Port to Python 3 and pure python

bin/nucleoatac

@@ -24,14 +24,14 @@ from nucleoatac.cli import nucleoatac_parser, nucleoatac_main
 
 
 if __name__ == '__main__':
-    print "Command run:  "+ ' '.join(map(str,sys.argv))
-    print "nucleoatac version " +  __version__
-    print "start run at: " + time.strftime("%Y-%m-%d %H:%M")
+    print("Command run:  "+ ' '.join(map(str,sys.argv)))
+    print("nucleoatac version " +  __version__)
+    print("start run at: " + time.strftime("%Y-%m-%d %H:%M"))
     try:
         parser = nucleoatac_parser()
         args = parser.parse_args()
         nucleoatac_main(args)
-        print "end run at: " + time.strftime("%Y-%m-%d %H:%M")
+        print("end run at: " + time.strftime("%Y-%m-%d %H:%M"))
     except KeyboardInterrupt:
         sys.stderr.write("User interrupted nucleoatac.")
         sys.exit(0)

bin/pyatac

@@ -23,14 +23,14 @@ from pyatac.cli import pyatac_parser, pyatac_main
 
 
 if __name__ == '__main__':
-    print "Command run:  "+ ' '.join(map(str,sys.argv))
-    print "pyatac version "+__version__
-    print "start run at: " + time.strftime("%Y-%m-%d %H:%M")
+    print("Command run:  "+ ' '.join(map(str,sys.argv)))
+    print("pyatac version "+__version__)
+    print("start run at: " + time.strftime("%Y-%m-%d %H:%M"))
     try:
         parser = pyatac_parser()
         args = parser.parse_args()
         pyatac_main(args)
-        print "end run at: " + time.strftime("%Y-%m-%d %H:%M")
+        print("end run at: " + time.strftime("%Y-%m-%d %H:%M"))
     except KeyboardInterrupt:
         sys.stderr.write("User interrupted pyatac.")
         sys.exit(0)

nucleoatac/NFRCalling.py

@@ -89,7 +89,7 @@ def findNFRs(self):
         if self.chrom in tbx.contigs:
             for row in tbx.fetch(self.chrom, self.start, self.end, parser = pysam.asTuple()):
                 nucs.append(int(row[1]))
-        for j in xrange(1,len(nucs)):
+        for j in range(1,len(nucs)):
             left = nucs[j-1] + 73
             right = nucs[j] - 72
             if right <= left:
@@ -106,7 +106,7 @@ def process(self, params):
         self.findNFRs()
     def removeData(self):
         """remove data from chunk-- deletes all attributes"""
-        names = self.__dict__.keys()
+        names = list(self.__dict__.keys())
         for name in names:
             delattr(self,name)
 

nucleoatac/NucleosomeCalling.py

@@ -61,7 +61,7 @@ def calculateBackgroundSignal(self, mat, vmat, nuc_cov):
                                                          self.bias_mat.start + offset,
                                                          self.bias_mat.end - offset),
                                        vmat.mat,mode = 'valid')[0]
-        self.vals = self.vals * self.nuc_cov/ self.cov.vals
+        self.vals = self.vals * self.nuc_cov // self.cov.vals
 
 
 
@@ -79,7 +79,7 @@ def simulateReads(self):
         sim_mat = np.reshape(sim_vect, self.vmat.mat.shape)
         return sim_mat
     def simulateDist(self, numiters = 1000):
-        self.scores = map(lambda x: np.sum(self.simulateReads() * self.vmat.mat),range(numiters))
+        self.scores = [np.sum(self.simulateReads() * self.vmat.mat) for x in range(numiters)]
     def analStd(self):
         flatv = np.ravel(self.vmat.mat)
         var = calculateCov(self.probs, flatv, self.reads)
@@ -91,8 +91,7 @@ def analMean(self):
 
 def norm(x, v, w, mean):
     """compute values of normal pdf with given mean and sd at values in x"""
-    norm = (1.0/(np.sqrt(2*np.pi*v)) *
-        np.exp(-(x - mean)**2/(2*v)))
+    norm = (1.0/(np.sqrt(2*np.pi*v)) * np.exp(-(x - mean)**2/(2*v)))
     norm = norm * (w/max(norm))
     return norm
 
