproid ``` ### Reproducibility + It's a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since. First, go to the [nf-core/coproid releases page](https://github.com/nf-core/coproid/releases) and find the latest version number - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. - ## Main arguments ### `-profile` -Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. Note that multiple profiles can be loaded, for example: `-profile docker` - the order of arguments is important! -If `-profile` is not specified at all the pipeline will be run locally and expects all software to be installed and available on the `PATH`. +Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. + +Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Conda) - see below. + +> We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported. + +The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation). + +Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important! +They are loaded in sequence, so later profiles can overwrite earlier profiles. + +If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended. -* `awsbatch` - * A generic configuration profile to be used with AWS Batch. -* `conda` - * A generic configuration profile to be used with [conda](https://conda.io/docs/) - * Pulls most software from [Bioconda](https://bioconda.github.io/) * `docker` * A generic configuration profile to be used with [Docker](http://docker.com/) * Pulls software from dockerhub: [`nfcore/coproid`](http://hub.docker.com/r/nfcore/coproid/) * `singularity` * A generic configuration profile to be used with [Singularity](http://singularity.lbl.gov/) * Pulls software from DockerHub: [`nfcore/coproid`](http://hub.docker.com/r/nfcore/coproid/) +* `conda` + * Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker or Singularity. + * A generic configuration profile to be used with [Conda](https://conda.io/docs/) + * Pulls most software from [Bioconda](https://bioconda.github.io/) * `test` * A profile with a complete configuration for automated testing * Includes links to test data so needs no other parameters @@ -109,6 +120,7 @@ If `-profile` is not specified at all the pipeline will be run locally and expec ### `--reads` + Use this to specify the location of your input FastQ files. For example: ```bash @@ -123,21 +135,22 @@ Please note the following requirements: If left unspecified, a default pattern is used: `data/*{1,2}.fastq.gz` -### `--singleEnd` -By default, the pipeline expects paired-end data. If you have single-end data, you need to specify `--singleEnd` on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for `--reads`. For example: +### `--single_end` + +By default, the pipeline expects paired-end data. If you have single-end data, you need to specify `--single_end` on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for `--reads`. For example: ```bash ---singleEnd --reads '*.fastq' +--single_end --reads '*.fastq' ``` It is not possible to run a mixture of single-end and paired-end files in one run. - ## Reference genomes The pipeline config files come bundled with paths to the illumina iGenomes reference index files. If running with docker or AWS, the configuration is set up to use the [AWS-iGenomes](https://ewels.github.io/AWS-iGenomes/) resource. ### `--genome` (using iGenomes) + There are 31 different species supported in the iGenomes references. To run the pipeline, you must specify which to use with the `--genome` flag. You can find the keys to specify the genomes in the [iGenomes config file](../conf/igenomes.config). Common genomes that are supported are: @@ -171,33 +184,48 @@ params { ``` + ### `--fasta` + If you prefer, you can specify the full path to your reference genome when you run the pipeline: ```bash --fasta '[path to Fasta reference]' ``` -### `--igenomesIgnore` +### `--igenomes_ignore` + Do not load `igenomes.config` when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in `igenomes.config`. ## Job resources + ### Automatic resubmission + Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with an error code of `143` (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped. ### Custom resource requests + Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at [`nf-core/configs`](https://github.com/nf-core/configs/tree/master/conf) for examples. If you are likely to be running `nf-core` pipelines regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter (see definition below). You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile. -If you have any questions or issues please send us a message on [Slack](https://nf-core-invite.herokuapp.com/). +If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack). ## AWS Batch specific parameters -Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use the `-awsbatch` profile and then specify all of the following parameters. + +Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use [`-profile awsbatch`](https://github.com/nf-core/configs/blob/master/conf/awsbatch.config) and then specify all of the following parameters. + ### `--awsqueue` + The JobQueue that you intend to use on AWS Batch. + ### `--awsregion` -The AWS region to run your job in. Default is set to `eu-west-1` but can be adjusted to your needs. + +The AWS region in which to run your job. Default is set to `eu-west-1` but can be adjusted to your needs. + +### `--awscli` + +The [AWS CLI](https://www.nextflow.io/docs/latest/awscloud.html#aws-cli-installation) path in your custom AMI. Default: `/home/ec2-user/miniconda/bin/aws`. Please make sure to also set the `-w/--work-dir` and `--outdir` parameters to a S3 storage bucket of your choice - you'll get an error message notifying you if you didn't. @@ -206,12 +234,23 @@ Please make sure to also set the `-w/--work-dir` and `--outdir` parameters to a ### `--outdir` + The output directory where the results will be saved. ### `--email` + Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (`~/.nextflow/config`) then you don't need to specify this on the command line for every run. +### `--email_on_fail` + +This works exactly as with `--email`, except emails are only sent if the workflow is not successful. + +### `--max_multiqc_email_size` + +Threshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB). + ### `-name` + Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic. This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always). @@ -219,6 +258,7 @@ This is used in the MultiQC report (if not default) and in the summary HTML / e- **NB:** Single hyphen (core Nextflow option) ### `-resume` + Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names. @@ -226,6 +266,7 @@ You can also supply a run name to resume a specific run: `-resume [run-name]`. U **NB:** Single hyphen (core Nextflow option) ### `-c` + Specify the path to a specific config file (this is a core NextFlow command). **NB:** Single hyphen (core Nextflow option) @@ -233,7 +274,8 @@ Specify the path to a specific config file (this is a core NextFlow command). Note - you can use this to override pipeline defaults. ### `--custom_config_version` -Provide git commit id for custom Institutional configs hosted at `nf-core/configs`. This was implemented for reproducibility purposes. Default is set to `master`. + +Provide git commit id for custom Institutional configs hosted at `nf-core/configs`. This was implemented for reproducibility purposes. Default: `master`. ```bash ## Download and use config file with following git commid id @@ -241,6 +283,7 @@ Provide git commit id for custom Institutional configs hosted at `nf-core/config ``` ### `--custom_config_base` + If you're running offline, nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell nextflow where to find them with the @@ -261,22 +304,28 @@ nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs > files + singularity containers + institutional configs in one go for you, to make this process easier. ### `--max_memory` + Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. `--max_memory '8.GB'` ### `--max_time` + Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. `--max_time '2.h'` ### `--max_cpus` + Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. `--max_cpus 1` ### `--plaintext_email` + Set to receive plain-text e-mails instead of HTML formatted. ### `--monochrome_logs` + Set to disable colourful command line output and live life in monochrome. ### `--multiqc_config` + Specify a path to a custom MultiQC configuration file. diff --git a/environment.yml b/environment.yml index 18d220d..3dae3e9 100644 --- a/environment.yml +++ b/environment.yml @@ -1,11 +1,15 @@ # You can use this file to create a conda environment for this pipeline: # conda env create -f environment.yml -name: nf-core-coproid-1.0dev +name: nf-core-coproid-1.1dev channels: - conda-forge - bioconda - defaults dependencies: + - conda-forge::python=3.7.3 + - conda-forge::markdown=3.1.1 + - conda-forge::pymdown-extensions=6.0 + - conda-forge::pygments=2.5.2 # TODO nf-core: Add required software dependencies here - - fastqc=0.11.8 - - multiqc=1.7 + - bioconda::fastqc=0.11.8 + - bioconda::multiqc=1.7 diff --git a/main.nf b/main.nf index c009ef3..bed5c99 100644 --- a/main.nf +++ b/main.nf @@ -9,7 +9,6 @@ ---------------------------------------------------------------------------------------- */ - def helpMessage() { // TODO nf-core: Add to this help message with new command line parameters log.info nfcoreHeader() @@ -22,39 +21,41 @@ def helpMessage() { nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' -profile docker Mandatory arguments: - --reads Path to input data (must be surrounded with quotes) - -profile Configuration profile to use. Can use multiple (comma separated) - Available: conda, docker, singularity, awsbatch, test and more. + --reads [file] Path to input data (must be surrounded with quotes) + -profile [str] Configuration profile to use. Can use multiple (comma separated) + Available: conda, docker, singularity, test, awsbatch, and more Options: - --genome Name of iGenomes reference - --singleEnd Specifies that the input is single end reads + --genome [str] Name of iGenomes reference + --single_end [bool] Specifies that the input is single-end reads - References If not specified in the configuration file or you wish to overwrite any of the references. - --fasta Path to Fasta reference + References If not specified in the configuration file or you wish to overwrite any of the references + --fasta [file] Path to fasta reference Other options: - --outdir The output directory where the results will be saved - --email Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits - --maxMultiqcEmailFileSize Theshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB) - -name Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic. + --outdir [file] The output directory where the results will be saved + --email [email] Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits + --email_on_fail [email] Same as --email, except only send mail if the workflow is not successful + --max_multiqc_email_size [str] Theshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB) + -name [str] Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic AWSBatch options: - --awsqueue The AWSBatch JobQueue that needs to be set when running on AWSBatch - --awsregion The AWS Region for your AWS Batch job to run on + --awsqueue [str] The AWSBatch JobQueue that needs to be set when running on AWSBatch + --awsregion [str] The AWS Region for your AWS Batch job to run on + --awscli [str] Path to the AWS CLI tool """.stripIndent() } -/* - * SET UP CONFIGURATION VARIABLES - */ - -// Show help emssage -if (params.help){ +// Show help message +if (params.help) { helpMessage() exit 0 } +/* + * SET UP CONFIGURATION VARIABLES + */ + // Check if genome exists in the config file if (params.genomes && params.genome && !params.genomes.containsKey(params.genome)) { exit 1, "The provided genome '${params.genome}' is not available in the iGenomes file. Currently the available genomes are ${params.genomes.keySet().join(", ")}" @@ -62,103 +63,101 @@ if (params.genomes && params.genome && !params.genomes.containsKey(params.genome // TODO nf-core: Add any reference files that are needed // Configurable reference genomes -fasta = params.genome ? params.genomes[ params.genome ].fasta ?: false : false -if ( params.fasta ){ - fasta = file(params.fasta) - if( !fasta.exists() ) exit 1, "Fasta file not found: ${params.fasta}" -} // // NOTE - THIS IS NOT USED IN THIS PIPELINE, EXAMPLE ONLY -// If you want to use the above in a process, define the following: +// If you want to use the channel below in a process, define the following: // input: -// file fasta from fasta +// file fasta from ch_fasta // - +params.fasta = params.genome ? params.genomes[ params.genome ].fasta ?: false : false +if (params.fasta) { ch_fasta = file(params.fasta, checkIfExists: true) } // Has the run name been specified by the user? // this has the bonus effect of catching both -name and --name custom_runName = params.name -if( !(workflow.runName ==~ /[a-z]+_[a-z]+/) ){ - custom_runName = workflow.runName +if (!