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mlovci/test_clipper.ipynb

Created July 29, 2014 02:15
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test_clipper.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd /nas3/lovci/projects/adhoc/clipper/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/nas/nas0/lovci/projects/adhoc/clipper\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"from clipper.src.call_peak import *\n",
"from clipper.src.peakfinder import *\n",
"import random\n",
"import itertools\n",
"import pandas as pd\n",
"import pybedtools\n",
"from gscripts.rnaseq.rpkmZ import Colors"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"logger = logging.getLogger()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"logger.setLevel(logging.INFO)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Peak(namedtuple('Peak', ['chrom', \n",
" 'genomic_start', \n",
" 'genomic_stop', \n",
" 'gene_name', \n",
" 'super_local_poisson_p', \n",
" 'strand',\n",
" 'thick_start',\n",
" 'thick_stop',\n",
" 'peak_number',\n",
" 'number_reads_in_peak',\n",
" 'gene_poisson_p',\n",
" 'size',\n",
" 'p'])):\n",
" def __repr__(self):\n",
" return \"\\t\".join(map(str, [self.chrom, self.genomic_start, self.genomic_stop,\n",
" \"_\".join(map(str, [self.gene_name, self.peak_number, self.number_reads_in_peak])),\n",
" min(self.super_local_poisson_p, self.gene_poisson_p), self.strand,\n",
" self.thick_start, self.thick_stop]))\n",
" def __len__(self):\n",
" return self.genomic_stop - self.genomic_start\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class SmoothingSpline(PeakGenerator):\n",
" \"\"\"Class to fit data to a smooth curve\"\"\"\n",
" \n",
" def __init__(self, xRange, yData, smoothingFactor=None,\n",
" lossFunction = \"get_turn_penalized_residuals\",\n",
" threshold = 0):\n",
" \n",
" \"\"\"\n",
" \n",
" xRange -- the range to interpolate the spline over, must be monotonically increasing\n",
" yData -- the yAxis of the spline that corosponds to the xRange\n",
" smoothingFactor -- tradeoff between smoothness of the spline and how well it fits\n",
" lossFunction -- loss function to use to optomize the spline\n",
" \n",
" \"\"\"\n",
" \n",
" super(SmoothingSpline,self).__init__(xRange, yData)\n",
" \n",
" if smoothingFactor is None:\n",
" #smoothingFactor = 0.25 * numpy.sum(yData) #\n",
" smoothingFactor = len(xRange)\n",
" \n",
" self.k = 3 #degree of spline (cubic)\n",
" self.smoothingFactor = smoothingFactor\n",
" self.spline = None\n",
" self.threshold = threshold\n",
" \n",
" #Sets loss function\n",
" if lossFunction == \"get_turn_penalized_residuals\":\n",
" self.lossFunction = self.get_turn_penalized_residuals\n",
" elif lossFunction == \"get_norm_penalized_residuals\":\n",
" self.lossFunction = self.get_norm_penalized_residuals\n",
" else:\n",
" raise TypeError(\"loss function not implemented\")\n",
"\n",
" \n",
" def get_norm_penalized_residuals(self, spline, norm_weight = 1, residual_weight = 10):\n",
" \n",
" \"\"\"\n",
" \n",
" Returns an error value for the spline. IN this case the error is calculated by\n",
" a weighted combination of the norm and the residuals\n",
" \n",
" spline -- the smoothing spline to get the weight of\n",
" norm_weight --the weight to apply to the norm\n",
" residual_weight -- the weight to apply to the residuals\n",
" \n",
" \"\"\"\n",
" \n",
" from scipy.linalg import norm\n",
"\n",
" #the exponent is a magic number and subject to change\n",
" err = (norm_weight*norm(spline(self.xRange))**2) + (residual_weight*sqrt(spline.get_residual()))\n",
" return err\n",
"\n",
" def get_turn_penalized_residuals(self, spline):\n",
" \n",
" \"\"\"\n",
" \n",
" Returns an error value for the spline. IN this case the error is calculated by\n",
" by the numbers of turns \n",
" \n",
" spline -- the smoothing spline to get the weight of\n",
" \n",
" \"\"\"\n",
" \n",
" func = spline(self.xRange)\n",
"\n",
" turns = sum(abs(diff(sign(diff(func))))) / 2\n",
" \n",
" err = sqrt((spline.get_residual()) * (turns ** 4))\n",
"\n",
" return err\n",
" \n",
" def fit_univariate_spline(self, smoothingFactor = None, weight = None):\n",
" \n",
" \"\"\"\n",
" \n",
" fit a spline, return the spline.\n",
" \n",
" (wrapper for UnivariateSpline with error handling and logging)\n",
" \n",
" Parameters:\n",
" smoothingFactor -- parameter for UnivariateSpline\n",
" xRange -- Int, range of spline\n",
" yData -- list, wiggle track positions \n",
" k -- int, degree of spline\n",
" weight -- spline weight\n",
" \n",
" Output: spline object\n",
" \n",
" \"\"\"\n",
" \n",
" if smoothingFactor is None:\n",
" smoothingFactor = self.smoothingFactor\n",
" \n",
" try:\n",
" spline = interpolate.UnivariateSpline(self.xRange, \n",
" self.yData, \n",
" s=smoothingFactor, \n",
" k=self.k, \n",
" w=weight)\n",
" \n",
" except Exception as error: #This error shouldn't happen anymore\n",
" logging.error(\"failed to build spline %s, %s, %s, %s, %s, %s\" % (error, \n",
" self.xRange, \n",
" self.yData, \n",
" smoothingFactor, \n",
" self.k, \n",
" weight) )\n",
" raise\n",
"\n",
" return spline\n",
"\n",
" def fit_loss(self, smoothingFactor=None, weight = None):\n",
" \"\"\"fit a curve with a given smoothing parameter, return the result of the loss fxn\"\"\"\n",
"\n",
" if smoothingFactor == None:\n",
" smoothingFactor = self.smoothingFactor\n",
"\n",
" spline = self.fit_univariate_spline(smoothingFactor=smoothingFactor, weight=weight)\n",
" err = self.lossFunction(spline)\n",
" \n",
" return err\n",
" \n",
"\n",
" def optimize_fit(self, s_estimate=None, method = 'L-BFGS-B', bounds=((1,None),),\n",
" weight=None):\n",
" \"\"\"\n",
"\n",
" optimize the smoothingFactor for fitting.\n",
" \n",
" \"\"\"\n",
" import scipy\n",
" from scipy import optimize\n",
" if s_estimate == None:\n",
" s_estimate = self.smoothingFactor\n",
"\n",
" minOpts = {'disp':False,\n",
" 'maxiter':1000}\n",
"\n",
" minimizeResult = scipy.optimize.minimize(self.fit_loss, s_estimate,\n",
" #args = (weight),\n",
" options = minOpts,\n",
" method = method,\n",
" bounds = bounds,\n",
" )\n",
"\n",
" if minimizeResult.success:\n",
" optimizedSmoothingFactor = minimizeResult.x\n",
" \n",
" else:\n",
" \n",
" #if optimization fails then we revert back to the estimate, probably should log this\n",