@@ -139,7 +138,7 @@ def addNorms(x,params):
             """Add several normal distributions together"""
             l = len(x)
             fit = np.zeros(l)
-            i = len(params)/3
+            i = len(params)//3
             for j in range(i):
                 fit += norm(x,params[j*3],params[3*j+1],params[3*j+2])
             return fit
@@ -156,21 +155,21 @@ def fitNorm(guess, bound, sig):
         allnucs = nuctrack.sorted_nuc_keys
         x = bisect_left(allnucs,index)
         if x == 0:
-            left = index - nuctrack.params.nonredundant_sep/3
-            means = (nuctrack.params.nonredundant_sep/3,)
+            left = index - nuctrack.params.nonredundant_sep//3
+            means = (nuctrack.params.nonredundant_sep//3,)
         elif index - allnucs[x-1] < nuctrack.params.nonredundant_sep:
             left = allnucs[x-1]
             means = (index - allnucs[x-1],0)
         else:
-            left = index - nuctrack.params.nonredundant_sep/3
-            means = (nuctrack.params.nonredundant_sep/3,)
+            left = index - nuctrack.params.nonredundant_sep//3
+            means = (nuctrack.params.nonredundant_sep//3,)
         if x == len(allnucs)-1:
-            right = index + nuctrack.params.nonredundant_sep/3 + 1
+            right = index + nuctrack.params.nonredundant_sep//3 + 1
         elif allnucs[x+1] - index < nuctrack.params.nonredundant_sep:
             right = allnucs[x+1]
             means += (allnucs[x+1] - left,)
         else:
-            right = index + nuctrack.params.nonredundant_sep/3 +1
+            right = index + nuctrack.params.nonredundant_sep//3 +1
         sig = nuctrack.smoothed.vals[left:right]
         sig[sig<0] = 0
         if len(means)==1:
@@ -237,14 +236,14 @@ def __init__(self, chunk):
     def initialize(self, parameters):
         self.params = parameters
     def getFragmentMat(self):
-        self.mat = FragmentMat2D(self.chrom, self.start - max(self.params.window,self.params.upper/2+1),
-                                 self.end + max(self.params.window,self.params.upper/2+1), 0, self.params.upper, atac = self.params.atac)
+        self.mat = FragmentMat2D(self.chrom, self.start - max(self.params.window,self.params.upper//2+1),
+                                 self.end + max(self.params.window,self.params.upper//2+1), 0, self.params.upper, atac = self.params.atac)
         self.mat.makeFragmentMat(self.params.bam)
     def makeBiasMat(self):
         self.bias_mat = BiasMat2D(self.chrom, self.start - self.params.window,
                                  self.end + self.params.window, 0, self.params.upper)
-        bias_track = InsertionBiasTrack(self.chrom, self.start - self.params.window - self.params.upper/2,
-                                  self.end + self.params.window + self.params.upper/2 + 1, log = True)
+        bias_track = InsertionBiasTrack(self.chrom, self.start - self.params.window - self.params.upper//2,
+                                  self.end + self.params.window + self.params.upper//2 + 1, log = True)
         if self.params.fasta is not None:
             bias_track.computeBias(self.params.fasta, self.params.chrs, self.params.pwm)
             self.bias_mat.makeBiasMat(bias_track)
@@ -298,7 +297,7 @@ def findAllNucs(self):
         #find peaks in normalized sigal
         cands1 = call_peaks(combined, min_signal = 0,
                                 sep = self.params.redundant_sep,
-                                boundary = self.params.nonredundant_sep/2, order = self.params.redundant_sep/2)
+                                boundary = self.params.nonredundant_sep//2, order = self.params.redundant_sep//2)
         for i in cands1:
             nuc = Nucleosome(i + self.start, self)
             if nuc.nuc_cov > self.params.min_reads:
@@ -310,7 +309,7 @@ def findAllNucs(self):
                         self.nuc_collection[i] = nuc
         self.sorted_nuc_keys = np.array(sorted(self.nuc_collection.keys()))
         self.nonredundant = reduce_peaks( self.sorted_nuc_keys,
-                                            map(lambda x: self.nuc_collection[x].z, self.sorted_nuc_keys),
+                                            [self.nuc_collection[x].z for x in self.sorted_nuc_keys],
                                                 self.params.nonredundant_sep)
         self.redundant = np.setdiff1d(self.sorted_nuc_keys, self.nonredundant)
     def fit(self):
@@ -340,7 +339,7 @@ def process(self, params):
         self.makeInsertionTrack()
     def removeData(self):
         """remove data from chunk-- deletes all attributes"""
-        names = self.__dict__.keys()
+        names = list(self.__dict__.keys())
         for name in names:
             delattr(self,name)
 