(workflow.runName ==~ /[a-z]+_[a-z]+/)) { + custom_runName = workflow.runName } - -if( workflow.profile == 'awsbatch') { - // AWSBatch sanity checking - if (!params.awsqueue || !params.awsregion) exit 1, "Specify correct --awsqueue and --awsregion parameters on AWSBatch!" - // Check outdir paths to be S3 buckets if running on AWSBatch - // related: https://github.com/nextflow-io/nextflow/issues/813 - if (!params.outdir.startsWith('s3:')) exit 1, "Outdir not on S3 - specify S3 Bucket to run on AWSBatch!" - // Prevent trace files to be stored on S3 since S3 does not support rolling files. - if (workflow.tracedir.startsWith('s3:')) exit 1, "Specify a local tracedir or run without trace! S3 cannot be used for tracefiles." +if (workflow.profile.contains('awsbatch')) { + // AWSBatch sanity checking + if (!params.awsqueue || !params.awsregion) exit 1, "Specify correct --awsqueue and --awsregion parameters on AWSBatch!" + // Check outdir paths to be S3 buckets if running on AWSBatch + // related: https://github.com/nextflow-io/nextflow/issues/813 + if (!params.outdir.startsWith('s3:')) exit 1, "Outdir not on S3 - specify S3 Bucket to run on AWSBatch!" + // Prevent trace files to be stored on S3 since S3 does not support rolling files. + if (params.tracedir.startsWith('s3:')) exit 1, "Specify a local tracedir or run without trace! S3 cannot be used for tracefiles." } // Stage config files -ch_multiqc_config = Channel.fromPath(params.multiqc_config) -ch_output_docs = Channel.fromPath("$baseDir/docs/output.md") +ch_multiqc_config = file("$baseDir/assets/multiqc_config.yaml", checkIfExists: true) +ch_multiqc_custom_config = params.multiqc_config ? Channel.fromPath(params.multiqc_config, checkIfExists: true) : Channel.empty() +ch_output_docs = file("$baseDir/docs/output.md", checkIfExists: true) /* * Create a channel for input read files */ -if(params.readPaths){ - if(params.singleEnd){ +if (params.readPaths) { + if (params.single_end) { Channel .from(params.readPaths) - .map { row -> [ row[0], [file(row[1][0])]] } + .map { row -> [ row[0], [ file(row[1][0], checkIfExists: true) ] ] } .ifEmpty { exit 1, "params.readPaths was empty - no input files supplied" } - .into { read_files_fastqc; read_files_trimming } + .into { ch_read_files_fastqc; ch_read_files_trimming } } else { Channel .from(params.readPaths) - .map { row -> [ row[0], [file(row[1][0]), file(row[1][1])]] } + .map { row -> [ row[0], [ file(row[1][0], checkIfExists: true), file(row[1][1], checkIfExists: true) ] ] } .ifEmpty { exit 1, "params.readPaths was empty - no input files supplied" } - .into { read_files_fastqc; read_files_trimming } + .into { ch_read_files_fastqc; ch_read_files_trimming } } } else { Channel - .fromFilePairs( params.reads, size: params.singleEnd ? 1 : 2 ) - .ifEmpty { exit 1, "Cannot find any reads matching: ${params.reads}\nNB: Path needs to be enclosed in quotes!\nIf this is single-end data, please specify --singleEnd on the command line." } - .into { read_files_fastqc; read_files_trimming } + .fromFilePairs(params.reads, size: params.single_end ? 1 : 2) + .ifEmpty { exit 1, "Cannot find any reads matching: ${params.reads}\nNB: Path needs to be enclosed in quotes!\nIf this is single-end data, please specify --single_end on the command line." } + .into { ch_read_files_fastqc; ch_read_files_trimming } } - // Header log info log.info nfcoreHeader() def summary = [:] -if(workflow.revision) summary['Pipeline Release'] = workflow.revision +if (workflow.revision) summary['Pipeline Release'] = workflow.revision summary['Run Name'] = custom_runName ?: workflow.runName // TODO nf-core: Report custom parameters here summary['Reads'] = params.reads summary['Fasta Ref'] = params.fasta -summary['Data Type'] = params.singleEnd ? 'Single-End' : 'Paired-End' +summary['Data Type'] = params.single_end ? 'Single-End' : 'Paired-End' summary['Max Resources'] = "$params.max_memory memory, $params.max_cpus cpus, $params.max_time time per job" -if(workflow.containerEngine) summary['Container'] = "$workflow.containerEngine - $workflow.container" +if (workflow.containerEngine) summary['Container'] = "$workflow.containerEngine - $workflow.container" summary['Output dir'] = params.outdir summary['Launch dir'] = workflow.launchDir summary['Working dir'] = workflow.workDir summary['Script dir'] = workflow.projectDir summary['User'] = workflow.userName -if(workflow.profile == 'awsbatch'){ - summary['AWS Region'] = params.awsregion - summary['AWS Queue'] = params.awsqueue +if (workflow.profile.contains('awsbatch')) { + summary['AWS Region'] = params.awsregion + summary['AWS Queue'] = params.awsqueue + summary['AWS CLI'] = params.awscli } summary['Config Profile'] = workflow.profile -if(params.config_profile_description) summary['Config Description'] = params.config_profile_description -if(params.config_profile_contact) summary['Config Contact'] = params.config_profile_contact -if(params.config_profile_url) summary['Config URL'] = params.config_profile_url -if(params.email) { - summary['E-mail Address'] = params.email - summary['MultiQC maxsize'] = params.maxMultiqcEmailFileSize +if (params.config_profile_description) summary['Config Description'] = params.config_profile_description +if (params.config_profile_contact) summary['Config Contact'] = params.config_profile_contact +if (params.config_profile_url) summary['Config URL'] = params.config_profile_url +if (params.email || params.email_on_fail) { + summary['E-mail Address'] = params.email + summary['E-mail on failure'] = params.email_on_fail + summary['MultiQC maxsize'] = params.max_multiqc_email_size } log.info summary.collect { k,v -> "${k.padRight(18)}: $v" }.join("\n") -log.info "\033[2m----------------------------------------------------\033[0m" +log.info "-\033[2m--------------------------------------------------\033[0m-" // Check the hostnames against configured profiles checkHostname() -def create_workflow_summary(summary) { - def yaml_file = workDir.resolve('workflow_summary_mqc.yaml') - yaml_file.text = """ +Channel.from(summary.collect{ [it.key, it.value] }) + .map { k,v -> "

$k
${v ?: 'N/A'}
" } + .reduce { a, b -> return [a, b].join("\n ") } + .map { x -> """ id: 'nf-core-coproid-summary' description: " - this information is collected when the pipeline is started." section_name: 'nf-core/coproid Workflow Summary' @@ -166,26 +165,23 @@ def create_workflow_summary(summary) { plot_type: 'html' data: |
-${summary.collect { k,v -> "
$k
${v ?: 'N/A'}
" }.join("\n")} + $x
- """.stripIndent() - - return yaml_file -} - + """.stripIndent() } + .set { ch_workflow_summary } /* * Parse software version numbers */ process get_software_versions { publishDir "${params.outdir}/pipeline_info", mode: 'copy', - saveAs: {filename -> - if (filename.indexOf(".csv") > 0) filename - else null - } + saveAs: { filename -> + if (filename.indexOf(".csv") > 0) filename + else null + } output: - file 'software_versions_mqc.yaml' into software_versions_yaml + file 'software_versions_mqc.yaml' into ch_software_versions_yaml file "software_versions.csv" script: @@ -199,30 +195,29 @@ process get_software_versions { """ } - - /* * STEP 1 - FastQC */ process fastqc { tag "$name" + label 'process_medium' publishDir "${params.outdir}/fastqc", mode: 'copy', - saveAs: {filename -> filename.indexOf(".zip") > 0 ? "zips/$filename" : "$filename"} + saveAs: { filename -> + filename.indexOf(".zip") > 0 ? "zips/$filename" : "$filename" + } input: - set val(name), file(reads) from read_files_fastqc + set val(name), file(reads) from ch_read_files_fastqc output: - file "*_fastqc.{zip,html}" into fastqc_results + file "*_fastqc.{zip,html}" into ch_fastqc_results script: """ - fastqc -q $reads + fastqc --quiet --threads $task.cpus $reads """ } - - /* * STEP 2 - MultiQC */ @@ -230,28 +225,28 @@ process multiqc { publishDir "${params.outdir}/MultiQC", mode: 'copy' input: - file multiqc_config from ch_multiqc_config + file (multiqc_config) from ch_multiqc_config + file (mqc_custom_config) from ch_multiqc_custom_config.collect().ifEmpty([]) // TODO nf-core: Add in log files from your new processes for MultiQC to find! - file ('fastqc/*') from fastqc_results.collect().ifEmpty([]) - file ('software_versions/*') from software_versions_yaml.collect() - file workflow_summary from create_workflow_summary(summary) + file ('fastqc/*') from ch_fastqc_results.collect().ifEmpty([]) + file ('software_versions/*') from ch_software_versions_yaml.collect() + file workflow_summary from ch_workflow_summary.collectFile(name: "workflow_summary_mqc.yaml") output: - file "*multiqc_report.html" into multiqc_report + file "*multiqc_report.html" into ch_multiqc_report file "*_data" file "multiqc_plots" script: rtitle = custom_runName ? "--title \"$custom_runName\"" : '' rfilename = custom_runName ? "--filename " + custom_runName.replaceAll('\\W','_').replaceAll('_+','_') + "_multiqc_report" : '' + custom_config_file = params.multiqc_config ? "--config $mqc_custom_config" : '' // TODO nf-core: Specify which MultiQC modules to use with -m for a faster run time """ - multiqc -f $rtitle $rfilename --config $multiqc_config . + multiqc -f $rtitle $rfilename $custom_config_file . """ } - - /* * STEP 3 - Output Description HTML */ @@ -266,12 +261,10 @@ process output_documentation { script: """ - markdown_to_html.r $output_docs results_description.html + markdown_to_html.py $output_docs -o results_description.html """ } - - /* * Completion e-mail notification */ @@ -279,8 +272,8 @@ workflow.onComplete { // Set up the e-mail variables def subject = "[nf-core/coproid] Successful: $workflow.runName" - if(!workflow.success){ - subject = "[nf-core/coproid] FAILED: $workflow.runName" + if (!workflow.success) { + subject = "[nf-core/coproid] FAILED: $workflow.runName" } def email_fields = [:] email_fields['version'] = workflow.manifest.version @@ -298,21 +291,20 @@ workflow.onComplete { email_fields['summary']['Date Completed'] = workflow.complete email_fields['summary']['Pipeline script file path'] = workflow.scriptFile email_fields['summary']['Pipeline script hash ID'] = workflow.scriptId - if(workflow.repository) email_fields['summary']['Pipeline repository Git URL'] = workflow.repository - if(workflow.commitId) email_fields['summary']['Pipeline repository Git Commit'] = workflow.commitId - if(workflow.revision) email_fields['summary']['Pipeline Git branch/tag'] = workflow.revision - if(workflow.container) email_fields['summary']['Docker image'] = workflow.container + if (workflow.repository) email_fields['summary']['Pipeline repository Git URL'] = workflow.repository + if (workflow.commitId) email_fields['summary']['Pipeline repository Git Commit'] = workflow.commitId + if (workflow.revision) email_fields['summary']['Pipeline Git branch/tag'] = workflow.revision email_fields['summary']['Nextflow Version'] = workflow.nextflow.version email_fields['summary']['Nextflow Build'] = workflow.nextflow.build email_fields['summary']['Nextflow Compile Timestamp'] = workflow.nextflow.timestamp - // TODO nf-core: If not using MultiQC, strip out this code (including params.maxMultiqcEmailFileSize) + // TODO nf-core: If not using MultiQC, strip out this code (including params.max_multiqc_email_size) // On success try attach the multiqc report def mqc_report = null try { if (workflow.success) { - mqc_report = multiqc_report.getVal() - if (mqc_report.getClass() == ArrayList){ + mqc_report = ch_multiqc_report.getVal() + if (mqc_report.getClass() == ArrayList) { log.warn "[nf-core/coproid] Found multiple reports from process 'multiqc', will use only one" mqc_report = mqc_report[0] } @@ -321,6 +313,12 @@ workflow.onComplete { log.warn "[nf-core/coproid] Could not attach MultiQC report to summary email" } + // Check if we are only sending emails on failure + email_address = params.email + if (!params.email && params.email_on_fail && !workflow.success) { + email_address = params.email_on_fail + } + // Render the TXT template def engine = new groovy.text.GStringTemplateEngine() def tf = new File("$baseDir/assets/email_template.txt") @@ -333,89 +331,89 @@ workflow.onComplete { def email_html = html_template.toString() // Render the sendmail template - def smail_fields = [ email: params.email, subject: subject, email_txt: email_txt, email_html: email_html, baseDir: "$baseDir", mqcFile: mqc_report, mqcMaxSize: params.maxMultiqcEmailFileSize.toBytes() ] + def smail_fields = [ email: email_address, subject: subject, email_txt: email_txt, email_html: email_html, baseDir: "$baseDir", mqcFile: mqc_report, mqcMaxSize: params.max_multiqc_email_size.toBytes() ] def sf = new File("$baseDir/assets/sendmail_template.txt") def sendmail_template = engine.createTemplate(sf).make(smail_fields) def sendmail_html = sendmail_template.toString() // Send the HTML e-mail - if (params.email) { + if (email_address) { try { - if( params.plaintext_email ){ throw GroovyException('Send plaintext e-mail, not HTML') } - // Try to send HTML e-mail using sendmail - [ 'sendmail', '-t' ].execute() << sendmail_html - log.info "[nf-core/coproid] Sent summary e-mail to $params.email (sendmail)" + if (params.plaintext_email) { throw GroovyException('Send plaintext e-mail, not HTML') } + // Try to send HTML e-mail using sendmail + [ 'sendmail', '-t' ].execute() << sendmail_html + log.info "[nf-core/coproid] Sent summary e-mail to $email_address (sendmail)" } catch (all) { - // Catch failures and try with plaintext - [ 'mail', '-s', subject, params.email ].execute() << email_txt - log.info "[nf-core/coproid] Sent summary e-mail to $params.email (mail)" + // Catch failures and try with plaintext + [ 'mail', '-s', subject, email_address ].execute() << email_txt + log.info "[nf-core/coproid] Sent summary e-mail to $email_address (mail)" } } // Write summary e-mail HTML to a file - def output_d = new File( "${params.outdir}/pipeline_info/" ) - if( !output_d.exists() ) { - output_d.mkdirs() + def output_d = new File("${params.outdir}/pipeline_info/") + if (!output_d.exists()) { + output_d.mkdirs() } - def output_hf = new File( output_d, "pipeline_report.html" ) + def output_hf = new File(output_d, "pipeline_report.html") output_hf.withWriter { w -> w << email_html } - def output_tf = new File( output_d, "pipeline_report.txt" ) + def output_tf = new File(output_d, "pipeline_report.txt") output_tf.withWriter { w -> w << email_txt } - c_reset = params.monochrome_logs ? '' : "\033[0m"; - c_purple = params.monochrome_logs ? '' : "\033[0;35m"; c_green = params.monochrome_logs ? '' : "\033[0;32m"; + c_purple = params.monochrome_logs ? '' : "\033[0;35m"; c_red = params.monochrome_logs ? '' : "\033[0;31m"; + c_reset = params.monochrome_logs ? '' : "\033[0m"; - if (workflow.stats.ignoredCountFmt > 0 && workflow.success) { - log.info "${c_purple}Warning, pipeline completed, but with errored process(es) ${c_reset}" - log.info "${c_red}Number of ignored errored process(es) : ${workflow.stats.ignoredCountFmt} ${c_reset}" - log.info "${c_green}Number of successfully ran process(es) : ${workflow.stats.succeedCountFmt} ${c_reset}" + if (workflow.stats.ignoredCount > 0 && workflow.success) { + log.info "-${c_purple}Warning, pipeline completed, but with errored process(es) ${c_reset}-" + log.info "-${c_red}Number of ignored errored process(es) : ${workflow.stats.ignoredCount} ${c_reset}-" + log.info "-${c_green}Number of successfully ran process(es) : ${workflow.stats.succeedCount} ${c_reset}-" } - if(workflow.success){ - log.info "${c_purple}[nf-core/coproid]${c_green} Pipeline completed successfully${c_reset}" + if (workflow.success) { + log.info "-${c_purple}[nf-core/coproid]${c_green} Pipeline completed successfully${c_reset}-" } else { checkHostname() - log.info "${c_purple}[nf-core/coproid]${c_red} Pipeline completed with errors${c_reset}" + log.info "-${c_purple}[nf-core/coproid]${c_red} Pipeline completed with errors${c_reset}-" } } -def nfcoreHeader(){ +def nfcoreHeader() { // Log colors ANSI codes - c_reset = params.monochrome_logs ? '' : "\033[0m"; - c_dim = params.monochrome_logs ? '' : "\033[2m"; c_black = params.monochrome_logs ? '' : "\033[0;30m"; - c_green = params.monochrome_logs ? '' : "\033[0;32m"; - c_yellow = params.monochrome_logs ? '' : "\033[0;33m"; c_blue = params.monochrome_logs ? '' : "\033[0;34m"; - c_purple = params.monochrome_logs ? '' : "\033[0;35m"; c_cyan = params.monochrome_logs ? '' : "\033[0;36m"; + c_dim = params.monochrome_logs ? '' : "\033[2m"; + c_green = params.monochrome_logs ? '' : "\033[0;32m"; + c_purple = params.monochrome_logs ? '' : "\033[0;35m"; + c_reset = params.monochrome_logs ? '' : "\033[0m"; c_white = params.monochrome_logs ? '' : "\033[0;37m"; + c_yellow = params.monochrome_logs ? '' : "\033[0;33m"; - return """ ${c_dim}----------------------------------------------------${c_reset} + return """ -${c_dim}--------------------------------------------------${c_reset}- ${c_green},--.