" optimizedSmoothingFactor = s_estimate\n",
" \n",
" #logging.error(\"Problem spline fitting. Here is the message:\\n%s\" % (minimizeResult.message))\n",
" #raise Exception\n",
" \n",
" optimizedSpline = self.fit_univariate_spline(optimizedSmoothingFactor, weight)\n",
" self.smoothingFactor = optimizedSmoothingFactor\n",
" self.spline = optimizedSpline\n",
" #print \"optimized: %f\" % optimizedSmoothingFactor\n",
" return optimizedSpline \n",
"\n",
" def get_regions_above_threshold(self, threshold, values):\n",
" \n",
" \"\"\"\n",
" \n",
" Idea here is to call all regions above a given threshold and return start \n",
" stop pairs for those regions added twist is that when everthere is a local\n",
" minima above the threshold we will treat that as a breakpoint\n",
" \n",
" generates start and stop positions for calling peaks on.\n",
" \n",
" threshold -- threshold for what is siginifant peak\n",
" values -- the values (as a numpy array) arranged from 0-length of the section\n",
" \n",
" returns list of tuples(start, stop) used for calling peaks\n",
" \n",
" \"\"\"\n",
" \n",
" xlocs = arange(0, len(values))\n",
" \n",
" #finds all turns, between above and below threshold\n",
" #and generate areas to call peaks in, also \n",
" #makes sure starting and stopping above maxima is caught\n",
" #threshold is at or equal to values, need to correct this\n",
" starts = xlocs[r_[True, diff(values >= threshold)] & (values >= threshold)]\n",
" stops = xlocs[r_[diff(values >= threshold), True] & (values >= threshold)]\n",
" stops = stops + 1 #add to fix off by one bug\n",
" \n",
"\n",
" #error correction incase my logic is wrong here, assuming that starts\n",
" #and stops are always paired, and the only two cases of not being \n",
" #pared are if the spline starts above the cutoff or the spline starts\n",
" #below the cutoff\n",
" assert len(starts) == len(stops)\n",
" \n",
" ### important note: for getting values x->y [inclusive] \n",
" #you must index an array as ar[x:(y+1)]| \n",
" # or else you end up with one-too-few values, the second \n",
" #index is non-inclusive\n",
" \n",
" #gets all local minima, function taken from:\n",
" #http://stackoverflow.com/questions/4624970/finding-local-maxima-minima-with-numpy-in-a-1d-numpy-array\n",
" #Can't have local minima at start or end, that would get caught by \n",
" #previous check, really need to think about that more\n",
"\n",
" local_minima = self.find_local_minima(values)\n",
"\n",
" #append to list any local minima above threshold\n",
" for i, minima in enumerate(local_minima):\n",
" if minima and values[i] >= threshold:\n",
" starts = append(starts, i)\n",
" stops = append(stops, i)\n",
" \n",
" starts = array(sorted(set(starts)))\n",
" stops = array(sorted(set(stops)))\n",
" starts_and_stops = []\n",
" \n",
" #making sure we aren't in some strange state\n",
" assert len(starts) == len(stops)\n",
" \n",
" #get all contigous start and stops pairs \n",
" while len(starts) > 0:\n",
" stop_list = stops[stops > starts[0]]\n",
" \n",
" #if there are no more stops left exit the loop and return the \n",
" #currently found starts and stops\n",
" if len(stop_list) == 0:\n",
" break \n",
" stop = stop_list[0]\n",
" starts_and_stops.append((starts[0], stop))\n",
" starts = starts[starts >= stop]\n",
" \n",
" starts = array([x[0] for x in starts_and_stops])\n",
" stops = array([x[1] for x in starts_and_stops])\n",
" return starts_and_stops, starts, stops\n",
" def find_local_maxima(self, arr):\n",
" \n",
" \"\"\"\n",
" \n",
" Returns a list of boolean values for an array that mark if a value is a local \n",
" maxima or not True for yes false for no\n",
" \n",
" Importantly for ranges of local maxima the value in the middle of the range\n",
" is chosen as the minimum value\n",
" \n",
" \"\"\"\n",
" \n",
" #walks through array, finding local maxima ranges\n",
" \n",
" #to initalize a new array to all false\n",
" maxima = empty(len(arr), dtype='bool')\n",
" maxima.fill(False)\n",
" \n",
" max_range_start = 0\n",
" increasing = True\n",
" for i in range(len(arr[:-1])):\n",
" \n",
" #update location of maxima start until \n",
" if arr[i] < arr[i + 1]:\n",
" \n",
" max_range_start = i + 1\n",
" increasing = True\n",
" \n",
" if (arr[i] > arr[i+1]) and increasing is True:\n",
" increasing = False\n",
" #gets the local maxima midpoint\n",
" maxima[(max_range_start + i) / 2] = True\n",
" \n",
" #catches last case\n",
" if increasing: \n",
" maxima[(max_range_start + len(arr) - 1) / 2] = True\n",
" \n",
" return maxima\n",
" \n",
" def find_local_minima(self, arr):\n",
" \n",
" \"\"\"\n",
" \n",
" Returns a list of boolean values for an array that mark if a value is a local \n",
" minima or not True for yes false for no\n",
" \n",
" Importantly for ranges of local minima the value in the middle of the range\n",
" is chosen as the minimum value\n",
" \n",
" \"\"\"\n",
" \n",
" #walks through array, finding local minima ranges\n",
" \n",
" #hacky way to initalize a new array to all false\n",
" minima = (arr == -1)\n",
" min_range_start = 0\n",
" decreasing = False\n",
" for i in range(len(arr[:-1])):\n",
" \n",
" #array needs to be smooth for this to work, otherwise we'll\n",
" #run into odd edge cases\n",
" #update location of minima start until \n",
" if arr[i] > arr[i + 1]:\n",
" min_range_start = i + 1\n",
" decreasing = True\n",
" \n",
" if (arr[i] < arr[i+1]) and decreasing is True:\n",
" decreasing = False\n",
" #gets the local minima midpoint\n",
" minima[(min_range_start + i) / 2] = True\n",
" \n",
" return minima\n",
" \n",
" def peaks(self, threshold=0, plotit = False):\n",
" \n",
" \"\"\"\n",
" \n",
" run optimization on spline fitting.\n",
" return peak start/stops\n",
" \n",
" \"\"\"\n",
"\n",
" #step 1, identify good initial value\n",
" initial_smoothing_value = self.smoothingFactor\n",
" #print \"initial SF: %f\" % initial_smoothing_value\n",
" bestSmoothingEstimate = initial_smoothing_value\n",
"\n",
"\n",
" #step 1 naive spline\n",
"\n",
" spline = self.fit_univariate_spline()\n",
" self.spline = spline\n",
" \n",
" if plotit == True:\n",
" self.plot()\n",
" \n",
" #step 2, refine to avoid local minima later\n",
" #high-temp optimize\n",
"\n",
" best_error = self.lossFunction(spline)\n",
"\n",
" #tries to find optimal initial smoothing parameter in this loop\n",