nucleoatac/Occupancy.py

@@ -8,7 +8,6 @@
 from scipy import signal, optimize, stats
 import numpy as np
 import matplotlib.pyplot as plt
-import pyximport; pyximport.install(setup_args={"include_dirs":np.get_include()})
 from pyatac.fragmentsizes import FragmentSizes
 from pyatac.tracks import Track, CoverageTrack
 from pyatac.chunk import Chunk
@@ -23,9 +22,11 @@ class FragmentMixDistribution:
     def __init__(self,  lower = 0, upper =2000):
         self.lower = lower
         self.upper = upper
+
     def getFragmentSizes(self, bamfile, chunklist = None):
         self.fragmentsizes = FragmentSizes(self.lower, self.upper)
         self.fragmentsizes.calculateSizes(bamfile, chunks = chunklist)
+
     def modelNFR(self, boundaries = (35,115)):
         """Model NFR distribution with gamma distribution"""
         b = np.where(self.fragmentsizes.get(self.lower,boundaries[1]) == max(self.fragmentsizes.get(self.lower,boundaries[1])))[0][0] + self.lower
@@ -64,14 +65,15 @@ def gamma_fit(X,o,p):
                             self.nfr_fit.get(boundaries[1],self.upper)))
         nuc[nuc<=0]=min(min(nfr)*0.1,min(nuc[nuc>0])*0.001)
         self.nuc_fit = FragmentSizes(self.lower, self.upper, vals = nuc)
+
     def plotFits(self,filename=None):
         """plot the Fits"""
         fig = plt.figure()
-        plt.plot(range(self.lower,self.upper),self.fragmentsizes.get(),
+        plt.plot(list(range(self.lower,self.upper)),self.fragmentsizes.get(),
                  label = "Observed")
-        plt.plot(range(self.lower,self.upper),self.nfr_fit0.get(), label = "NFR Fit")
-        plt.plot(range(self.lower,self.upper),self.nuc_fit.get(), label = "Nucleosome Model")
-        plt.plot(range(self.lower,self.upper),self.nfr_fit.get(), label = "NFR Model")
+        plt.plot(list(range(self.lower,self.upper)),self.nfr_fit0.get(), label = "NFR Fit")
+        plt.plot(list(range(self.lower,self.upper)),self.nuc_fit.get(), label = "Nucleosome Model")
+        plt.plot(list(range(self.lower,self.upper)),self.nfr_fit.get(), label = "NFR Model")
         plt.legend()
         plt.xlabel("Fragment size")
         plt.ylabel("Relative Frequency")
@@ -109,8 +111,8 @@ def calculateOccupancy(inserts, bias, params):
     nuc_probs = nuc_probs / np.sum(nuc_probs)
     nfr_probs = params.nfr_probs * bias
     nfr_probs = nfr_probs / np.sum(nfr_probs)
-    x = map(lambda alpha: np.log(alpha * nuc_probs + (1 - alpha) * nfr_probs), params.alphas)
-    logliks = np.array(map(lambda j: np.sum(x[j]*inserts),range(params.l)))
+    x = [np.log(alpha * nuc_probs + (1 - alpha) * nfr_probs) for alpha in params.alphas]
+    logliks = np.array([np.sum(x[j]*inserts) for j in range(params.l)])
     logliks[np.isnan(logliks)] = -float('inf')
     occ = params.alphas[np.argmax(logliks)]
     #Compute upper and lower bounds for 95% confidence interval
@@ -129,11 +131,11 @@ def calculateOccupancyMLE(self, mat, bias_mat, params):
         """Calculate Occupancy track"""
         offset=self.start - mat.start
         if offset<params.flank:
-            raise Exception("For calculateOccupancyMLE, mat does not have sufficient flanking regions"),offset
+            raise Exception("For calculateOccupancyMLE, mat does not have sufficient flanking regions")(offset)
         self.vals=np.ones(self.end - self.start)*float('nan')
         self.lower_bound = np.ones(self.end - self.start)*float('nan')
         self.upper_bound =np.ones(self.end - self.start)*float('nan')
-        for i in xrange(params.halfstep,len(self.vals),params.step):
+        for i in range(params.halfstep,len(self.vals),params.step):
             new_inserts = np.sum(mat.get(lower = 0, upper = params.upper,
                                          start = self.start+i-params.flank, end = self.start+i+params.flank+1),
                                          axis = 1)
@@ -190,7 +192,7 @@ def __init__(self, insert_dist, upper, fasta, pwm, sep = 120, min_occ = 0.1, fla
         if step%2 == 0:
             step = step - 1
         self.step = step
-        self.halfstep = (self.step-1) / 2
+        self.halfstep = (self.step-1) // 2
 