${c_black}/${c_green},-.${c_reset} ${c_blue} ___ __ __ __ ___ ${c_green}/,-._.--~\'${c_reset} ${c_blue} |\\ | |__ __ / ` / \\ |__) |__ ${c_yellow}} {${c_reset} ${c_blue} | \\| | \\__, \\__/ | \\ |___ ${c_green}\\`-._,-`-,${c_reset} ${c_green}`._,._,\'${c_reset} ${c_purple} nf-core/coproid v${workflow.manifest.version}${c_reset} - ${c_dim}----------------------------------------------------${c_reset} + -${c_dim}--------------------------------------------------${c_reset}- """.stripIndent() } -def checkHostname(){ +def checkHostname() { def c_reset = params.monochrome_logs ? '' : "\033[0m" def c_white = params.monochrome_logs ? '' : "\033[0;37m" def c_red = params.monochrome_logs ? '' : "\033[1;91m" def c_yellow_bold = params.monochrome_logs ? '' : "\033[1;93m" - if(params.hostnames){ + if (params.hostnames) { def hostname = "hostname".execute().text.trim() params.hostnames.each { prof, hnames -> hnames.each { hname -> - if(hostname.contains(hname) && !workflow.profile.contains(prof)){ + if (hostname.contains(hname) && !workflow.profile.contains(prof)) { log.error "====================================================\n" + " ${c_red}WARNING!${c_reset} You are running with `-profile $workflow.profile`\n" + " but your machine hostname is ${c_white}'$hostname'${c_reset}\n" + diff --git a/nextflow.config b/nextflow.config index 6a919ec..0867a55 100644 --- a/nextflow.config +++ b/nextflow.config @@ -10,29 +10,35 @@ params { // Workflow flags // TODO nf-core: Specify your pipeline's command line flags + genome = false reads = "data/*{1,2}.fastq.gz" - singleEnd = false + single_end = false outdir = './results' // Boilerplate options name = false - multiqc_config = "$baseDir/assets/multiqc_config.yaml" + multiqc_config = false email = false - maxMultiqcEmailFileSize = 25.MB + email_on_fail = false + max_multiqc_email_size = 25.MB plaintext_email = false monochrome_logs = false help = false - igenomes_base = "./iGenomes" + igenomes_base = 's3://ngi-igenomes/igenomes/' tracedir = "${params.outdir}/pipeline_info" - awsqueue = false - awsregion = 'eu-west-1' - igenomesIgnore = false + igenomes_ignore = false custom_config_version = 'master' custom_config_base = "https://raw.githubusercontent.com/nf-core/configs/${params.custom_config_version}" hostnames = false config_profile_description = false config_profile_contact = false config_profile_url = false + + // Defaults only, expecting to be overwritten + max_memory = 128.GB + max_cpus = 16 + max_time = 240.h + } // Container slug. Stable releases should specify release tag! @@ -50,19 +56,33 @@ try { } profiles { - awsbatch { includeConfig 'conf/awsbatch.config' } conda { process.conda = "$baseDir/environment.yml" } debug { process.beforeScript = 'echo $HOSTNAME' } - docker { docker.enabled = true } - singularity { singularity.enabled = true } + docker { + docker.enabled = true + // Avoid this error: + // WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap. + // Testing this in nf-core after discussion here https://github.com/nf-core/tools/pull/351 + // once this is established and works well, nextflow might implement this behavior as new default. + docker.runOptions = '-u \$(id -u):\$(id -g)' + } + singularity { + singularity.enabled = true + singularity.autoMounts = true + } test { includeConfig 'conf/test.config' } } // Load igenomes.config if required -if(!params.igenomesIgnore){ +if (!params.igenomes_ignore) { includeConfig 'conf/igenomes.config' } +// Export this variable to prevent local Python libraries from conflicting with those in the container +env { + PYTHONNOUSERSITE = 1 +} + // Capture exit codes from upstream processes when piping process.shell = ['/bin/bash', '-euo', 'pipefail'] @@ -89,16 +109,16 @@ manifest { homePage = 'https://github.com/nf-core/coproid' description = 'Coprolite Identification' mainScript = 'main.nf' - nextflowVersion = '>=0.32.0' - version = '1.0dev' + nextflowVersion = '>=19.10.0' + version = '1.1dev' } // Function to ensure that resource requirements don't go beyond // a maximum limit def check_max(obj, type) { - if(type == 'memory'){ + if (type == 'memory') { try { - if(obj.compareTo(params.max_memory as nextflow.util.MemoryUnit) == 1) + if (obj.compareTo(params.max_memory as nextflow.util.MemoryUnit) == 1) return params.max_memory as nextflow.util.MemoryUnit else return obj @@ -106,9 +126,9 @@ def check_max(obj, type) { println " ### ERROR ### Max memory '${params.max_memory}' is not valid! Using default value: $obj" return obj } - } else if(type == 'time'){ + } else if (type == 'time') { try { - if(obj.compareTo(params.max_time as nextflow.util.Duration) == 1) + if (obj.compareTo(params.max_time as nextflow.util.Duration) == 1) return params.max_time as nextflow.util.Duration else return obj @@ -116,7 +136,7 @@ def check_max(obj, type) { println " ### ERROR ### Max time '${params.max_time}' is not valid! Using default value: $obj" return obj } - } else if(type == 'cpus'){ + } else if (type == 'cpus') { try { return Math.min( obj, params.max_cpus as int ) } catch (all) { From 82fdd29991d2ca06ff16fd1019dcac5d05d4a66c Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 6 Mar 2020 13:54:01 +0100 Subject: [PATCH 58/96] update environment --- environment.yml | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/environment.yml b/environment.yml index d2e98b8..d470fd5 100644 --- a/environment.yml +++ b/environment.yml @@ -1,3 +1,5 @@ +# You can use this file to create a conda environment for this pipeline: +# conda env create -f environment.yml name: nf-core-coproid-1.1dev channels: - conda-forge @@ -11,7 +13,7 @@ dependencies: - bioconda::bedtools=2.29.0 - bioconda::blast=2.9.0 - conda-forge::bokeh=1.3.4 - - bioconda::bowtie2=2.3.5 + - bioconda::bowtie2=2.4.1 - bioconda::damageprofiler=0.4.6 - bioconda::fastqc=0.11.8 - conda-forge::jupyter=1.0.0 @@ -22,10 +24,12 @@ dependencies: - bioconda::pmdtools=0.60 - bioconda::pysam=0.15.3 - bioconda::samtools=1.9 - - maxibor::sourcepredict=0.4 + - maxibor::sourcepredict=0.5 - conda-forge::scipy=1.3.1 - conda-forge::matplotlib=3.1.2 - bioconda::multiqc=1.7 - - conda-forge::r-markdown=1.1 - conda-forge::jupyter_contrib_nbextensions=0.5.1 - conda-forge::plotnine=0.6.0 + - conda-forge::markdown=3.1.1 + - conda-forge::pymdown-extensions=6.0 + - conda-forge::pygments=2.5.2 From 8999ba793376da90747aa8ac024cbf829af080bf Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 6 Mar 2020 14:04:02 +0100 Subject: [PATCH 59/96] remove extra text --- nextflow.config | 6 ------ 1 file changed, 6 deletions(-) diff --git a/nextflow.config b/nextflow.config index 0d29b08..9ef826f 100644 --- a/nextflow.config +++ b/nextflow.config @@ -62,12 +62,6 @@ params { max_memory = 128.GB max_cpus = 16 max_time = 240.h - -} - - max_memory = 128.GB - max_cpus = 16 - max_time = 240.h } // Container slug. Stable releases should specify release tag! From 3e357927f7197c1fb430c5e190fe3b067366a951 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 6 Mar 2020 14:07:44 +0100 Subject: [PATCH 60/96] update dockerfile --- Dockerfile | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/Dockerfile b/Dockerfile index c0a00a4..b8b51d4 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,13 +1,11 @@ -FROM nfcore/base:1.8 +FROM nfcore/base:1.9 LABEL authors="Maxime Borry" \ description="Docker image containing all software requirements for the nf-core/coproid pipeline" # Install the conda environment COPY environment.yml / RUN conda env create -f /environment.yml && conda clean -a - -# Add conda installation dir to PATH (instead of doing 'conda activate') +RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH # Dump the details of the installed packages to a file for posterity -RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml From 4932dfb6094fe7e1bb53ff2c31ffab2963501ac0 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 6 Mar 2020 16:23:34 +0100 Subject: [PATCH 61/96] remove deps --- environment.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/environment.yml b/environment.yml index d470fd5..616c1e0 100644 --- a/environment.yml +++ b/environment.yml @@ -7,13 +7,11 @@ channels: - bioconda - defaults dependencies: - - conda-forge::openblas=0.3.7 - bioconda::adapterremoval=2.3.1 - bioconda::bcftools=1.9 - bioconda::bedtools=2.29.0 - - bioconda::blast=2.9.0 - conda-forge::bokeh=1.3.4 - - bioconda::bowtie2=2.4.1 + - bioconda::bowtie2=2.3.5 - bioconda::damageprofiler=0.4.6 - bioconda::fastqc=0.11.8 - conda-forge::jupyter=1.0.0 From d2a1bcceb61633cf71c35495e2b95c2ddfb636e0 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 13:19:44 +0200 Subject: [PATCH 62/96] update dependencies --- Dockerfile | 4 ++++ environment.yml | 29 ++++++++++++++-------------- nextflow.config | 2 +- templates/coproID_report.ipynb | 35 ++++++++++++++++++++++++++++------ 4 files changed, 49 insertions(+), 21 deletions(-) diff --git a/Dockerfile b/Dockerfile index d8df6d9..33e45c7 100644 --- a/Dockerfile +++ b/Dockerfile @@ -11,3 +11,7 @@ ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH # Dump the details of the installed packages to a file for posterity RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml + +# Numba cache dir patch +ENV NUMBA_CACHE_DIR /tmp +ENV HOME /tmp \ No newline at end of file diff --git a/environment.yml b/environment.yml index d470fd5..e00fe68 100644 --- a/environment.yml +++ b/environment.yml @@ -9,27 +9,28 @@ channels: dependencies: - conda-forge::openblas=0.3.7 - bioconda::adapterremoval=2.3.1 - - bioconda::bcftools=1.9 - - bioconda::bedtools=2.29.0 + - bioconda::bcftools=1.10.2 + - bioconda::bedtools=2.29.2 - bioconda::blast=2.9.0 - - conda-forge::bokeh=1.3.4 - - bioconda::bowtie2=2.4.1 - - bioconda::damageprofiler=0.4.6 - - bioconda::fastqc=0.11.8 + - conda-forge::bokeh=2.0.1 + - bioconda::bowtie2=2.3.5.1 + - bioconda::damageprofiler=0.4.9 + - bioconda::fastqc=0.11.9 - conda-forge::jupyter=1.0.0 - bioconda::kraken2=2.0.8_beta - - conda-forge::notebook=6.0.1 + - conda-forge::notebook=6.0.3 - conda-forge::nbconvert=5.6.1 - - bioconda::nextflow=19.10.0 + - bioconda::nextflow=20.01.0 - bioconda::pmdtools=0.60 - bioconda::pysam=0.15.3 - - bioconda::samtools=1.9 + - bioconda::samtools=1.10 - maxibor::sourcepredict=0.5 - - conda-forge::scipy=1.3.1 - - conda-forge::matplotlib=3.1.2 + - conda-forge::scipy=1.4.1 + - conda-forge::matplotlib=3.2.1 - bioconda::multiqc=1.7 - conda-forge::jupyter_contrib_nbextensions=0.5.1 - conda-forge::plotnine=0.6.0 - - conda-forge::markdown=3.1.1 - - conda-forge::pymdown-extensions=6.0 - - conda-forge::pygments=2.5.2 + - conda-forge::markdown=3.2.1 + - conda-forge::pymdown-extensions=7.0 + - conda-forge::pygments=2.6.1 + - conda-forge::tbb=2020.1 diff --git a/nextflow.config b/nextflow.config index 9ef826f..668776e 100644 --- a/nextflow.config +++ b/nextflow.config @@ -41,7 +41,7 @@ params { sp_embed = 'mds' sp_neighbors = 'all' name = false - multiqc_config = false + multiqc_config = "$baseDir/assets/multiqc_config.yaml" email = false email_on_fail = false max_multiqc_email_size = 25.MB diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 8f8de24..7013a44 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -56,7 +56,8 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import os\n", - "from IPython.display import display, Markdown, Latex\n", + "from IPython.display import display, Markdown, Latex, HTML\n", + "import base64\n", "from bokeh.plotting import figure, show, output_notebook\n", "from bokeh.models import ColumnDataSource\n", "from bokeh.transform import factor_cmap\n", @@ -66,6 +67,7 @@ "from bokeh.models.widgets import Button\n", "from bokeh.models.widgets import DataTable, DateFormatter, TableColumn\n", "from bokeh.models import CustomJS\n", + "import ipywidgets as widgets\n", "from plotnine import *\n", "import warnings\n", "warnings.simplefilter('ignore')" @@ -206,7 +208,7 @@ " link.dispatchEvent(new MouseEvent('click'))\n", " }\n", " \"\"\"\n", - " button.callback = CustomJS(args=dict(source=source),code=javaScript)\n", + " button.js_event_callbacks = CustomJS(args=dict(source=source),code=javaScript)\n", " output_notebook(hide_banner=True)\n", " show(button)" ] @@ -243,20 +245,41 @@ "outputs": [], "source": [ "d = \"coproID_result.csv\"\n", - "pd.read_csv(d, index_col=0)" + "df = pd.read_csv(d, index_col=0)\n", + "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { + "hideCode": false, "tags": [ "remove_cell" ] }, "outputs": [], "source": [ - "bokeh_table(d)" + "def create_download_link(df=df, title = \"Download table as CSV\", filename = \"coproid_result.csv\"): \n", + " csv = df.to_csv(index =False)\n", + " b64 = base64.b64encode(csv.encode())\n", + " payload = b64.decode()\n", + " html = '{title}'\n", + " html = html.format(payload=payload,title=title,filename=filename)\n", + " return HTML(html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove_cell" + ] + }, + "outputs": [], + "source": [ + "create_download_link(df)\n" ] }, { @@ -389,7 +412,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.6" }, "toc": { "base_numbering": 1, @@ -406,5 +429,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 2080930a0b96b67c762ffd3b20e77714b39dada1 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 13:23:26 +0200 Subject: [PATCH 63/96] update env to fix docker for 1.1 release --- environment.yml | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/environment.yml b/environment.yml index 616c1e0..e00fe68 100644 --- a/environment.yml +++ b/environment.yml @@ -7,27 +7,30 @@ channels: - bioconda - defaults dependencies: + - conda-forge::openblas=0.3.7 - bioconda::adapterremoval=2.3.1 - - bioconda::bcftools=1.9 - - bioconda::bedtools=2.29.0 - - conda-forge::bokeh=1.3.4 - - bioconda::bowtie2=2.3.5 - - bioconda::damageprofiler=0.4.6 - - bioconda::fastqc=0.11.8 + - bioconda::bcftools=1.10.2 + - bioconda::bedtools=2.29.2 + - bioconda::blast=2.9.0 + - conda-forge::bokeh=2.0.1 + - bioconda::bowtie2=2.3.5.1 + - bioconda::damageprofiler=0.4.9 + - bioconda::fastqc=0.11.9 - conda-forge::jupyter=1.0.0 - bioconda::kraken2=2.0.8_beta - - conda-forge::notebook=6.0.1 + - conda-forge::notebook=6.0.3 - conda-forge::nbconvert=5.6.1 - - bioconda::nextflow=19.10.0 + - bioconda::nextflow=20.01.0 - bioconda::pmdtools=0.60 - bioconda::pysam=0.15.3 - - bioconda::samtools=1.9 + - bioconda::samtools=1.10 - maxibor::sourcepredict=0.5 - - conda-forge::scipy=1.3.1 - - conda-forge::matplotlib=3.1.2 + - conda-forge::scipy=1.4.1 + - conda-forge::matplotlib=3.2.1 - bioconda::multiqc=1.7 - conda-forge::jupyter_contrib_nbextensions=0.5.1 - conda-forge::plotnine=0.6.0 - - conda-forge::markdown=3.1.1 - - conda-forge::pymdown-extensions=6.0 - - conda-forge::pygments=2.5.2 + - conda-forge::markdown=3.2.1 + - conda-forge::pymdown-extensions=7.0 + - conda-forge::pygments=2.6.1 + - conda-forge::tbb=2020.1 From ea678cef9da4e5bc3a438a54f5f530d899fa828b Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 13:32:00 +0200 Subject: [PATCH 64/96] update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4cbf286..a021118 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -15,6 +15,7 @@ c2d4164](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d - Update travis for more recent nextflow requirements [1e3e454](https://github.com/nf-core/coproid/commit/1e3e454e72f1bc8eb43aaa1e5165981cb77a56dc) - Adapt coproID to nf-core tools 1.8 release [#21](https://github.com/nf-core/coproid/pull/21) - Add social preview image [#22](https://github.com/nf-core/coproid/pull/22) +- Fix Kraken2 segmentation error [#26](https://github.com/nf-core/coproid/pull/26) ## v1.0 - 2019-04-26 From 3a963d68703021093c63cd1fb61e49d39bfd5c04 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 14:03:46 +0200 Subject: [PATCH 65/96] remove nextflow from env --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index e00fe68..6dd3b5e 100644 --- a/environment.yml +++ b/environment.yml @@ -20,7 +20,6 @@ dependencies: - bioconda::kraken2=2.0.8_beta - conda-forge::notebook=6.0.3 - conda-forge::nbconvert=5.6.1 - - bioconda::nextflow=20.01.0 - bioconda::pmdtools=0.60 - bioconda::pysam=0.15.3 - bioconda::samtools=1.10 From 7254c9a69b177b212d535335e85384d53d206de5 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 14:22:33 +0200 Subject: [PATCH 66/96] remove nextflow --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index e00fe68..6dd3b5e 100644 --- a/environment.yml +++ b/environment.yml @@ -20,7 +20,6 @@ dependencies: - bioconda::kraken2=2.0.8_beta - conda-forge::notebook=6.0.3 - conda-forge::nbconvert=5.6.1 - - bioconda::nextflow=20.01.0 - bioconda::pmdtools=0.60 - bioconda::pysam=0.15.3 - bioconda::samtools=1.10 From bb5425946547bf3774b905761634aa43b1276846 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:11:21 +0200 Subject: [PATCH 67/96] update doc --- README.md | 41 +++++++++++++++++++++++++++++++---------- docs/README.md | 40 ---------------------------------------- docs/conf.py | 9 ++++----- docs/configuration.md | 6 +++--- docs/introduction.md | 28 ++++++++++++++++++++++++---- docs/output.md | 1 - 6 files changed, 62 insertions(+), 63 deletions(-) delete mode 100644 docs/README.md diff --git a/README.md b/README.md index f30c012..9dd0b93 100644 --- a/README.md +++ b/README.md @@ -8,10 +8,9 @@ [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) -[![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) - ## Introduction **CoproID** helps you to identify the _"true maker"_ of Illumina sequenced Coprolites/Paleofaeces by checking the microbiome composition and the endogenous DNA. @@ -46,14 +45,14 @@ The nf-core/coproid pipeline comes with documentation about the pipeline, found The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory and at the following address: [coproid.readthedocs.io](https://coproid.readthedocs.io) -1. [Installation](https://nf-co.re/usage/installation) -2. Pipeline configuration - - [Local installation](https://nf-co.re/usage/local_installation) - - [Adding your own system config](https://nf-co.re/usage/adding_own_config) - - [Reference genomes](https://nf-co.re/usage/reference_genomes) -3. [Running the pipeline](docs/usage.md) -4. [Output and how to interpret the results](docs/output.md) -5. [Troubleshooting](https://nf-co.re/usage/troubleshooting) +1. [Installation](https://nf-co.re/usage/installation) +2. [Pipeline configuration + - [Local installation](https://nf-co.re/usage/local_installation) + - [Adding your own system config](https://nf-co.re/usage/adding_own_config) + - [Reference genomes](https://nf-co.re/usage/reference_genomes) +3. [Running the pipeline](docs/usage.md) +4. [Output and how to interpret the results](docs/output.md) +5. [Troubleshooting](https://nf-co.re/usage/troubleshooting) ## Credits @@ -65,6 +64,28 @@ If you would like to contribute to this pipeline, please see the [contributing g For further information or help, don't hesitate to get in touch on [Slack](https://nfcore.slack.com/channels/coproid) (you can join with [this invite](https://nf-co.re/join/slack)). +## Citing + +coproID has been published in [peerJ](https://peerj.com/articles/9001), the bibtex citation is available below: + +```bibtex +@article{borry_coproid_2020, + title = {{CoproID} predicts the source of coprolites and paleofeces using microbiome composition and host {DNA} content}, + volume = {8}, + issn = {2167-8359}, + url = {https://peerj.com/articles/9001}, + doi = {10.7717/peerj.9001}, + language = {en}, + urldate = {2020-04-20}, + journal = {PeerJ}, + author = {Borry, Maxime and Cordova, Bryan and Perri, Angela and Wibowo, Marsha and Honap, Tanvi Prasad and Ko, Jada and Yu, Jie and Britton, Kate and Girdland-Flink, Linus and Power, Robert C. and Stuijts, Ingelise and Salazar-García, Domingo C. and Hofman, Courtney and Hagan, Richard and Kagoné, Thérèse Samdapawindé and Meda, Nicolas and Carabin, Helene and Jacobson, David and Reinhard, Karl and Lewis, Cecil and Kostic, Aleksandar and Jeong, Choongwon and Herbig, Alexander and Hübner, Alexander and Warinner, Christina}, + month = apr, + year = {2020}, + note = {Publisher: PeerJ Inc.}, + pages = {e9001} +} +``` + ## Contributors [James A. Fellows-Yates](https://github.com/jfy133) diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index a351aec..0000000 --- a/docs/README.md +++ /dev/null @@ -1,40 +0,0 @@ -# nf-core/coproid: Documentation - -![nf-core-logo](../assets/img/coproID_nf-core_logo.svg) - -**coproID** (**CO**prolite **ID**entification) is a tool developed at the -[Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) -by [Maxime Borry](https://github.com/maxibor) - -The purpose of **coproID** is to help identify the host of given sequenced -microbiome when there is a doubt between species. - -**coproID** is a pipeline developed using [Nextflow](https://www.nextflow.io/) - -and made available through [nf-core](https://github.com/nf-core) - -Even though it was developed with coprolite host identification in mind, it can - be applied to any microbiome, provided they contain host DNA. - -1. [Installation](https://nf-co.re/usage/installation) -2. Pipeline configuration - - [Local installation](https://nf-co.re/usage/local_installation) - - [Adding your own system config](https://nf-co.re/usage/adding_own_config) - - [Reference genomes](https://nf-co.re/usage/reference_genomes) -3. [Running the pipeline](usage.md) -4. [Output and how to interpret the results](output.md) -5. [Troubleshooting](https://nf-co.re/usage/troubleshooting) - -## Quick start - -Example: - - nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' - -## coproID example workFlow - -![dag](source/_static/img/coproid_dag.png) - -## How to cite coproID - -The coproID article is coming. diff --git a/docs/conf.py b/docs/conf.py index 5bdb5ea..c600488 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,10 +16,8 @@ # import sys # sys.path.insert(0, os.path.abspath('.')) -from recommonmark.parser import CommonMarkParser - source_parsers = { - '.md': CommonMarkParser, + '.md': 'recommonmark.parser.CommonMarkParser', } @@ -32,7 +30,7 @@ # The short X.Y version version = '' # The full version, including alpha/beta/rc tags -release = '1.0' +release = '1.1' # -- General configuration --------------------------------------------------- @@ -52,6 +50,7 @@ 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', + 'recommonmark' ] # Add any paths that contain templates here, relative to this directory. @@ -99,7 +98,7 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +# html_static_path = ['_static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. diff --git a/docs/configuration.md b/docs/configuration.md index ebf5787..28aa6ff 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -5,6 +5,6 @@ This part of the documentation (common to all nf-core pipelines) is hosted on [n ## Pipeline configuration -- [Local installation](https://nf-co.re/usage/local_installation) -- [Adding your own system config](https://nf-co.re/usage/adding_own_config) -- [Reference genomes](https://nf-co.re/usage/reference_genomes) +- [Local installation](https://nf-co.re/usage/local_installation) +- [Adding your own system config](https://nf-co.re/usage/adding_own_config) +- [Reference genomes](https://nf-co.re/usage/reference_genomes) diff --git a/docs/introduction.md b/docs/introduction.md index 175b818..8cb3e06 100644 --- a/docs/introduction.md +++ b/docs/introduction.md @@ -1,6 +1,6 @@ # Introduction -![](../assets/img/coproID_nf-core_logo.svg) +![nf-core-logo](../assets/nf-core-coproid_logo.png) **coproID** (**CO**prolite **ID**entification) is a tool developed at the [Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) @@ -16,12 +16,32 @@ Even though it was developed with coprolite host identification in mind, it can Example: - nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' +```bash +nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' +``` ## coproID example workFlow -![](../assets/img/coproid_dag.png) +![coproid-dag](../assets/img/coproid_dag.png) ## How to cite coproID -The coproID article is coming. +coproID has been published in [peerJ](https://peerj.com/articles/9001), the bibtex citation is available below: + +```bibtex +@article{borry_coproid_2020, + title = {{CoproID} predicts the source of coprolites and paleofeces using microbiome composition and host {DNA} content}, + volume = {8}, + issn = {2167-8359}, + url = {https://peerj.com/articles/9001}, + doi = {10.7717/peerj.9001}, + language = {en}, + urldate = {2020-04-20}, + journal = {PeerJ}, + author = {Borry, Maxime and Cordova, Bryan and Perri, Angela and Wibowo, Marsha and Honap, Tanvi Prasad and Ko, Jada and Yu, Jie and Britton, Kate and Girdland-Flink, Linus and Power, Robert C. and Stuijts, Ingelise and Salazar-García, Domingo C. and Hofman, Courtney and Hagan, Richard and Kagoné, Thérèse Samdapawindé and Meda, Nicolas and Carabin, Helene and Jacobson, David and Reinhard, Karl and Lewis, Cecil and Kostic, Aleksandar and Jeong, Choongwon and Herbig, Alexander and Hübner, Alexander and Warinner, Christina}, + month = apr, + year = {2020}, + note = {Publisher: PeerJ Inc.}, + pages = {e9001} +} +``` diff --git a/docs/output.md b/docs/output.md index 1b15776..9572496 100644 --- a/docs/output.md +++ b/docs/output.md @@ -7,7 +7,6 @@ This document describes the output produced by the coproID pipeline. The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps: - ## FastQC [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your reads. It provides information about the quality score distribution across your reads, the per base sequence content (%T/A/G/C). You get information about adapter contamination and other overrepresented sequences. From 40ef7ff5ad967733a0829e2775f58de6011101fd Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:14:26 +0200 Subject: [PATCH 68/96] update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 822166d..06835ed 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ c2d4164](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d - Adapt coproID to nf-core tools 1.8 release [#21](https://github.com/nf-core/coproid/pull/21) - Add social preview image [#22](https://github.com/nf-core/coproid/pull/22) - Fix Kraken2 segmentation error [#26](https://github.com/nf-core/coproid/pull/26) +- Update to nf-core tools 1.9 release, and doc for new version of sphinx ## v1.0 - 2019-04-26 From a35dd250c1750053280fe08d3b52ec1070c2d22c Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:18:51 +0200 Subject: [PATCH 69/96] update changelog --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 06835ed..181424d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,7 +16,7 @@ c2d4164](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d - Adapt coproID to nf-core tools 1.8 release [#21](https://github.com/nf-core/coproid/pull/21) - Add social preview image [#22](https://github.com/nf-core/coproid/pull/22) - Fix Kraken2 segmentation error [#26](https://github.com/nf-core/coproid/pull/26) -- Update to nf-core tools 1.9 release, and doc for new version of sphinx +- Update to nf-core tools 1.9 release, and doc for new version of sphinx [#27](https://github.com/nf-core/coproid/pull/27) ## v1.0 - 2019-04-26 From 15122a8b562546700205b063ca15d99c12b908cc Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:22:09 +0200 Subject: [PATCH 70/96] add README in docs --- docs/README.md | 103 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 docs/README.md diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..9dd0b93 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,103 @@ +# ![nf-core/coproid](docs/images/nf-core-coproid_logo.png) + +**Coprolite Identification**. + +[![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) +[![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) +[![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) + +[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) +[![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) +[![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) + +## Introduction + +**CoproID** helps you to identify the _"true maker"_ of Illumina sequenced