" for i in range(2, 50):\n",
"\n",
" cur_smoothing_value = initial_smoothing_value * i\n",
"\n",
" cur_error = self.fit_loss(cur_smoothing_value)\n",
" self.spline = self.fit_univariate_spline(cur_smoothing_value)\n",
" \n",
" if plotit == True:\n",
" self.plot(label=str(cur_smoothing_value))\n",
"\n",
" if cur_error < best_error:\n",
" bestSmoothingEstimate = cur_smoothing_value\n",
" best_error = cur_error\n",
"\n",
" try:\n",
" #fine optimization of smooting paramater\n",
" #low-temp optimize\n",
" optimizedSpline = self.optimize_fit(s_estimate=bestSmoothingEstimate)\n",
" self.spline = optimizedSpline\n",
" if plotit:\n",
" self.plot(title = \"optimized spline\", threshold=self.threshold)\n",
"\n",
" except Exception as error:\n",
" logging.error(\"failed spline fitting optimization at section (major crash)\")\n",
"\n",
" raise\n",
"\n",
" #descretizes the data so it is easy to get regions above a given threshold\n",
" spline_values = array([int(x) for x in optimizedSpline(self.xRange)])\n",
" \n",
" if plotit is True:\n",
" self.plot()\n",
" \n",
" starts_and_stops, starts, stops = self.get_regions_above_threshold(self.threshold, \n",
" spline_values)\n",
" peak_definitions = []\n",
" for peak_start, peak_stop in starts_and_stops: \n",
" peak_center = [x + peak_start for x in self.xRange[self.find_local_maxima(spline_values[peak_start:(peak_stop + 1)])]]\n",
" \n",
" assert len(peak_center) in (0,1) \n",
" \n",
" if len(peak_center) == 1:\n",
" peak_definitions.append((peak_start, peak_stop, peak_center[0]))\n",
" self.peakCalls = peak_definitions\n",
" return peak_definitions\n",
" def plot(self, ax=None):\n",
" self.peakCalls = self.peaks()\n",
" if ax==None:\n",
" ax = pylab.gca()\n",
" for peak in self.peakCalls:\n",
" ax.axvline(x=peak[2], color='red', alpha=1, linewidth=4) #peak middle\n",
" ax.axvspan(peak[0]+1, peak[1]-1, facecolor='blue', linewidth=2, alpha=.2)#peak span\n",
" ax.plot(self.yData, c='b', alpha=0.7)\n",
" ax.plot(self.spline(self.xRange))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class SS(SmoothingSpline):\n",
" def __init__(self, *args, **kwargs):\n",
" super(SS, self).__init__(*args, **kwargs)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 639
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def peaks_from_info(wiggle, pos_counts, lengths, interval, gene_length, \n",
" max_gap=15, fdr_alpha=0.05, binom_alpha=0.001, method=\"binomial\" ,user_threshold=None,\n",
" minreads=20, poisson_cutoff=0.05, plotit=False, \n",
" width_cutoff=10, windowsize=500, SloP=False, \n",
" correct_p=False, max_width=None, min_width=None, \n",
" algorithm=\"spline\", stastical_test = \"poisson\", verbose=False):\n",
"\n",
" \"\"\"\n",
" \n",
" same args as before \n",
" wiggle is converted from bam file\n",
" pos_counts - one point per read instead of coverage of entire read\n",
" lengths - lengths aligned portions of reads \n",
" rest are the same fix later\n",
"\n",
"\n",
" calls peaks for an individual gene \n",
" \n",
"\n",
" interval - gtf interval describing gene to query\n",
" max_gap - space between sections for calling new peaks\n",
" fdr_alpha - false discovery rate, p-value bonferoni correct from peaks script (called in setup)\n",
" user_threshold - user defined FDR thershold (probably should be factored into fdr_alpha\n",
" minreads - min reads in section to try and call peaks\n",
" poisson_cutoff - p-value for signifance cut off for number of reads in genomic_center that gets called - might want to use ashifted distribution\n",
" plotit - makes figures \n",
" \n",
" w_cutoff - width cutoff, peaks narrower than this are discarted \n",
" windowssize - for super local calculation distance left and right to look \n",
" SloP - super local p-value instead of gene-wide p-value\n",
" correct_p - boolean bonferoni correction of p-values from poisson\n",
" algorithm - str the algorithm to run\n",
" \"\"\"\n",
"\n",
" #used for poisson calclulation?\n",
" nreads_in_gene = sum(pos_counts)\n",
"\n",
" #decides FDR calcalation, maybe move getFRDcutoff mean into c code\n",
" gene_threshold = 0\n",
"\n",
" \n",
" if user_threshold is None: \n",
" if method == \"binomial\": #Uses Binomial Distribution to get cutoff if specified by user \n",
" gene_threshold = get_FDR_cutoff_binom(lengths, gene_length, binom_alpha)\n",
" elif method == \"random\":\n",
" \n",
" gene_threshold = get_FDR_cutoff_mean(readlengths = lengths,\n",
" genelength = gene_length,\n",
" alpha = fdr_alpha)\n",
" else:\n",
" raise ValueError(\"Method %s does not exist\" % (method))\n",
" else:\n",
" logging.info(\"using user threshold\")\n",
" gene_threshold = user_threshold\n",
" \n",
" \n",
" \n",
" if not isinstance(gene_threshold, int):\n",
" raise TypeError\n",
" \n",
" #these are what is built in this dict, complicated enough that it might \n",
" #be worth turning into an object\n",
" peak_dict = {}\n",
" peak_dict['clusters'] = []\n",
" peak_dict['sections'] = {}\n",
" peak_dict['nreads'] = int(nreads_in_gene)\n",
" peak_dict['threshold'] = gene_threshold\n",
" peak_dict['loc'] = interval \n",
"\n",
" peak_number=1\n",
" \n",
" sections = find_sections(wiggle, max_gap)\n",
" if plotit is True: \n",
" plot_sections(wiggle, sections, gene_threshold)\n",
"\n",
" for sect in sections:\n",
"\n",
" sectstart, sectstop = sect\n",
" sect_length = sectstop - sectstart + 1\n",
" data = wiggle[sectstart:(sectstop + 1)]\n",
"\n",
" #this cts is alright because we know the reads are bounded\n",
" cts = pos_counts[sectstart:(sectstop + 1)]\n",
" xvals = arange(len(data))\n",
" Nreads = sum(cts)\n",
" peak_dict['sections'][sect] = {}\n",
" threshold = int()\n",
" peak_dict['sections'][sect]['nreads'] = int(Nreads)\n",
"\n",
" #makes sure there are enough reads\n",
" if Nreads < minreads:\n",
" logging.info(\"\"\"%d is not enough reads, skipping section: %s\"\"\" %(Nreads, sect))\n",
" peak_dict['sections'][sect]['tried'] = False \n",
" continue\n",
" else:\n",
" logging.info(\"\"\"Analyzing section %s with %d reads\"\"\" %(sect, Nreads))\n",
" pass\n",
" \n",
" \n",
" if user_threshold == None:\n",
" if SloP:\n",
" \n",
" #not exactly the right way to do this but it should be very close.\n",
" sect_read_lengths = [int(np.mean(lengths))] * Nreads #not random anymore.... this is deterministic\n",
