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How to use morf_name method in autotest

Best Python code snippet using autotest_python

coverage.py

Source:coverage.py

...745    def find_def_coverage(self, morf, statements, missing, definfo):746        """Return mapping from function name to coverage.747        """748        def_coverage = {}749        root = self.morf_name(morf)750        statements = set(statements)751        missing = set(missing)752        for info in definfo:753            if info.codestart is None:754                info.coverage = 1755            else:756                lines = set(range(info.codestart, info.end+1))757                stmt = len(lines.intersection(statements))758                miss = len(lines.intersection(missing))759                if miss == 0: info.cover = 1760                else: info.coverage = (1.0 - float(miss)/float(stmt))761    def relative_filename(self, filename):762        """ Convert filename to relative filename from self.relative_dir.763        """764        return filename.replace(self.relative_dir, "")765    def morf_name(self, morf):766        """ Return the name of morf as used in report.767        """768        if isinstance(morf, types.ModuleType):769            return morf.__name__770        else:771            return self.relative_filename(os.path.splitext(morf)[0])772    def filter_by_prefix(self, morfs, omit_prefixes):773        """ Return list of morfs where the morf name does not begin774            with any one of the omit_prefixes.775        """776        filtered_morfs = []777        for morf in morfs:778            for prefix in omit_prefixes:779                if self.morf_name(morf).startswith(prefix):780                    break781            else:782                filtered_morfs.append(morf)783        return filtered_morfs784    def morf_name_compare(self, x, y):785        return cmp(self.morf_name(x), self.morf_name(y))786    def report(self, morfs, show_missing=1, ignore_errors=0, file=None, omit_prefixes=[]):787        if not isinstance(morfs, types.ListType):788            morfs = [morfs]789        # On windows, the shell doesn't expand wildcards.  Do it here.790        globbed = []791        for morf in morfs:792            if isinstance(morf, strclass):793                globbed.extend(glob.glob(morf))794            else:795                globbed.append(morf)796        morfs = globbed797        798        morfs = self.filter_by_prefix(morfs, omit_prefixes)799        morfs.sort(self.morf_name_compare)800        max_name = max([5,] + map(len, map(self.morf_name, morfs)))801        fmt_name = "%%- %ds  " % max_name802        fmt_err = fmt_name + "%s: %s"803        header = fmt_name % "Name" + " Stmts   Exec  Cover"804        fmt_coverage = fmt_name + "% 6d % 6d % 5d%%"805        if show_missing:806            header = header + "   Missing"807            fmt_coverage = fmt_coverage + "   %s"808        if not file:809            file = sys.stdout810        print >>file, header811        print >>file, "-" * len(header)812        total_statements = 0813        total_executed = 0814        for morf in morfs:815            name = self.morf_name(morf)816            try:817                _, statements, _, missing, readable  = self.analysis2(morf)818                n = len(statements)819                m = n - len(missing)820                if n > 0:821                    pc = 100.0 * m / n822                else:823                    pc = 100.0824                args = (name, n, m, pc)825                if show_missing:826                    args = args + (readable,)827                print >>file, fmt_coverage % args828                total_statements = total_statements + n829                total_executed = total_executed + m...