Coprolites/Paleofaeces by checking the microbiome composition and the endogenous DNA. + +The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. + +## Quick Start + +i. Install [`nextflow`](https://nf-co.re/usage/installation) + +ii. Install either [`Docker`](https://docs.docker.com/engine/installation/) or [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) for full pipeline reproducibility (please only use [`Conda`](https://conda.io/miniconda.html) as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles)) + +iii. Download the pipeline and test it on a minimal dataset with a single command + +```bash +nextflow run nf-core/coproid -profile test, +``` + +> Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use `-profile institute` in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment. + +iv. Start running your own analysis! + +```bash +nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker +``` + +See [usage docs](docs/usage.md) for all of the available options when running the pipeline. + +## Documentation + +The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory: + +The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory and at the following address: [coproid.readthedocs.io](https://coproid.readthedocs.io) + +1. [Installation](https://nf-co.re/usage/installation) +2. [Pipeline configuration + - [Local installation](https://nf-co.re/usage/local_installation) + - [Adding your own system config](https://nf-co.re/usage/adding_own_config) + - [Reference genomes](https://nf-co.re/usage/reference_genomes) +3. [Running the pipeline](docs/usage.md) +4. [Output and how to interpret the results](docs/output.md) +5. [Troubleshooting](https://nf-co.re/usage/troubleshooting) + +## Credits + +nf-core/coproid was written by [Maxime Borry](https://github.com/maxibor). + +## Contributions and Support + +If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md). + +For further information or help, don't hesitate to get in touch on [Slack](https://nfcore.slack.com/channels/coproid) (you can join with [this invite](https://nf-co.re/join/slack)). + +## Citing + +coproID has been published in [peerJ](https://peerj.com/articles/9001), the bibtex citation is available below: + +```bibtex +@article{borry_coproid_2020, + title = {{CoproID} predicts the source of coprolites and paleofeces using microbiome composition and host {DNA} content}, + volume = {8}, + issn = {2167-8359}, + url = {https://peerj.com/articles/9001}, + doi = {10.7717/peerj.9001}, + language = {en}, + urldate = {2020-04-20}, + journal = {PeerJ}, + author = {Borry, Maxime and Cordova, Bryan and Perri, Angela and Wibowo, Marsha and Honap, Tanvi Prasad and Ko, Jada and Yu, Jie and Britton, Kate and Girdland-Flink, Linus and Power, Robert C. and Stuijts, Ingelise and Salazar-García, Domingo C. and Hofman, Courtney and Hagan, Richard and Kagoné, Thérèse Samdapawindé and Meda, Nicolas and Carabin, Helene and Jacobson, David and Reinhard, Karl and Lewis, Cecil and Kostic, Aleksandar and Jeong, Choongwon and Herbig, Alexander and Hübner, Alexander and Warinner, Christina}, + month = apr, + year = {2020}, + note = {Publisher: PeerJ Inc.}, + pages = {e9001} +} +``` + +## Contributors + +[James A. Fellows-Yates](https://github.com/jfy133) + +## Tool references + +- **AdapterRemoval v2** Schubert, M., Lindgreen, S., & Orlando, L. (2016). AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes, 9, 88. [https://doi.org/10.1186/s13104-016-1900-2](https://doi.org/10.1186/s13104-016-1900-2) +- **FastQC** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) +- **Bowtie2** Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357. [https://dx.doi.org/10.1038%2Fnmeth.1923](https://dx.doi.org/10.1038%2Fnmeth.1923) +- **Samtools** Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. [https://doi.org/10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) +- **Kraken2** Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. BioRxiv, 762302. [https://doi.org/10.1101/762302](https://doi.org/10.1101/762302) +- **PMDTools** Skoglund, P., Northoff, B. H., Shunkov, M. V., Derevianko, A. P., Pääbo, S., Krause, J., & Jakobsson, M. (2014). Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proceedings of the National Academy of Sciences of the United States of America, 111(6), 2229–2234. [https://doi.org/10.1073/pnas.1318934111](https://doi.org/10.1073/pnas.1318934111) +- **DamageProfiler** Judith Neukamm (Unpublished) +- **Sourcepredict** Borry, M. (2019). Sourcepredict: Prediction of metagenomic sample sources using dimension reduction followed by machine learning classification. The Journal of Open Source Software. [https://doi.org/10.21105/joss.01540](https://doi.org/10.21105/joss.01540) +- **MultiQC** Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. [https://doi.org/10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) From 9c2202cc6dd999f2f0d5b38a89a89a4eefa045c3 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:25:43 +0200 Subject: [PATCH 71/96] citation suggestion update --- README.md | 2 +- docs/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9dd0b93..d927e36 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@ For further information or help, don't hesitate to get in touch on [Slack](https ## Citing -coproID has been published in [peerJ](https://peerj.com/articles/9001), the bibtex citation is available below: +coproID has been published in [peerJ](https://peerj.com/articles/9001). The bibtex citation is available below: ```bibtex @article{borry_coproid_2020, diff --git a/docs/README.md b/docs/README.md index 9dd0b93..d927e36 100644 --- a/docs/README.md +++ b/docs/README.md @@ -66,7 +66,7 @@ For further information or help, don't hesitate to get in touch on [Slack](https ## Citing -coproID has been published in [peerJ](https://peerj.com/articles/9001), the bibtex citation is available below: +coproID has been published in [peerJ](https://peerj.com/articles/9001). The bibtex citation is available below: ```bibtex @article{borry_coproid_2020, From b2423524c32c9f6de51ae689ef24aa567d190dbc Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:29:58 +0200 Subject: [PATCH 72/96] fix markdown linting --- CHANGELOG.md | 2 +- docs/usage.md | 22 ++++++++++------------ 2 files changed, 11 insertions(+), 13 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 181424d..878f7cc 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,7 +13,7 @@ c2d4164](https://github.com/nf-core/coproid/pull/20/commits/c2d4164bf068ed4fc92d - Update documentation [bedfdde](https://github.com/nf-core/coproid/commit/bedfddec8500adac8e0cb9cc8e0df2dc6a784f15) - Update Nextflow minimum version to 19.04.0 [44999fd](https://github.com/nf-core/coproid/commit/44999fd4d38b21d53f970621dbf3587c044da8d1) - Update travis for more recent nextflow requirements [1e3e454](https://github.com/nf-core/coproid/commit/1e3e454e72f1bc8eb43aaa1e5165981cb77a56dc) -- Adapt coproID to nf-core tools 1.8 release [#21](https://github.com/nf-core/coproid/pull/21) +- Adapt coproID to nf-core tools 1.8 release [#21](https://github.com/nf-core/coproid/pull/21) - Add social preview image [#22](https://github.com/nf-core/coproid/pull/22) - Fix Kraken2 segmentation error [#26](https://github.com/nf-core/coproid/pull/26) - Update to nf-core tools 1.9 release, and doc for new version of sphinx [#27](https://github.com/nf-core/coproid/pull/27) diff --git a/docs/usage.md b/docs/usage.md index 2377cbb..43be1a1 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -86,9 +86,9 @@ Use this to specify the location of your input FastQ files. For example: Please note the following requirements: -1. The path must be enclosed in quotes -2. The path must have at least one `*` wildcard character -3. When using the pipeline with paired end data, the path must use `{1,2}` notation to specify read pairs. +1. The path must be enclosed in quotes +2. The path must have at least one `*` wildcard character +3. When using the pipeline with paired end data, the path must use `{1,2}` notation to specify read pairs. If left unspecified, a default pattern is used: `data/*{1,2}.fastq.gz` @@ -129,10 +129,10 @@ There are 31 different species supported in the iGenomes references. To run the You can find the keys to specify the genomes in the [iGenomes config file](../conf/igenomes.config). Common genomes that are supported are: -- Human - - `--genome GRCh37` -- Dog - - `--genome CanFam3.1` +* Human + * `--genome GRCh37` +* Dog + * `--genome CanFam3.1` > There are numerous others - check the config file for more. @@ -336,12 +336,11 @@ Proportion of Endogenous DNA in organism 3 target microbiome. Must be between 0 --endo3 0.01 ``` - ### `sp_embed` SourcePredict embedding algorithm. One of mds, tsne, umap. Default to mds -``` +```bash --sp_embed mds ``` @@ -351,7 +350,7 @@ More information is available in the [Sourcepredict documentation](https://sourc Sourcepredict normalization method. One of 'rle', 'gmpr', 'subsample'. Default = 'gmpr' -``` +```bash --sp_norm 'gmpr' ``` @@ -361,7 +360,7 @@ More informations are available in the [Sourcepredict documentation](https://sou Sourcepredict numbers of neighbors for KNN ML. Integer or all. Default = all -``` +```bash --sp_neighbors all ``` @@ -491,7 +490,6 @@ Note - you can use this to override pipeline defaults. Provide git commit id for custom Institutional configs hosted at `nf-core/configs`. This was implemented for reproducibility purposes. Default: `master`. - ```bash ## Download and use config file with following git commid id --custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96 From a6dadd1e3158a6b836609b35f2707d1554eac411 Mon Sep 17 00:00:00 2001 From: maxibor Date: Mon, 20 Apr 2020 15:35:42 +0200 Subject: [PATCH 73/96] update dockerfile for numba and ete write dir --- Dockerfile | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/Dockerfile b/Dockerfile index b8b51d4..520e878 100644 --- a/Dockerfile +++ b/Dockerfile @@ -9,3 +9,8 @@ RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH # Dump the details of the installed packages to a file for posterity +RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml + +# Numba cache dir patch +ENV NUMBA_CACHE_DIR /tmp +ENV HOME /tmp \ No newline at end of file From bf966333615f007fa0e8c982b69635a0b020ce04 Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 14:01:50 +0200 Subject: [PATCH 74/96] logo update --- assets/img/coproID_nf-core_logo.svg | 205 ---------------------- assets/img/coproID_nf-core_logo_small.png | Bin 35885 -> 0 bytes assets/img/coproid_logo.png | Bin 0 -> 176555 bytes assets/img/coproid_logo.psd | Bin 0 -> 950641 bytes assets/img/coproid_logo_small.jpg | Bin 22416 -> 0 bytes 5 files changed, 205 deletions(-) delete mode 100644 assets/img/coproID_nf-core_logo.svg delete mode 100644 assets/img/coproID_nf-core_logo_small.png create mode 100644 assets/img/coproid_logo.png create mode 100644 assets/img/coproid_logo.psd delete mode 100644 assets/img/coproid_logo_small.jpg diff --git a/assets/img/coproID_nf-core_logo.svg b/assets/img/coproID_nf-core_logo.svg deleted file mode 100644 index 4eb0845..0000000 --- a/assets/img/coproID_nf-core_logo.svg +++ /dev/null @@ -1,205 +0,0 @@ - -image/svg+xmlnf- -core/ -coproID - \ No newline at end of file diff --git a/assets/img/coproID_nf-core_logo_small.png 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z^oi-o<6n%*e`^n?@vkDkeqtQ_b<%THR!bzc4o??xG{jF4^v-}8t~a|l41zfXWU+oXP$<{s*hNsXV9Mhk%PdviInb@A;G{^?7!0Vr>=%h9#z zuFlB(fq0aYmS$)3E#ae;@?NKeK3q5zihrgp+4`VkECvE1KoAYiJ%MgtSOAYrw8Ci@ z2Ek~FOmORd3Y-ioZ#Zc<>v-=~Qv(l|ieVtmeuqWF~#e6f_ozlY*4sO%gGL}J~v=)e)!5C0^9 z0`BVKc8vV&wtJm+zHz+H>ZoCf*_y#1%bUug%XhOk z%VZbU-t9sAMT;QR@eXZtfN2@JuT^5XdijK43io|KxP)Vdxp#)Cj;~zi`v~zwZ+CMY NjnApII{ From a8074b319970f4d3b9655feaa03123b60ac6426b Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 14:02:02 +0200 Subject: [PATCH 75/96] update logos in page --- README.md | 8 +++++--- docs/README.md | 8 +++++--- docs/introduction.md | 2 +- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index d927e36..cf01290 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,17 @@ -# ![nf-core/coproid](docs/images/nf-core-coproid_logo.png) +# ![nf-core/coproid](assets/img/coproid_logo.png) -**Coprolite Identification**. +**A fully reproducible pipeline for COPROlite and paleofeces host IDentification** [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) - [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) +![Singularity Container available](https://img.shields.io/badge/singularity-available-7E4C74.svg) [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) +[![Joins us on Slack](https://img.shields.io/badge/slack-nfcore/coproid-blue.svg)](https://nfcore.slack.com/channels/coproid) +[![Published in PeerJ](https://img.shields.io/badge/peerj-published-%2300B2FF)](https://peerj.com/articles/9001) ## Introduction diff --git a/docs/README.md b/docs/README.md index d927e36..cf01290 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,15 +1,17 @@ -# ![nf-core/coproid](docs/images/nf-core-coproid_logo.png) +# ![nf-core/coproid](assets/img/coproid_logo.png) -**Coprolite Identification**. +**A fully reproducible pipeline for COPROlite and paleofeces host IDentification** [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) - [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) +![Singularity Container available](https://img.shields.io/badge/singularity-available-7E4C74.svg) [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) +[![Joins us on Slack](https://img.shields.io/badge/slack-nfcore/coproid-blue.svg)](https://nfcore.slack.com/channels/coproid) +[![Published in PeerJ](https://img.shields.io/badge/peerj-published-%2300B2FF)](https://peerj.com/articles/9001) ## Introduction diff --git a/docs/introduction.md b/docs/introduction.md index 8cb3e06..e2736fa 100644 --- a/docs/introduction.md +++ b/docs/introduction.md @@ -1,6 +1,6 @@ # Introduction -![nf-core-logo](../assets/nf-core-coproid_logo.png) +![nf-core-logo](../assets/img/coproid_logo.png) **coproID** (**CO**prolite **ID**entification) is a tool developed at the [Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) From 02317126a6041906144224d224341d088caa2b46 Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 14:07:31 +0200 Subject: [PATCH 76/96] update badge --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cf01290..5c7dbd8 100644 --- a/README.md +++ b/README.md @@ -5,13 +5,13 @@ [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) -[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) +[![install