" sect_read_lengths = [sect_length - 1 if read > sect_length else read for read in sect_read_lengths]\n",
"\n",
" if method == \"binomial\": #Uses Binomial Distribution to get cutoff if specified by user \n",
" threshold = max(gene_threshold, get_FDR_cutoff_binom(sect_read_lengths, sect_length, binom_alpha))\n",
" elif method == \"random\":\n",
" #use the minimum FDR cutoff between superlocal and gene-wide calculations\n",
" threshold = max(gene_threshold, get_FDR_cutoff_mean(readlengths=sect_read_lengths,\n",
" genelength=sect_length,\n",
" alpha=fdr_alpha))\n",
" else:\n",
" raise ValueError(\"Method %s does not exist\" % (method))\n",
" logging.info(\"Using super-local threshold %d\" %(threshold))\n",
" \n",
" else:\n",
" threshold = gene_threshold\n",
" else:\n",
" threshold = user_threshold\n",
" \n",
" #saves threshold for each individual section\n",
" peak_dict['sections'][sect]['threshold'] = threshold\n",
" peak_dict['sections'][sect]['nreads'] = int(Nreads)\n",
" peak_dict['sections'][sect]['tried'] = True\n",
" peak_dict['sections'][sect]['nPeaks'] = 0\n",
" \n",
" if max(data) < threshold:\n",
" logging.info(\"data does not excede threshold, stopping\")\n",
" continue\n",
" \n",
" if algorithm == \"spline\":\n",
" data = map(float, data)\n",
" initial_smoothing_value = ((sectstop - sectstart + 1)**(1/3)) + 10\n",
" logging.info(\"initial smoothing value: %.2f\" % initial_smoothing_value)\n",
" fitter = SmoothingSpline(xvals, data, smoothingFactor=initial_smoothing_value,\n",
" lossFunction=\"get_turn_penalized_residuals\",\n",
" threshold=threshold)\n",
" \n",
" elif algorithm == \"gaussian\":\n",
" cts = map(float, cts)\n",
" fitter = GaussMix(xvals, cts)\n",
" \n",
" elif algorithm == \"classic\":\n",
" data = map(float, data)\n",
" fitter = Classic(xvals, data, max_width, min_width, max_gap)\n",
"\n",
" try:\n",
" peak_definitions = fitter.peaks()\n",
" logging.info(\"optimized smoothing value: %.2f\" % fitter.smoothingFactor)\n",
" \n",
" if peak_definitions is None:\n",
" numpeaks = 0\n",
" else:\n",
" numpeaks = len(peak_definitions)\n",
" logging.info(\"I identified %d potential peaks\" % (numpeaks))\n",
"\n",
" except Exception as error:\n",
" logging.error(\"peak finding failed:, %s, %s\" % (interval.name, error))\n",
" raise error\n",
" \n",
" #subsections that are above threshold\n",
" #peak center is actually the location where we think binding should\n",
" #occur, not the average of start and stop\n",
" for peak_start, peak_stop, peak_center in peak_definitions: \n",
" \n",
" genomic_start = interval.start + sectstart + peak_start\n",
" genomic_stop = interval.start + sectstart + peak_stop\n",
" number_reads_in_peak = np.sum(pos_counts[(peak_start + sectstart):(peak_stop + sectstart + 1)])\n",
" \n",
" #sum(cts[peak_start:(peak_stop + 1)])\n",
" logging.info(\"\"\"Peak %d (%d - %d) has %d \n",
" reads\"\"\" %(peak_number, \n",
" peak_start,\n",
" (peak_stop + 1),\n",
" number_reads_in_peak))\n",
"\n",
" \n",
" #highest point in start stop\n",
" genomic_center = interval.start + sectstart + peak_center\n",
"\n",
" #makes it thicker so we can see on the browser \n",
" thick_start = genomic_center - 2\n",
" thick_stop = genomic_center + 2\n",
"\n",
" #best_error checking logic to keep bed files from breaking\n",
" if thick_start < genomic_start:\n",
" thick_start = genomic_start\n",
" if thick_stop > genomic_stop:\n",
" thick_stop = genomic_stop\n",
"\n",
" peak_length = genomic_stop - genomic_start + 1\n",
"\n",
" #skip really small peaks\n",
" if peak_length < width_cutoff:\n",
" logging.info(\"small peak\")\n",
" #continue\n",
" \n",
"\n",
" #super local logic \n",
" #best_error check to make sure area is in area of gene\n",
"\n",
" #distance from gene start\n",
" if genomic_center - interval.start - windowsize < 0: \n",
" area_start = 0\n",
"\n",
" #for super local gets area around genomic_center for calculation\n",
" else: \n",
" area_start = genomic_center - interval.start - windowsize\n",
" #area_start = sectstart\n",
"\n",
" #same thing except for end of gene instead of start\n",
" if genomic_center + windowsize > interval.stop: #distance to gene stop\n",
" area_stop = interval.start - interval.stop + 1\n",
" else:\n",
" area_stop = genomic_center - interval.start + windowsize\n",
"\n",
" #use area reads + 1/2 all other reads in gene: \n",
" #area_reads = sum(pos_counts[area_start:area_stop]) + \n",
" #0.5*(sum(pos_counts) - \n",
" #sum(pos_counts[area_start:area_stop]))\n",
"\n",
" #use area reads:\n",
" area_reads = sum(pos_counts[area_start:area_stop])\n",
" area_size = area_stop - area_start + 1\n",
"\n",
" #area_reads = sum(pos_counts[sectstart:sectstop])\n",
" #area_size = sect_length\n",
"\n",
" #calcluates poisson based of whole gene vs genomic_center\n",
" if algorithm == \"classic\" and peak_length < min_width:\n",
" peak_length = min_width\n",
" \n",
" if stastical_test == \"poisson\":\n",
" gene_pois_p = poissonP(nreads_in_gene, \n",
" number_reads_in_peak, \n",
" int(interval.attrs['effective_length']), \n",
" peak_length)\n",
" elif stastical_test == \"negative_binomial\":\n",
" pass\n",
" \n",
" #set SloP\n",
" if SloP is True:\n",
" #same thing except for based on super local p-value\n",
" if stastical_test == \"poisson\":\n",
" slop_pois_p = poissonP(area_reads, \n",
" number_reads_in_peak, \n",
" area_size, \n",
" peak_length)\n",
" if math.isnan(slop_pois_p):\n",
" slop_pois_p = np.Inf\n",
"\n",
" else:\n",
" slop_pois_p = np.Inf\n",
"\n",
"\n",
"\n",
" #TODO a peak object might just be a gtf file or\n",
" #bed file...\n",
" peak_dict['clusters'].append(Peak(interval.chrom, \n",
" genomic_start, \n",
" genomic_stop, \n",
" interval.attrs['gene_id'], \n",
" slop_pois_p, \n",
" interval.strand,\n",
" thick_start,\n",
" thick_stop,\n",
" peak_number,\n",
" number_reads_in_peak,\n",
" gene_pois_p,\n",
" peak_length,\n",
" 0\n",
" )\n",
" )\n",
"\n",
" peak_number += 1\n",
" peak_dict['sections'][sect]['nPeaks'] +=1\n",
" \n",
" peak_dict['Nclusters'] = peak_number - 1\n",
" if plotit:\n",
" import sys\n",
" plt.show()\n",
" v = sys.stdin.read(1)\n",
" return peak_dict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 649