 class OccChunk(Chunk):
     """Class for calculating occupancy and occupancy peaks
@@ -201,43 +203,50 @@ def __init__(self, chunk):
         self.chrom = chunk.chrom
         self.peaks = {}
         self.nfrs = []
+
     def getFragmentMat(self):
         self.mat = FragmentMat2D(self.chrom, self.start - self.params.flank,
                                  self.end + self.params.flank, 0, self.params.upper)
         self.mat.makeFragmentMat(self.params.bam)
+
     def makeBiasMat(self):
         self.bias_mat = BiasMat2D(self.chrom, self.start - self.params.flank,
                                  self.end + self.params.flank, 0, self.params.upper)
         if self.params.fasta is not None:
-            bias_track = InsertionBiasTrack(self.chrom, self.start - self.params.window - self.params.upper/2,
-                                  self.end + self.params.window + self.params.upper/2 + 1, log = True)
+            bias_track = InsertionBiasTrack(self.chrom, self.start - self.params.window - self.params.upper//2,
+                                  self.end + self.params.window + self.params.upper//2 + 1, log = True)
             bias_track.computeBias(self.params.fasta, self.params.chrs, self.params.pwm)
             self.bias_mat.makeBiasMat(bias_track)
+
     def calculateOcc(self):
         """calculate occupancy for chunk"""
         self.occ = OccupancyTrack(self.chrom,self.start,self.end)
         self.occ.calculateOccupancyMLE(self.mat, self.bias_mat, self.params)
         self.occ.makeSmoothed(window_len = self.params.window, sd = self.params.flank/3.0)
+
     def getCov(self):
         """Get read coverage for regions"""
         self.cov = CoverageTrack(self.chrom, self.start, self.end)
         self.cov.calculateCoverage(self.mat, 0, self.params.upper, self.params.window)
+
     def callPeaks(self):
         """Call peaks of occupancy profile"""
         peaks = call_peaks(self.occ.smoothed_vals, sep = self.params.sep, min_signal = self.params.min_occ)
         for peak in peaks:
             tmp = OccPeak(peak + self.start, self)
             if tmp.occ_lower > self.params.min_occ and tmp.reads > 0:
                 self.peaks[peak] = tmp
+
     def getNucDist(self):
         """Get nucleosomal insert distribution"""
         nuc_dist = np.zeros(self.params.upper)
-        for peak in self.peaks.keys():
+        for peak in list(self.peaks.keys()):
             sub = self.mat.get(start = self.peaks[peak].start-self.params.flank, end = self.peaks[peak].start+1+self.params.flank)
             sub_sum = np.sum(sub,axis=1)
             sub_sum = sub_sum / float(sum(sub_sum))
             nuc_dist += sub_sum
         return(nuc_dist)
+
     def process(self, params):
         """proces chunk -- calculat occupancy, get coverage, call peaks"""
         self.params = params
@@ -246,9 +255,10 @@ def process(self, params):
         self.calculateOcc()
         self.getCov()
         self.callPeaks()
+
     def removeData(self):
         """remove data from chunk-- deletes all attributes"""
-        names = self.__dict__.keys()
+        names = list(self.__dict__.keys())
         for name in names:
             delattr(self, name)
 

nucleoatac/__init__.py

@@ -1,3 +1 @@
-#Define version based on setup script
-import pkg_resources
-__version__ = pkg_resources.require("NucleoATAC")[0].version
+__version__ = '0.3.4.py3'