with bioconda](https://img.shields.io/badge/install%20with-conda-brightgreen.svg)](https://docs.conda.io/en/latest) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) ![Singularity Container available](https://img.shields.io/badge/singularity-available-7E4C74.svg) [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) [![Joins us on Slack](https://img.shields.io/badge/slack-nfcore/coproid-blue.svg)](https://nfcore.slack.com/channels/coproid) -[![Published in PeerJ](https://img.shields.io/badge/peerj-published-%2300B2FF)](https://peerj.com/articles/9001) +[![Published in PeerJ](https://img.shields.io/badge/PeerJ-published-%2300B2FF)](https://peerj.com/articles/9001) ## Introduction From 45d069f77d6b13daad60d3ce5657ae697a2ff46d Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 14:23:16 +0200 Subject: [PATCH 77/96] fix markdown linting --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5c7dbd8..271c8b6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ![nf-core/coproid](assets/img/coproid_logo.png) -**A fully reproducible pipeline for COPROlite and paleofeces host IDentification** +## A fully reproducible pipeline for COPROlite and paleofeces host IDentification [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) From 77d69ebaded7ddc851e6fdaa8a1a356a62815ba4 Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 14:27:55 +0200 Subject: [PATCH 78/96] fix bioconda badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 271c8b6..d636432 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A519.10.0-brightgreen.svg)](https://www.nextflow.io/) -[![install with bioconda](https://img.shields.io/badge/install%20with-conda-brightgreen.svg)](https://docs.conda.io/en/latest) +[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](http://bioconda.github.io/) [![Docker](https://img.shields.io/docker/automated/nfcore/coproid.svg)](https://hub.docker.com/r/nfcore/coproid) ![Singularity Container available](https://img.shields.io/badge/singularity-available-7E4C74.svg) [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) From 433329d7d1d7a96109b09a6bd4118b1ad3267228 Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 14:53:11 +0200 Subject: [PATCH 79/96] update logo in multiqc config --- assets/multiqc_config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/assets/multiqc_config.yaml b/assets/multiqc_config.yaml index 4fe81b8..5127dd8 100644 --- a/assets/multiqc_config.yaml +++ b/assets/multiqc_config.yaml @@ -8,7 +8,7 @@ report_comment: > export_plots: true -custom_logo: 'coproid_logo_small.jpg' +custom_logo: 'coproid_logo.png' custom_logo_url: 'https://github.com/nf-core/coproid' custom_logo_title: 'github.com/nf-core/coproid' From c897c32b18e6a2d07142dff1caa4fe98ac8e08e8 Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 15:38:45 +0200 Subject: [PATCH 80/96] fix markdown lint --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index cf01290..9b2e4cc 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,6 +1,6 @@ # ![nf-core/coproid](assets/img/coproid_logo.png) -**A fully reproducible pipeline for COPROlite and paleofeces host IDentification** +## A fully reproducible pipeline for COPROlite and paleofeces host IDentification [![GitHub Actions CI Status](https://github.com/nf-core/coproid/workflows/nf-core%20CI/badge.svg)](https://github.com/nf-core/coproid/actions) [![GitHub Actions Linting Status](https://github.com/nf-core/coproid/workflows/nf-core%20linting/badge.svg)](https://github.com/nf-core/coproid/actions) From 41cb8435ab72577b94d338416dc5f26f333fdeed Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 15:39:01 +0200 Subject: [PATCH 81/96] update logo path --- main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.nf b/main.nf index e4da510..0d75c7c 100644 --- a/main.nf +++ b/main.nf @@ -79,7 +79,7 @@ DEFAULT VARIABLE VALUES SETUP bowtie_setting = '' collapse_setting = '' report_template = "$baseDir/templates/coproID_report.ipynb" -coproid_logo = file("$baseDir/assets/img/coproid_logo_small.jpg") +coproid_logo = file("$baseDir/assets/img/coproid_logo.png") // Show help message From 3d90001b6fe3ae16bab8f865a0e636a026e66f28 Mon Sep 17 00:00:00 2001 From: maxibor Date: Tue, 21 Apr 2020 16:06:06 +0200 Subject: [PATCH 82/96] more logo renaming --- main.nf | 10 ++-------- templates/coproID_report.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 10 deletions(-) diff --git a/main.nf b/main.nf index 0d75c7c..f215bff 100644 --- a/main.nf +++ b/main.nf @@ -339,12 +339,6 @@ SOURCEPREDICT SOURCES AND LABELS sp_labels = file(params.sp_labels, checkIfExists: true) sp_sources = file(params.sp_sources, checkIfExists: true) - -/******************* -coproID logo channel -********************/ -logo = file("$baseDir/assets/img/coproID_nf-core_logo_small.png") - /******************* Logging parameters ********************/ @@ -1178,7 +1172,7 @@ if (params.adna) { input: file(copro_csv) from coproid_res - file(thelogo) from logo + file(thelogo) from file(dplot1) from damage_result_genome1.collect().ifEmpty([]) file(dplot1) from damage_result_genome2.collect().ifEmpty([]) file(dplot3) from damage_result_genome3.collect().ifEmpty([]) @@ -1206,7 +1200,7 @@ if (params.adna) { input: file(copro_csv) from coproid_res - file(thelogo) from logo + file(thelogo) from coproid_logo file(dplot1) from damage_result_genome1.collect().ifEmpty([]) file(dplot1) from damage_result_genome2.collect().ifEmpty([]) file(umap) from sourcepredict_embed_out diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 7013a44..0ea862e 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -28,7 +28,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "[![](coproID_nf-core_logo_small.png)](https://github.com/nf-core/coproID)" + "[![](coproid_logo.png)](https://github.com/nf-core/coproID)" ] }, { @@ -430,4 +430,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file From 52f1782d719a77aeba848f17371e3b30067a6c9a Mon Sep 17 00:00:00 2001 From: maxibor Date: Wed, 22 Apr 2020 12:54:55 +0200 Subject: [PATCH 83/96] coproID report section name update --- templates/coproID_report.ipynb | 40 ++++++++++++++++++++++------------ 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/templates/coproID_report.ipynb b/templates/coproID_report.ipynb index 0ea862e..d534b94 100644 --- a/templates/coproID_report.ipynb +++ b/templates/coproID_report.ipynb @@ -5,7 +5,9 @@ "metadata": {}, "source": [ "# coproID report" - ] + ], + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -29,7 +31,9 @@ "metadata": {}, "source": [ "[![](coproid_logo.png)](https://github.com/nf-core/coproID)" - ] + ], + "execution_count": null, + "outputs": [] }, { "cell_type": "markdown", @@ -40,11 +44,13 @@ "\n", "If you read these lines, coproID successfully finished running and you can find your results below. \n", "You can find more informations about the different result files in the coproID documentation: [coproid.readthedocs.io](https://coproid.readthedocs.io/en/latest/output.html)" - ] + ], + "execution_count": null, + "outputs": [] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -75,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -88,7 +94,7 @@ " organisms = [i.replace(\"normalized_bp_proportion_aligned_\",\"\") for i in list(df.columns) if \"normalized_bp_proportion_aligned_\" in i]\n", " organisms_clean = [i.replace(\"_\",\" \") for i in organisms]\n", " if len(organisms_clean) < 3:\n", - " display(Markdown(\"### Plot\"))\n", + " display(Markdown(\"### coproID summary plot\"))\n", " species_text = pd.DataFrame()\n", " species_text['x'] = [0.25, 0.75, 0.75, 0.25]\n", " species_text['y'] = [0.25, 0.25, 0.75, 0.75]\n", @@ -116,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "tags": [ "remove_cell" @@ -230,9 +236,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1- coproID summary\n", + "## coproID summary\n", "### Table" - ] + ], + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -299,8 +307,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2- microbiome profile embedding" - ] + "## Microbiome profile embedding" + ], + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -320,8 +330,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3- Damage plots" - ] + "## Damage plots" + ], + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -412,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.3-final" }, "toc": { "base_numbering": 1, From 9ea3ace90887f340cb56ed7eb8c6ab173b2be2c0 Mon Sep 17 00:00:00 2001 From: maxibor Date: Wed, 22 Apr 2020 12:55:05 +0200 Subject: [PATCH 84/96] doc update --- README.md | 6 ++--- docs/README.md | 2 +- docs/introduction.md | 6 ++--- docs/output.md | 55 ++++++++++++++++++++++++++++++++++++-------- docs/usage.md | 50 ++++++++++++++++++++++------------------ 5 files changed, 79 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index d636432..c664037 100644 --- a/README.md +++ b/README.md @@ -36,19 +36,17 @@ nextflow run nf-core/coproid -profile test, iv. Start running your own analysis! ```bash -nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker +nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` See [usage docs](docs/usage.md) for all of the available options when running the pipeline. ## Documentation -The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory: - The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory and at the following address: [coproid.readthedocs.io](https://coproid.readthedocs.io) 1. [Installation](https://nf-co.re/usage/installation) -2. [Pipeline configuration +2. Pipeline configuration - [Local installation](https://nf-co.re/usage/local_installation) - [Adding your own system config](https://nf-co.re/usage/adding_own_config) - [Reference genomes](https://nf-co.re/usage/reference_genomes) diff --git a/docs/README.md b/docs/README.md index 9b2e4cc..cb76488 100644 --- a/docs/README.md +++ b/docs/README.md @@ -36,7 +36,7 @@ nextflow run nf-core/coproid -profile test, iv. Start running your own analysis! ```bash -nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker +nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` See [usage docs](docs/usage.md) for all of the available options when running the pipeline. diff --git a/docs/introduction.md b/docs/introduction.md index e2736fa..4aeab61 100644 --- a/docs/introduction.md +++ b/docs/introduction.md @@ -6,18 +6,18 @@ [Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) by [Maxime Borry](https://github.com/maxibor) -The purpose of **coproID** is to help identify the host of given sequence microbiome when there is a doubt between species. +The purpose of **coproID** is to help identify the host of shotgun sequenced gut microbiomes when there is a doubt between species. **coproID** is a pipeline developed using [Nextflow](https://www.nextflow.io/) and made available through [nf-core](https://github.com/nf-core) -Even though it was developed with coprolite host identification in mind, it can be applied to any microbiome, provided they contain host DNA. +Even though it was developed with coprolite and paleofeces host identification in mind, it can be applied to any microbiome, provided they contain host DNA. ## Quick start Example: ```bash -nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' +nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` ## coproID example workFlow diff --git a/docs/output.md b/docs/output.md index 9572496..b7f3479 100644 --- a/docs/output.md +++ b/docs/output.md @@ -1,13 +1,13 @@ # Output This document describes the output produced by the coproID pipeline. +Results are found in the `results` directory (default, specified by `--outdir`). -## Pipeline overview +## MultiQC report -The pipeline is built using [Nextflow](https://www.nextflow.io/) -and processes data using the following steps: +File `multiqc_report.html` -## FastQC +### FastQC section [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your reads. It provides information about the quality score distribution across your reads, the per base sequence content (%T/A/G/C). You get information about adapter contamination and other overrepresented sequences. @@ -15,7 +15,7 @@ For further reading and documentation see the [FastQC help](http://www.bioinform > **NB:** The FastQC plots displayed in the MultiQC report shows _untrimmed_ reads. They may contain adapter sequence and potentially regions with low quality. -### AdapterRemoval +### AdapterRemoval section [AdapterRemoval](https://github.com/MikkelSchubert/adapterremoval) searches for and removes remnant adapter sequences from High-Throughput Sequencing (HTS) data and (optionally) trims low quality bases from the 3' end of reads following adapter removal. AdapterRemoval can analyze both single end and paired end data, and can be used to merge overlapping paired-ended reads into (longer) consensus sequences. @@ -38,6 +38,11 @@ This file contains the coproID report This table summarizes the read ratios and microbiome source proportions as computed by coproID and sourcepredict. You can download the table in `.csv` format by clicking on the green "Download" button. +### coproID summary plot + +This plot summarizes the coproID prediction. +> **Note:** This plot is only available when coproID is used with 2 organisms + ### microbiome profile embedding This interactive plot shows the embedding of the microbiome samples by [sourcepredict](https://github.com/maxibor/sourcepredict) @@ -62,10 +67,6 @@ This directory contains the merged OTU count for all samples of the run, as coun This directory contains all the output files of DamageProfiler (see multiqc section above) -## MultiQC - -[MultiQC](http://multiqc.info) is a visualisation tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in within the report data directory. - ## alignments This directory contains the alignment `.bam` files for aligned and aligned sequences to each target genome. @@ -73,3 +74,39 @@ This directory contains the alignment `.bam` files for aligned and aligned seque ## pmdtools This directory contains the alignment `.bam` files for aligned and aligned **ancient DNA** sequences to each target genome, according to [PMDTools](https://github.com/pontussk/PMDtools). + +## pipeline_info + +This directory contains all the informations about the pipeline run. + +### execution_report.html + +Interactive report showing resources used in the execution of the pipeline + +### execution_timeline.html + +Timeline of pipeline execution + +### execution_trace.txt + +Log of pipeline execution + +### pipeline_dag.svg + +Pipeline