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"interval = pybedtools.Interval(\"chr10\", 105727470, 105787342, strand=\"+\")\n",
"interval.attrs['effective_length'] = interval.length\n",
"interval.attrs['gene_id'] = 'slk'\n",
"\n",
"bam_file = \"FOX2.bam\"\n",
"bam_fileobj = pysam.Samfile(bam_file, 'rb')\n",
"subset_reads = bam_fileobj.fetch(reference=interval.chrom, start=interval.start, end=interval.stop)\n",
"#need to document reads to wiggle\n",
"(wiggle, \n",
" jxns, \n",
" pos_counts, \n",
" read_lengths, \n",
" allreads) = readsToWiggle_pysam(subset_reads, interval.start, interval.stop, interval.strand, \"start\", False)\n",
"gap=15\n",
"gene_peaks = peaks_from_info(wiggle, pos_counts, read_lengths, interval, \n",
" interval.length, max_gap=gap, SloP=True)\n",
"_ = pylab.hist([y-x for (x,y) in sections], bins=70, facecolor=Colors().redColor, edgecolor=\"none\")\n",
"\n",
"f = pylab.figure(figsize=(18,4))\n",
"ax = pylab.gca()\n",
"for sect, dat in gene_peaks['sections'].iteritems() :\n",
" if dat['tried'] == True:\n",
" col = 'g'\n",
" else:\n",
" col = 'y'\n",
" ax.axvspan(sect[0], sect[1], color=col, alpha=0.5)\n",
"ax.plot(wiggle)\n",
"ax.set_xlim(xmax=len(wiggle))\n",
"\n",
"for peak in gene_peaks['clusters']:\n",
" print peak.thick_start - interval.start, peak.thick_stop- interval.start\n",
" ax.axvspan(peak.genomic_start - interval.start, peak.genomic_stop- interval.start, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:13 is not enough reads, skipping section: (25, 183)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (343, 364)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (382, 409)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (616, 632)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (825, 864)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (889, 949)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (1085, 1177)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (1273, 1295)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (1362, 1394)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (1613, 1653)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (1696, 1727)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (1960, 2024)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (2111, 2138)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (2260, 2288)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (2309, 2349)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (2495, 2519)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (2700, 2728)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (2906, 2941)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (3544, 3575)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (3781, 3819)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (3882, 3964)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (4096, 4118)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (4410, 4450)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (4533, 4573)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (5191, 5218)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (5519, 5545)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (5606, 5709)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (5770, 5805)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (5822, 5854)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:18 is not enough reads, skipping section: (5872, 5988)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (6007, 6048)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (6171, 6223)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (6375, 6415)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (6461, 6487)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (7834, 7880)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (7942, 8034)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (8218, 8258)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (8480, 8518)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (9920, 9951)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (9973, 10003)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (10121, 10160)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (10618, 10642)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (11137, 11166)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (11998, 12032)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (12253, 12281)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (12355, 12393)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (12603, 12647)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (12705, 12739)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (12857, 12897)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (13063, 13093)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (13324, 13429)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (13450, 13480)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (13555, 13610)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (14360, 14383)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (14469, 14509)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (14673, 14712)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (14815, 14837)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (15152, 15174)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (15209, 15254)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (15360, 15411)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:10 is not enough reads, skipping section: (15539, 15637)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (15717, 15750)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (15840, 15868)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (16079, 16126)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (16143, 16177)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (16244, 16273)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (16307, 16347)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (16489, 16529)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (17080, 17109)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (17303, 17343)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (17604, 17643)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (18291, 18348)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (19448, 