nucleoatac/cli.py

@@ -32,9 +32,9 @@ def nucleoatac_main(args):
         print('---------Calling NFR positions--------------------------------------------------')
         run_nfr(args)
     elif call == "run":
-        occ_args = parser.parse_args(map(str,['occ','--bed',args.bed,'--bam',args.bam,
+        occ_args = parser.parse_args(list(map(str,['occ','--bed',args.bed,'--bam',args.bam,
                                             '--fasta', args.fasta, '--pwm', args.pwm,
-                                            '--out',args.out,'--cores',args.cores]))
+                                            '--out',args.out,'--cores',args.cores])))
         vprocess_args = parser.parse_args(['vprocess','--sizes',args.out+'.nuc_dist.txt','--out',args.out])
         nuc_args_list = ['nuc','--bed',args.bed,'--bam',args.bam,'--out',args.out,'--cores', str(args.cores),
                                         '--occ_track', args.out + '.occ.bedgraph.gz','--vmat', args.out + '.VMat',

nucleoatac/diff_occ.py

@@ -29,7 +29,7 @@ def _diffHelper(arg):
                 occ.occ, [occ.peaks[i] for i in sorted(occ.peaks.keys())])
         occ.removeData()
     except Exception as e:
-        print('Caught exception when processing:\n'+  chunk.asBed()+"\n")
+        print(('Caught exception when processing:\n'+  chunk.asBed()+"\n"))
         traceback.print_exc()
         print()
         raise e
@@ -43,7 +43,7 @@ def _writeDiff(pos_queue, out):
             for pos in poslist:
                 pos.write(out_handle)
             pos_queue.task_done()
-    except Exception, e:
+    except Exception as e:
         print('Caught exception when writing occupancy track\n')
         traceback.print_exc()
         print()
@@ -57,7 +57,7 @@ def run_diff(args, bases = 500000):
     """
     chrs = read_chrom_sizes_from_bam(args.bam)
     pwm = PWM.open(args.pwm)
-    chunks = ChunkList.read(args.bed, chromDict = chrs, min_offset = args.flank + args.upper/2 + max(pwm.up,pwm.down))
+    chunks = ChunkList.read(args.bed, chromDict = chrs, min_offset = args.flank + args.upper//2 + max(pwm.up,pwm.down))
     chunks.merge()
     maxQueueSize = max(2,int(100 * bases / np.mean([chunk.length() for chunk in chunks])))
     #get fragmentsizes
@@ -78,7 +78,7 @@ def run_diff(args, bases = 500000):
     diff_process.start()
     nuc_dist = np.zeros(args.upper)
     for j in sets:
-        tmp = pool1.map(_occHelper, zip(j,itertools.repeat(params)))
+        tmp = pool1.map(_occHelper, list(zip(j,itertools.repeat(params))))
         for result in tmp:
             diff_queue.put(result[1])
     pool1.close()

nucleoatac/run_nfr.py

@@ -31,7 +31,7 @@ def _nfrHelper(arg):
             out = nfr.nfrs
         nfr.removeData()
     except Exception as e:
-        print('Caught exception when processing:\n'+  chunk.asBed()+"\n")
+        print(('Caught exception when processing:\n'+  chunk.asBed()+"\n"))
         traceback.print_exc()
         print()
         raise e
@@ -45,7 +45,7 @@ def _writeNFR(pos_queue, out):
             for pos in poslist:
                 pos.write(out_handle)
             pos_queue.task_done()
-    except Exception, e:
+    except Exception as e:
         print('Caught exception when writing occupancy track\n')
         traceback.print_exc()
         print()
@@ -59,7 +59,7 @@ def _writeIns(track_queue, out):
         for track in iter(track_queue.get, 'STOP'):
             track.write_track(out_handle)
             track_queue.task_done()
-    except Exception, e:
+    except Exception as e:
         print('Caught exception when writing insertion track\n')
         traceback.print_exc()
         print()
@@ -104,7 +104,7 @@ def run_nfr(args):
         ins_process = mp.Process(target = _writeIns, args=(ins_queue, args.out))
         ins_process.start()
     for j in sets:
-        tmp = pool1.map(_nfrHelper, zip(j,itertools.repeat(params)))
+        tmp = pool1.map(_nfrHelper, list(zip(j,itertools.repeat(params))))
         for result in tmp:
             if params.ins_track is None:
                 nfr_queue.put(result[0])