workflow overview + +### pipeline_report.html + +nf-core log of pipeline metadata and execution + +### pipeline_report.txt + +Same as above, in text format + +### results_description.html + +The content of this page + +### software_versions.csv + +List of softwares and their versions. diff --git a/docs/usage.md b/docs/usage.md index 43be1a1..29c90de 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -15,7 +15,7 @@ NXF_OPTS='-Xms1g -Xmx4g' The typical command for running the pipeline is as follows: ```bash -nextflow run nf-core/coproid --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/kraken_db' -profile docker +nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles. @@ -188,28 +188,29 @@ params { } ``` -### `--fasta1` +### `--genome2` (using iGenomes) -If you prefer, you can specify the full path to your reference genome when you run the pipeline: +Name of iGenomes reference for candidate organism 2. Must be provided if fasta2 is not provided. +See `--genome1` above for details. ```bash ---fasta1 'path/to/fasta/reference.fa' +--genome2 'CanFam3.1' ``` -### `--fasta2` +### `--fasta1` If you prefer, you can specify the full path to your reference genome when you run the pipeline: ```bash ---fasta2 'path/to/fasta/reference.fa' +--fasta1 'path/to/fasta/reference1.fa' ``` -### `--genome2` (using iGenomes) +### `--fasta2` -Name of iGenomes reference for candidate organism 3. Must be provided if fasta2 is not provided +If you prefer, you can specify the full path to your reference genome when you run the pipeline: ```bash ---genome2 'CanFam3.1' +--fasta2 'path/to/fasta/reference2.fa' ``` ### `--igenomes_ignore` @@ -338,7 +339,7 @@ Proportion of Endogenous DNA in organism 3 target microbiome. Must be between 0 ### `sp_embed` -SourcePredict embedding algorithm. One of mds, tsne, umap. Default to mds +SourcePredict embedding algorithm. One of mds, tsne, umap. Default to mds from coproID version 1.1 ```bash --sp_embed mds @@ -370,46 +371,49 @@ More informations are available in the [Sourcepredict documentation](https://sou ### `--name3` -Name of candidate 1. Example: "Sus_scrofa" +Name of candidate species 3. -### `--fasta3` +`--name3 Sus_scrofa` + +### `--genome3` (using iGenomes) -Path to canidate organism 3 genome fasta file (must be surrounded with quotes). Must be provided if ### \`genome3 is not provided +Name of iGenomes reference for candidate organism 3. Must be provided if `--fasta3` is not provided. +See `--genome1` above for more details. ```bash ---fasta3 'path/to/fasta/reference.fa' +--genome3 'Sscrofa10.2' ``` -### `--genome3` (using iGenomes) +### `--fasta3` -Name of iGenomes reference for candidate organism 3. Must be provided if \`fasta3 is not provided +Path to canidate organism 3 genome fasta file (must be surrounded with quotes). Must be provided if `--genome3` is not provided ```bash ---genome3 'Sscrofa10.2' +--fasta3 'path/to/fasta/reference3.fa' ``` ### `--index1` -Path to Bowtie2 index genome candidate 2 Coprolite maker's genome +Path to Bowtie2 pre-indexed genome candidate 1 Coprolite maker's genome ```bash ---index1 'path/to/bt_index/basename' +--index1 'path/to/bt_index/basename1' ``` ### `--index2` -Path to Bowtie2 index genome candidate 2 Coprolite maker's genome +Path to Bowtie2 pre-indexed genome candidate 2 Coprolite maker's genome ```bash ---index2 'path/to/bt_index/basename' +--index2 'path/to/bt_index/basename2' ``` ### `--index3` -Path to Bowtie2 index genome candidate 3 Coprolite maker's genome +Path to Bowtie2 pre-indexed genome candidate 3 Coprolite maker's genome ```bash ---index3 'path/to/bt_index/basename' +--index3 'path/to/bt_index/basename3' ``` ## Job resources From 2b485a09cc32e36716612375a658a64be41ed201 Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 13:47:18 +0200 Subject: [PATCH 85/96] removed TODO --- .github/workflows/ci.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a68f2f5..b7a2517 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -25,6 +25,4 @@ jobs: docker tag nfcore/coproid:dev nfcore/coproid:dev - name: Run pipeline with test data run: | - # TODO nf-core: You can customise CI pipeline run tests as required - # (eg. adding multiple test runs with different parameters) nextflow run ${GITHUB_WORKSPACE} -profile test,docker From 8164d3cced8aa29a1291cd3f5ff09a4e3ca73ad0 Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 13:55:42 +0200 Subject: [PATCH 86/96] removed unused packages --- environment.yml | 3 --- main.nf | 1 - 2 files changed, 4 deletions(-) diff --git a/environment.yml b/environment.yml index 6dd3b5e..43076b7 100644 --- a/environment.yml +++ b/environment.yml @@ -9,9 +9,6 @@ channels: dependencies: - conda-forge::openblas=0.3.7 - bioconda::adapterremoval=2.3.1 - - bioconda::bcftools=1.10.2 - - bioconda::bedtools=2.29.2 - - bioconda::blast=2.9.0 - conda-forge::bokeh=2.0.1 - bioconda::bowtie2=2.3.5.1 - bioconda::damageprofiler=0.4.9 diff --git a/main.nf b/main.nf index f215bff..f84d350 100644 --- a/main.nf +++ b/main.nf @@ -1270,7 +1270,6 @@ process get_software_versions { multiqc --version > v_multiqc.txt sourcepredict -h > v_sourcepredict.txt samtools --version > v_samtools.txt - bedtools --version > v_bedtools.txt kraken2 --version > v_kraken2.txt bowtie2 --version > v_bowtie2.txt python --version > v_python.txt From d26681805fa96589304c4f0dd5e9bd129c75ef8f Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 13:56:02 +0200 Subject: [PATCH 87/96] add doi and fix name spelling --- README.md | 4 ++-- docs/README.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index c664037..ba62496 100644 --- a/README.md +++ b/README.md @@ -88,7 +88,7 @@ coproID has been published in [peerJ](https://peerj.com/articles/9001). The bibt ## Contributors -[James A. Fellows-Yates](https://github.com/jfy133) +[James A. Fellows Yates](https://github.com/jfy133) ## Tool references @@ -98,6 +98,6 @@ coproID has been published in [peerJ](https://peerj.com/articles/9001). The bibt - **Samtools** Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. [https://doi.org/10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) - **Kraken2** Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. BioRxiv, 762302. [https://doi.org/10.1101/762302](https://doi.org/10.1101/762302) - **PMDTools** Skoglund, P., Northoff, B. H., Shunkov, M. V., Derevianko, A. P., Pääbo, S., Krause, J., & Jakobsson, M. (2014). Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proceedings of the National Academy of Sciences of the United States of America, 111(6), 2229–2234. [https://doi.org/10.1073/pnas.1318934111](https://doi.org/10.1073/pnas.1318934111) -- **DamageProfiler** Judith Neukamm (Unpublished) +- **DamageProfiler** Judith Neukamm (Unpublished): [10.5281/zenodo.1064062](https://doi.org/10.5281/zenodo.1064062) - **Sourcepredict** Borry, M. (2019). Sourcepredict: Prediction of metagenomic sample sources using dimension reduction followed by machine learning classification. The Journal of Open Source Software. [https://doi.org/10.21105/joss.01540](https://doi.org/10.21105/joss.01540) - **MultiQC** Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. [https://doi.org/10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) diff --git a/docs/README.md b/docs/README.md index cb76488..4645233 100644 --- a/docs/README.md +++ b/docs/README.md @@ -90,7 +90,7 @@ coproID has been published in [peerJ](https://peerj.com/articles/9001). The bibt ## Contributors -[James A. Fellows-Yates](https://github.com/jfy133) +[James A. Fellows Yates](https://github.com/jfy133) ## Tool references @@ -100,6 +100,6 @@ coproID has been published in [peerJ](https://peerj.com/articles/9001). The bibt - **Samtools** Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. [https://doi.org/10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) - **Kraken2** Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. BioRxiv, 762302. [https://doi.org/10.1101/762302](https://doi.org/10.1101/762302) - **PMDTools** Skoglund, P., Northoff, B. H., Shunkov, M. V., Derevianko, A. P., Pääbo, S., Krause, J., & Jakobsson, M. (2014). Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proceedings of the National Academy of Sciences of the United States of America, 111(6), 2229–2234. [https://doi.org/10.1073/pnas.1318934111](https://doi.org/10.1073/pnas.1318934111) -- **DamageProfiler** Judith Neukamm (Unpublished) +- **DamageProfiler** Judith Neukamm (Unpublished): [10.5281/zenodo.1064062](https://doi.org/10.5281/zenodo.1064062) - **Sourcepredict** Borry, M. (2019). Sourcepredict: Prediction of metagenomic sample sources using dimension reduction followed by machine learning classification. The Journal of Open Source Software. [https://doi.org/10.21105/joss.01540](https://doi.org/10.21105/joss.01540) - **MultiQC** Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. [https://doi.org/10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) From 67e8ea887f3842e8a0e73625cd1f7cd3dccbccea Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 15:11:20 +0200 Subject: [PATCH 88/96] move introduction --- docs/introduction.md | 47 -------------------------------------------- 1 file changed, 47 deletions(-) delete mode 100644 docs/introduction.md diff --git a/docs/introduction.md b/docs/introduction.md deleted file mode 100644 index 4aeab61..0000000 --- a/docs/introduction.md +++ /dev/null @@ -1,47 +0,0 @@ -# Introduction - -![nf-core-logo](../assets/img/coproid_logo.png) - -**coproID** (**CO**prolite **ID**entification) is a tool developed at the -[Max Planck insitute for the Science of Human History](http://www.shh.mpg.de/en) -by [Maxime Borry](https://github.com/maxibor) - -The purpose of **coproID** is to help identify the host of shotgun sequenced gut microbiomes when there is a doubt between species. - -**coproID** is a pipeline developed using [Nextflow](https://www.nextflow.io/) and made available through [nf-core](https://github.com/nf-core) - -Even though it was developed with coprolite and paleofeces host identification in mind, it can be applied to any microbiome, provided they contain host DNA. - -## Quick start - -Example: - -```bash -nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker -``` - -## coproID example workFlow - -![coproid-dag](../assets/img/coproid_dag.png) - -## How to cite coproID - -coproID has been published in [peerJ](https://peerj.com/articles/9001), the bibtex citation is available below: - -```bibtex -@article{borry_coproid_2020, - title = {{CoproID} predicts the source of coprolites and paleofeces using microbiome composition and host {DNA} content}, - volume = {8}, - issn = {2167-8359}, - url = {https://peerj.com/articles/9001}, - doi = {10.7717/peerj.9001}, - language = {en}, - urldate = {2020-04-20}, - journal = {PeerJ}, - author = {Borry, Maxime and Cordova, Bryan and Perri, Angela and Wibowo, Marsha and Honap, Tanvi Prasad and Ko, Jada and Yu, Jie and Britton, Kate and Girdland-Flink, Linus and Power, Robert C. and Stuijts, Ingelise and Salazar-García, Domingo C. and Hofman, Courtney and Hagan, Richard and Kagoné, Thérèse Samdapawindé and Meda, Nicolas and Carabin, Helene and Jacobson, David and Reinhard, Karl and Lewis, Cecil and Kostic, Aleksandar and Jeong, Choongwon and Herbig, Alexander and Hübner, Alexander and Warinner, Christina}, - month = apr, - year = {2020}, - note = {Publisher: PeerJ Inc.}, - pages = {e9001} -} -``` From 82746d849e08556b2efb39c1620933bb97496d1f Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 15:11:56 +0200 Subject: [PATCH 89/96] move fasta before genomes --- docs/usage.md | 55 ++++++++++++++++++++++++++------------------------- 1 file changed, 28 insertions(+), 27 deletions(-) diff --git a/docs/usage.md b/docs/usage.md index 29c90de..2e422b6 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -123,8 +123,26 @@ The MiniKraken2_v2_8GB database can be downloaded [here](https://ccb.jhu.edu/sof The pipeline config files come bundled with paths to the illumina iGenomes reference index files. If running with docker or AWS, the configuration is set up to use the [AWS-iGenomes](https://ewels.github.io/AWS-iGenomes/) resource. +### `--fasta1` + +Reference genome1 can be specified by using the full path to the genome fasta file. Must be provided if `--genome1` is not provided. + +```bash +--fasta1 'path/to/fasta/reference1.fa' +``` + +### `--fasta2` + +Reference genome2 can be specified by using the full path to the genome fasta file. Must be provided if `--genome2` is not provided. + +```bash +--fasta2 'path/to/fasta/reference2.fa' +``` + ### `--genome1` (using iGenomes) +Alternatively, reference genomes can be specified using pre-index genomes available through the iGenomes service. Must be provided if `--fasta1` is not provided. + There are 31 different species supported in the iGenomes references. To run the pipeline, you must specify which to use with the `--genome` flag. You can find the keys to specify the genomes in the [iGenomes config file](../conf/igenomes.config). Common genomes that are supported are: @@ -190,29 +208,12 @@ params { ### `--genome2` (using iGenomes) -Name of iGenomes reference for candidate organism 2. Must be provided if fasta2 is not provided. -See `--genome1` above for details. +Name of iGenomes reference for candidate organism 2. Must be provided if `--fasta2` is not provided. ```bash --genome2 'CanFam3.1' ``` -### `--fasta1` - -If you prefer, you can specify the full path to your reference genome when you run the pipeline: - -```bash ---fasta1 'path/to/fasta/reference1.fa' -``` - -### `--fasta2` - -If you prefer, you can specify the full path to your reference genome when you run the pipeline: - -```bash ---fasta2 'path/to/fasta/reference2.fa' -``` - ### `--igenomes_ignore` Do not load `igenomes.config` when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in `igenomes.config`. @@ -329,14 +330,6 @@ Proportion of Endogenous DNA in organism 2 target microbiome. Must be between 0 --endo2 0.01 ``` -### `--endo3` - -Proportion of Endogenous DNA in organism 3 target microbiome. Must be between 0 and 1. Default = 0.01 - -```bash ---endo3 0.01 -``` - ### `sp_embed` SourcePredict embedding algorithm. One of mds, tsne, umap. Default to mds from coproID version 1.1 @@ -386,12 +379,20 @@ See `--genome1` above for more details. ### `--fasta3` -Path to canidate organism 3 genome fasta file (must be surrounded with quotes). Must be provided if `--genome3` is not provided +Reference genome3 can be specified by using the full path to the genome fasta file. Must be provided if `--genome3` is not provided. ```bash --fasta3 'path/to/fasta/reference3.fa' ``` +### `--endo3` + +Proportion of Endogenous DNA in organism 3 target microbiome. Must be between 0 and 1. Default = 0.01 + +```bash +--endo3 0.01 +``` + ### `--index1` Path to Bowtie2 pre-indexed genome candidate 1 Coprolite maker's genome From 7896fca7607793aa82c881e3dfb64a5a7b528f55 Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 15:12:06 +0200 Subject: [PATCH 90/96] rename introduction --- docs/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index 7b67a5b..f0c4ce6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -10,7 +10,7 @@ Welcome to coproID's documentation! :maxdepth: 2 :caption: Contents: - introduction + README installation configuration usage From a6ddc484cc1909e46b1691e321d69dee7751e476 Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 15:14:02 +0200 Subject: [PATCH 91/96] add coproid method overview --- docs/README.