19476)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (19521, 19561)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (19582, 19632)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (19675, 19702)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (19781, 19819)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (19973, 19994)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (20131, 20159)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (20628, 20662)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (20792, 20826)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (20877, 20917)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (21317, 21355)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (22351, 22391)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (22570, 22592)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (22693, 22737)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (22962, 23015)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (23350, 23413)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (23433, 23462)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (23532, 23564)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (23601, 23643)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (23763, 23842)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (24070, 24163)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (24252, 24292)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (24948, 24975)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (25060, 25100)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (25650, 25755)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (25958, 25998)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (26031, 26062)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (26140, 26173)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (26326, 26402)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (26522, 26562)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (26603, 26635)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Analyzing section (26735, 26892) with 32 reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Using super-local threshold 14\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:initial smoothing value: 11.00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:optimized smoothing value: 495.00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:I identified 1 potential peaks\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 1 (86 - 108) has 4 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (26965, 27023)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (27133, 27163)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (27208, 27248)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (27328, 27352)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (27643, 27675)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (28129, 28210)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (28244, 28332)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (28387, 28427)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (28577, 28614)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (29074, 29114)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (29140, 29212)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (29258, 29298)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (29368, 29444)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (29591, 29621)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (29702, 29739)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (29773, 29799)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:19 is not enough reads, skipping section: (29839, 30023)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (30078, 30098)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (30122, 30237)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (30294, 30334)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:14 is not enough reads, skipping section: (30365, 30463)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (30577, 30617)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (30678, 30758)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (30809, 30839)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (31005, 31039)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (31061, 31106)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (31194, 31259)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (31280, 31313)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:0 is not enough reads, skipping section: (31406, 31429)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (31535, 31632)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (31658, 31688)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (31825, 31869)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (32005, 32044)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:0 is not enough reads, skipping section: (32154, 32171)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (33880, 33940)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (34458, 34523)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (34597, 34632)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (34904, 34944)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (35013, 35052)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (35136, 35198)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (35378, 35484)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (35599, 35640)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (35736, 35776)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (35830, 35897)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (36371, 36390)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (36457, 36492)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (36521, 36605)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (36956, 36997)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (37016, 37050)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (37128, 37193)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (37316, 37398)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (37491, 37525)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (37584, 