newcompute_features.py

Source:newcompute_features.py

1#Copyright MIT License, 2017 Armaghan Naik, Pieter Spealman2#PS 08.18.18 - suppressing ignorable warnings3# https://github.com/numpy/numpy/pull/4324import warnings5warnings.simplefilter("ignore")6import string7import os8import numpy9import scipy10import scipy.stats11import sys12import simplejson as json13uorf_identifier = ['chr', 'morf_name', 'ss_start', 'ss_end', 'polarity']14SETTINGS = json.load(open('analysis.json','r'))15crappy_starts = set(SETTINGS['ignore_start_codons'])16def load_a_sample(kindof, aname):17	predictions = []18	evidence = []19	for achr in SETTINGS['chromosomes']:20		try:21			predictions.extend([x for x in json.load(open('predictions.'+kindof+aname+'/'+achr+'.predictions')) if x['uorf_sequence'][:3] not in crappy_starts])22		except json.decoder.JSONDecodeError:23			pass24		try:25			evidence.extend([x for x in json.load(open('predictions.'+kindof+aname+'/'+achr+'.evidence')) if x['uorf_sequence'][:3] not in crappy_starts])26		except json.decoder.JSONDecodeError:27			pass28	# load up abundance calculations29	orf_transcript_estimate = {}30	sname = [x['mRNA'] for x in SETTINGS['SAMPLES'] if x['name']==aname][0].split('/')[-1]31	for line in open(SETTINGS['processed_dir']+sname+'-quantified.csv'):32		Line = line.rstrip().split()33		orf_transcript_estimate[Line[0]] = float(Line[1])34	#35	orf_loading_estimates_lookup = {}36	sname = [x['RPF'] for x in SETTINGS['SAMPLES'] if x['name']==aname][0].split('/')[-1]37	for line in open(SETTINGS['processed_dir']+sname+'-quantified.csv'):38		Line = line.rstrip().split()39		orf_loading_estimates_lookup[Line[0]] = float(Line[1])40	return predictions, evidence, orf_transcript_estimate, orf_loading_estimates_lookup41feat_order = []42designfeats = ['ss_start', 'ss_end', 'max_power_freq', 'entropy_of_power', 'median_phase', 'median_power', 'morf_5putr_peak_phase', 'morf_5putr_peak_power', 'morf_5putr_region_power', 'morf_5putr_sum_downstream', 'morf_5putr_sum_upstream', 'morf_dist_TIS', 'morf_dist_TSS', 'within_phase_of_in_frame', 'within_phase_of_max_power_freq', 'within_power_of_in_frame', 'within_power_of_max_power_freq', 'protection_sum', 'pwm_score', 'relative_start_magnitude', 'weighted_avg_phase', 'morf_nterm_region_ends_before', 'morf_nterm_region_start_after', 'morf_nonoverlap','morf_len_TLS',  'within_power_of_max_power_freq', 'within_power_of_in_frame']43dfidx = {x[1]:x[0] for x in enumerate(designfeats)}44keepfeats = ['within_power_of_max_power_freq', 'within_power_of_in_frame',  'morf_dist_TIS', 'morf_dist_TSS', 'morf_nterm_region_ends_before', 'morf_nterm_region_start_after', 'morf_nonoverlap']45def compute_derived_features(rep_data, orf_transcript_ests, orf_loading_ests):46	n = len(rep_data)47	X = numpy.zeros((n,len(designfeats)))48	QQ = numpy.zeros((n,))49	total_rpf_depth = float(orf_loading_ests['ALL_READS'])50	ii = 051	which_key = []52	for aval in rep_data:53		if aval['morf_name'] in orf_transcript_ests:54			X[ii,:] = [float(aval[z]) for z in designfeats]55			QQ[ii] = orf_transcript_ests[aval['morf_name']]56			if (QQ[ii] + X[ii,dfidx['morf_5putr_region_power']]) >= 20 and QQ[ii]>0:57				ii += 158				which_key.append([aval[x] for x in uorf_identifier])59	X = X[:ii,:]60	QQ = QQ[:ii]61	n = ii62	# normalize mRNA relative abundances63	QQ /= float(orf_transcript_ests['ALL_READS'])64	synfeats = {}65	for k in keepfeats:66		synfeats[k] = X[:,dfidx[k]] 67	mylengths = numpy.abs(X[:,dfidx['ss_end']]-X[:,dfidx['ss_start']])68	synfeats['spacing_of_max_power'] = X[:,dfidx['max_power_freq']]/mylengths69	synfeats['relative_power_of_max_power_freq'] = X[:,dfidx['within_power_of_max_power_freq']]/QQ70	synfeats['relative_power_of_in_frame'] = X[:,dfidx['within_power_of_in_frame']]/QQ71	synfeats['relative_median_power'] = X[:,dfidx['median_power']]/QQ72	#73	synfeats['region_relative_morf_peak_power']= X[:,dfidx['morf_5putr_peak_power']]/QQ	74	#75	synfeats['abs_phase_of_max_power_freq'] = X[:,dfidx['within_phase_of_max_power_freq']]76	synfeats['abs_phase_of_in_frame'] = X[:,dfidx['within_phase_of_in_frame']]77	#78	synfeats['length_of_puorf'] = mylengths79	#80	#81	avg_upstream_reads = X[:,dfidx['morf_5putr_sum_upstream']]/X[:,dfidx['morf_dist_TSS']]/QQ82	avg_upstream_reads[numpy.isnan(avg_upstream_reads)] = 083	synfeats['relative_avg_upstream_reads'] = avg_upstream_reads84	#85	avg_downstream_reads = X[:,dfidx['morf_5putr_sum_downstream']]/X[:,dfidx['morf_dist_TIS']]/QQ86	avg_downstream_reads[numpy.isnan(avg_downstream_reads)] = 087	synfeats['relative_avg_downstream_reads'] = avg_downstream_reads88	synfeats['normalized_start_magnitude'] = X[:,dfidx['relative_start_magnitude']]/total_rpf_depth89	# print feat_order90	synfeatorder = sorted(synfeats.keys())91	if len(feat_order)==0:92		feat_order.extend(synfeatorder)93	else:94		synfeatorder = feat_order95	newX = numpy.vstack([synfeats[x] for x in synfeatorder]).T96	# import pdb97	# pdb.set_trace()98	return which_key, newX, X99for kindof in ['']+['p'+str(i)+'-' for i in range(len(SETTINGS['permutation_seeds']))]:100	outdir = 'calculated'+kindof101	if not os.path.exists(outdir):102		os.mkdir(outdir)103	for asample in SETTINGS['SAMPLES']:104		sname = asample['name']105		print asample, sname106		predictions, evidence, orf_transcript_estimate, orf_loading_estimates_lookup = load_a_sample(kindof, sname)107		which_key, newX, X = compute_derived_features(predictions, orf_transcript_estimate, orf_loading_estimates_lookup)108		fout = open(outdir+'/'+sname+'.table','w')109		for x in which_key:110			fout.write(' '.join([str(z) for z in x])+"\n")111		fout.close()112		numpy.save(outdir+'/'+sname+'-feats.npy', newX)113		numpy.save(outdir+'/'+sname+'-design-feats.npy', X)114		sname = asample['name']+'-evidence'115		which_key, newX, X = compute_derived_features(evidence, orf_transcript_estimate, orf_loading_estimates_lookup)116		fout = open(outdir+'/'+sname+'.table','w')117		for x in which_key:118			fout.write(' '.join([str(z) for z in x])+"\n")119		fout.close()120		numpy.save(outdir+'/'+sname+'-feats.npy', newX)121		numpy.save(outdir+'/'+sname+'-design-feats.npy', X)122	fout = open('here.forder','w')123	for x in feat_order:124		fout.write(x+"\n")125	fout.close()126	fout = open('here.dorder','w')127	for x in designfeats:128		fout.write(x+"\n")...

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