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/docs/README.md b/docs/README.md index 4645233..7913fa6 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,4 +1,6 @@ -# ![nf-core/coproid](assets/img/coproid_logo.png) +# Introduction + + ![nf-core/coproid](../assets/img/coproid_logo.png) ## A fully reproducible pipeline for COPROlite and paleofeces host IDentification @@ -13,11 +15,17 @@ [![Joins us on Slack](https://img.shields.io/badge/slack-nfcore/coproid-blue.svg)](https://nfcore.slack.com/channels/coproid) [![Published in PeerJ](https://img.shields.io/badge/peerj-published-%2300B2FF)](https://peerj.com/articles/9001) -## Introduction - **CoproID** helps you to identify the _"true maker"_ of Illumina sequenced Coprolites/Paleofaeces by checking the microbiome composition and the endogenous DNA. -The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. +It combines the analysis of putative host ancient DNA with a machine learning prediction of the feces source based on microbiome taxonomic composition: + +- (**A**) First coproID performs a comparative mapping of all reads agains two (or three) target genomes (genome1, genome2, and eventually genome3) and computes a host-DNA species ratio (*NormalizedRatio*) +- (**B**) Then coproID performs a metagenomic taxonomic profiling, and compares the obtained profiles to modern reference samples of the target species metagenomes. Using [machine learning](https://joss.theoj.org/papers/10.21105/joss.01540), coproID then estimates the host source from the metagenomic taxonomic composition (*prop_microbiome*). +- Finally, coproID combines **A** and **B** to predict the likely host of the metagenomic sample. + +The coproID pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. + +A detailed description of coproID can be found in the [article published in PeerJ](https://peerj.com/articles/9001). ## Quick Start @@ -39,6 +47,8 @@ iv. Start running your own analysis! nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` +> NB: The example above assumes access to [iGenomes](https://nf-co.re/usage/reference_genomes). + See [usage docs](docs/usage.md) for all of the available options when running the pipeline. ## Documentation @@ -48,7 +58,7 @@ The nf-core/coproid pipeline comes with documentation about the pipeline, found The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory and at the following address: [coproid.readthedocs.io](https://coproid.readthedocs.io) 1. [Installation](https://nf-co.re/usage/installation) -2. [Pipeline configuration +2. Pipeline configuration - [Local installation](https://nf-co.re/usage/local_installation) - [Adding your own system config](https://nf-co.re/usage/adding_own_config) - [Reference genomes](https://nf-co.re/usage/reference_genomes) From ac1e01eacf75bd5a7d78dbc30e4ae6f9f6e31657 Mon Sep 17 00:00:00 2001 From: maxibor Date: Thu, 23 Apr 2020 15:20:47 +0200 Subject: [PATCH 92/96] harmonizing readmes --- README.md | 20 ++++++++++++++++---- docs/README.md | 4 +++- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ba62496..ea14f85 100644 --- a/README.md +++ b/README.md @@ -11,13 +11,19 @@ [![Documentation Status](https://readthedocs.org/projects/coproid/badge/?version=latest)](https://coproid.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2653756.svg)](https://doi.org/10.5281/zenodo.2653756) [![Joins us on Slack](https://img.shields.io/badge/slack-nfcore/coproid-blue.svg)](https://nfcore.slack.com/channels/coproid) -[![Published in PeerJ](https://img.shields.io/badge/PeerJ-published-%2300B2FF)](https://peerj.com/articles/9001) - -## Introduction +[![Published in PeerJ](https://img.shields.io/badge/peerj-published-%2300B2FF)](https://peerj.com/articles/9001) **CoproID** helps you to identify the _"true maker"_ of Illumina sequenced Coprolites/Paleofaeces by checking the microbiome composition and the endogenous DNA. -The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. +It combines the analysis of putative host ancient DNA with a machine learning prediction of the feces source based on microbiome taxonomic composition: + +- (**A**) First coproID performs a comparative mapping of all reads agains two (or three) target genomes (genome1, genome2, and eventually genome3) and computes a host-DNA species ratio (*NormalizedRatio*) +- (**B**) Then coproID performs a metagenomic taxonomic profiling, and compares the obtained profiles to modern reference samples of the target species metagenomes. Using [machine learning](https://joss.theoj.org/papers/10.21105/joss.01540), coproID then estimates the host source from the metagenomic taxonomic composition (*prop_microbiome*). +- Finally, coproID combines **A** and **B** to predict the likely host of the metagenomic sample. + +The coproID pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. + +A detailed description of coproID can be found in the [article published in PeerJ](https://peerj.com/articles/9001). ## Quick Start @@ -39,10 +45,16 @@ iv. Start running your own analysis! nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` +This command runs coproID to estimate whether the source of test samples (`--reads '*_R{1,2}.fastq.gz'`) are coming from a human (`--genome1 'GRCh37' -name1 'Homo_sapiens'`) or a dog (`--genome2 'CanFam3.1' --name2 'Canis_familiaris'`), and specifies the path to the minikraken database (`--krakendb 'path/to/minikraken_db'`). + +> NB: The example above assumes access to [iGenomes](https://nf-co.re/usage/reference_genomes). + See [usage docs](docs/usage.md) for all of the available options when running the pipeline. ## Documentation +The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory: + The nf-core/coproid pipeline comes with documentation about the pipeline, found in the `docs/` directory and at the following address: [coproid.readthedocs.io](https://coproid.readthedocs.io) 1. [Installation](https://nf-co.re/usage/installation) diff --git a/docs/README.md b/docs/README.md index 7913fa6..9c58200 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,6 +1,6 @@ # Introduction - ![nf-core/coproid](../assets/img/coproid_logo.png) +![nf-core/coproid](../assets/img/coproid_logo.png) ## A fully reproducible pipeline for COPROlite and paleofeces host IDentification @@ -47,6 +47,8 @@ iv. Start running your own analysis! nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` +This command runs coproID to estimate whether the source of test samples (`--reads '*_R{1,2}.fastq.gz'`) are coming from a human (`--genome1 'GRCh37' -name1 'Homo_sapiens'`) or a dog (`--genome2 'CanFam3.1' --name2 'Canis_familiaris'`), and specifies the path to the minikraken database (`--krakendb 'path/to/minikraken_db'`). + > NB: The example above assumes access to [iGenomes](https://nf-co.re/usage/reference_genomes). See [usage docs](docs/usage.md) for all of the available options when running the pipeline. From 180805b9afa6147c27ce2745327eb7d7009dded0 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 24 Apr 2020 13:53:46 +0200 Subject: [PATCH 93/96] fasta3 above genome3 --- docs/usage.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/usage.md b/docs/usage.md index 2e422b6..9872ac0 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -368,21 +368,21 @@ Name of candidate species 3. `--name3 Sus_scrofa` -### `--genome3` (using iGenomes) +### `--fasta3` -Name of iGenomes reference for candidate organism 3. Must be provided if `--fasta3` is not provided. -See `--genome1` above for more details. +Reference genome3 can be specified by using the full path to the genome fasta file. Must be provided if `--genome3` is not provided. ```bash ---genome3 'Sscrofa10.2' +--fasta3 'path/to/fasta/reference3.fa' ``` -### `--fasta3` +### `--genome3` (using iGenomes) -Reference genome3 can be specified by using the full path to the genome fasta file. Must be provided if `--genome3` is not provided. +Name of iGenomes reference for candidate organism 3. Must be provided if `--fasta3` is not provided. +See `--genome1` above for more details. ```bash ---fasta3 'path/to/fasta/reference3.fa' +--genome3 'Sscrofa10.2' ``` ### `--endo3` From 53e63926eedb47ef9f45b0789d0053934d26fc93 Mon Sep 17 00:00:00 2001 From: maxibor Date: Fri, 24 Apr 2020 14:03:04 +0200 Subject: [PATCH 94/96] add more detailed description of multiqc output --- docs/output.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/output.md b/docs/output.md index b7f3479..6219a13 100644 --- a/docs/output.md +++ b/docs/output.md @@ -19,6 +19,9 @@ For further reading and documentation see the [FastQC help](http://www.bioinform [AdapterRemoval](https://github.com/MikkelSchubert/adapterremoval) searches for and removes remnant adapter sequences from High-Throughput Sequencing (HTS) data and (optionally) trims low quality bases from the 3' end of reads following adapter removal. AdapterRemoval can analyze both single end and paired end data, and can be used to merge overlapping paired-ended reads into (longer) consensus sequences. +- *Retained and Discarded Paired-End Collapsed*: This plot shows the number/proportion of reads that passed adapter removal and trimming filters. +- *Length Distribution Paired End Collapsed*: This plot shows the length distribution of the different read categories. + ### Bowtie2 [Bowtie 2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. @@ -29,6 +32,10 @@ This plot shows the number of reads aligning to the reference in different ways. [DamageProfiler](https://github.com/Integrative-Transcriptomics/DamageProfiler) calculates damage profiles of mapped reads. These plots represents the damage patterns and read length distribution. +### nf-core/coproid Software Versions + +This section shows the version of the different softwares used in this pipeline. + ## coproID_report.html This file contains the coproID report From a561efb5ceaf84b4ab1fc668fff2493a6d6c45b9 Mon Sep 17 00:00:00 2001 From: Maxime Date: Sun, 26 Apr 2020 15:37:38 +0200 Subject: [PATCH 95/96] bump version to 1.1 --- .github/workflows/ci.yml | 2 +- CHANGELOG.md | 2 +- Dockerfile | 6 +++--- environment.yml | 2 +- nextflow.config | 4 ++-- 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b7a2517..356b5bc 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -22,7 +22,7 @@ jobs: - name: Pull docker image run: | docker pull nfcore/coproid:dev - docker tag nfcore/coproid:dev nfcore/coproid:dev + docker tag nfcore/coproid:dev nfcore/coproid:1.1 - name: Run pipeline with test data run: | nextflow run ${GITHUB_WORKSPACE} -profile test,docker diff --git a/CHANGELOG.md b/CHANGELOG.md index 878f7cc..54bc43c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # nf-core/coproid: Changelog -## v1.1dev +## v1.1 - Update mapped basepair count to be quicker and include it in report [#14](https://github.com/nf-core/coproid/pull/14) - Remove outdated scripts [#14](https://github.com/nf-core/coproid/pull/14) diff --git a/Dockerfile b/Dockerfile index 3f46808..5faa963 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,11 +5,11 @@ LABEL authors="Maxime Borry" \ # Install the conda environment COPY environment.yml / RUN conda env create -f /environment.yml && conda clean -a -RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml -ENV PATH /opt/conda/envs/nf-core-coproid-1.1dev/bin:$PATH +RUN conda env export --name nf-core-coproid-1.1 > nf-core-coproid-1.1.yml +ENV PATH /opt/conda/envs/nf-core-coproid-1.1/bin:$PATH # Dump the details of the installed packages to a file for posterity -RUN conda env export --name nf-core-coproid-1.1dev > nf-core-coproid-1.1dev.yml +RUN conda env export --name nf-core-coproid-1.1 > nf-core-coproid-1.1.yml # Numba cache dir patch ENV NUMBA_CACHE_DIR /tmp diff --git a/environment.yml b/environment.yml index 43076b7..cb67af9 100644 --- a/environment.yml +++ b/environment.yml @@ -1,6 +1,6 @@ # You can use this file to create a conda environment for this pipeline: # conda env create -f environment.yml -name: nf-core-coproid-1.1dev +name: nf-core-coproid-1.1 channels: - conda-forge - maxibor diff --git a/nextflow.config b/nextflow.config index 668776e..376d4b7 100644 --- a/nextflow.config +++ b/nextflow.config @@ -66,7 +66,7 @@ params { } // Container slug. Stable releases should specify release tag! // Developmental code should specify :dev -process.container = 'nfcore/coproid:dev' +process.container = 'nfcore/coproid:1.1' // Load base.config by default for all pipelines includeConfig 'conf/base.config' @@ -133,7 +133,7 @@ manifest { description = 'Coprolite Identification' mainScript = 'main.nf' nextflowVersion = '>=19.10.0' - version = '1.1dev' + version = '1.1' } // Function to ensure that resource requirements don't go beyond From 6821314aab1066f727c0f25c42e75c9af412e8cc Mon Sep 17 00:00:00 2001 From: Maxime Borry Date: Wed, 29 Apr 2020 12:27:47 +0200 Subject: [PATCH 96/96] Update docs/usage.md Pipeline remote changed to nf-core Co-Authored-By: James A. Fellows Yates --- docs/usage.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/usage.md b/docs/usage.md index 9872ac0..370d9aa 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -15,7 +15,7 @@ NXF_OPTS='-Xms1g -Xmx4g' The typical command for running the pipeline is as follows: ```bash -nextflow run maxibor/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker +nextflow run nf-core/coproid --genome1 'GRCh37' --genome2 'CanFam3.1' --name1 'Homo_sapiens' --name2 'Canis_familiaris' --reads '*_R{1,2}.fastq.gz' --krakendb 'path/to/minikraken_db' -profile docker ``` This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.

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