37607)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (37664, 37702)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:15 is not enough reads, skipping section: (37718, 37817)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (37895, 37979)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (38082, 38107)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (38193, 38239)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (38663, 38688)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (40331, 40365)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (40412, 40452)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (40515, 40547)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (40580, 40626)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (40700, 40733)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (40773, 40813)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (41301, 41349)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (41394, 41454)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (41869, 41909)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (41995, 42039)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (42105, 42190)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (42290, 42346)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:12 is not enough reads, skipping section: (42386, 42481)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (42498, 42599)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (42621, 42668)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (42719, 42822)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Analyzing section (42883, 43324) with 187 reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Using super-local threshold 26\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:initial smoothing value: 11.00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:optimized smoothing value: 517.00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:I identified 5 potential peaks\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 2 (54 - 72) has 20 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 3 (71 - 88) has 11 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 4 (107 - 111) has 2 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:small peak\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 5 (202 - 215) has 20 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 6 (214 - 241) has 11 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (43512, 43550)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (43672, 43697)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (43736, 43764)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:4 is not enough reads, skipping section: (43858, 43894)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (44363, 44410)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (44528, 44554)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (44623, 44663)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:7 is not enough reads, skipping section: (44717, 44772)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (44829, 44869)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:9 is not enough reads, skipping section: (44956, 45053)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (45107, 45147)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (45170, 45215)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (45247, 45416)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (45464, 45510)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (45561, 45603)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (45686, 45724)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (45750, 45790)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (45877, 45915)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (45938, 45961)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (46343, 46375)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (46411, 46470)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (46819, 46845)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (47015, 47050)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (47164, 47191)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (47314, 47345)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (47396, 47434)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (47716, 47752)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (47868, 47908)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (47927, 47959)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (48058, 48095)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (48154, 48194)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (49154, 49188)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (49432, 49458)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (50086, 50126)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (50399, 50433)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (50468, 50507)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (50557, 50619)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (50764, 50788)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (50810, 50850)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (51047, 51090)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (51173, 51213)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (51242, 51282)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (51418, 51457)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (51480, 51512)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (51955, 51987)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:5 is not enough reads, skipping section: (52074, 52104)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (52131, 52171)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (52196, 52210)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (52374, 52439)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:0 is not enough reads, skipping section: (52775, 52801)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (53930, 53992)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (54766, 54806)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (55537, 55573)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (55873, 55907)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (56099, 56150)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (56306, 56328)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (56363, 56403)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (56432, 56461)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (56723, 56763)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (56861, 56889)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (57056, 57074)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:2 is not enough reads, skipping section: (57904, 57932)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:8 is not enough reads, skipping section: (58090, 58199)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (58312, 58347)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:6 is not enough reads, skipping section: (58435, 58516)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:1 is not enough reads, skipping section: (58599, 58637)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:3 is not enough reads, skipping section: (58741, 58811)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:8 is not enough reads, skipping section: (58982, 59112)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:8 is not enough reads, skipping section: (59411, 59514)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Analyzing section (59530, 59702) with 40 reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Using super-local threshold 16\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:initial smoothing value: 11.00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:optimized smoothing value: 154.00\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:I identified 1 potential peaks\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:Peak 7 (120 - 136) has 4 \n",
" reads\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"INFO:root:9 is not enough reads, skipping section: (59748, 59833)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"26829 26833\n",
"42946 42950\n",
"42960 42964\n",
"42990 42993\n",
"43090 43094\n",
"43100 43104\n",
"59655 59659\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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AIvfs+OV+/DU82UjjXj+BKR2p/J8H0p/aG0jftwMpi21y+dxypA4AFulTqR8+fFilpaWa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\nLEKpA4BF/g9PeZl4eAJY/gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x234c3190>"
]
},
{
"metadata": {},
"output_type": "display_data",
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TZm2PpEyPAhoNAKTwT1DQNNTUEHuWmvLQA67nMNBTMXGcchRw/gGoF0sdkjI9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\nGBQAAAAAAAAHgwIAAAAAAOD4F3dNmp2IVdz2AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x286e0d10>"
]
}
],
"prompt_number": 650
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, axes = pylab.subplots(10,2, figsize=(8,20))\n",
"axes = itertools.chain(*axes)\n",
"for sect, dat in gene_peaks['sections'].iteritems():\n",
" sect_length = sect[1] - sect[0]\n",
" if dat['nreads'] < 20:\n",
" continue\n",
" sect_read_lengths = [int(np.mean(read_lengths))] * dat['nreads'] #not random anymore.... this is deterministic\n",
" sect_read_lengths = [sect_length - 1 if read > sect_length else read for read in sect_read_lengths]\n",
" thresh = get_FDR_cutoff_binom(sect_read_lengths, sect_length, 0.001) \n",
" y = wiggle[sect[0]:sect[1]+1]\n",
" x = np.arange(len(y))\n",
" ss = SmoothingSpline(x,y, smoothingFactor=(len(y) ** (1/3))+10, threshold=thresh)\n",
" ax = axes.next()\n",
" ss.plot(ax=ax)\n",
" ax.set_title(\"%s t:%d, s:%.0f n:%d\" % (str(sect), thresh, ss.smoothingFactor, dat['nreads']))\n",
"pylab.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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AHuhUy3ZjVaSLQnqmHmE1ECkLGDcZuD73M5ToiQEASRYVsMszAAD4IQxmuWNQ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Os3nzZvPoo4+aFStWmMcff9x8/vnnebOWyZn8QNbkB7JmYTzoDwAAWGd5XGEEAABwB2hwAACA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\n1qHBAQAA1qHBAQAA1vk/H6bjO1cjqrYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x28c27c50>"
]
}
],
"prompt_number": 646
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"interval = pybedtools.Interval(\"chr1\", 225674534, 225840845, strand=\"-\")\n",
"interval.attrs['effective_length'] = interval.length\n",
"interval.attrs['gene_id'] = 'enah'\n",
"bam_file = \"FOX2.bam\"\n",
"bam_fileobj = pysam.Samfile(bam_file, 'rb')\n",
"subset_reads = bam_fileobj.fetch(reference=interval.chrom, start=interval.start, end=interval.stop)\n",
"#need to document reads to wiggle\n",
"wiggle, jxns, pos_counts, read_lengths, allreads = readsToWiggle_pysam(subset_reads, interval.start, interval.stop, interval.strand, \"start\", False)\n",
"gene_peaks = peaks_from_info(wiggle, pos_counts, read_lengths, interval, interval.length, max_gap=5, SloP=True, binom_alpha=0.05)\n",
"_ = pylab.hist([y-x for (x,y) in sections], bins=70, facecolor=Colors().redColor, edgecolor=\"none\")\n",
"f = pylab.figure(figsize=(18,4))\n",
"ax = pylab.gca()\n",
"for sect, dat in gene_peaks['sections'].iteritems() :\n",
" if dat['tried'] == True:\n",
" col = 'g'\n",
" else:\n",
" col = 'y'\n",
" ax.axvspan(sect[0], sect[1], color=col, alpha=0.5)\n",
"ax.plot(wiggle)\n",
"ax.set_xlim(xmax=len(wiggle))\n",
"\n",
"for peak in gene_peaks['clusters']:\n",
" print peak.thick_start - interval.start, peak.thick_stop- interval.start\n",
" ax.axvspan(peak.genomic_start - interval.start, peak.genomic_stop- interval.start, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"250 254\n",
"1260 1264\n",
"7570 7574\n",
"9324 9328\n",
"9498 9502\n",
"9687 9691\n",
"9932 9936\n",
"10048 10052\n",
"13893 13897\n",
"16243 16247\n",
"16561 16565\n",
"17501 17505\n",
"18094 18098\n",
"48220 48224\n",
"50014 50018\n",
"52942 52946\n",
"54014 54018\n",
"57450 57454\n",
"93494 93498\n",
"107355 107359\n",
"112298 112302\n",
"130828 130832\n",
"131995 131999\n",
"132147 132151\n",
"133707 133711\n",
"147032 147036\n",
"148658 148662\n",
"161096 161099\n",
"162509 162513\n",
"164234 164238\n",
"165227 165231\n",
"165738 165742\n",
"166077 166081\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AIvfs+OV+/DU82UjjXj+BKR2p/J8H0p/aG0jftwMpi21y+dxypA4AFulTqR8+fFilpaWa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\nLEKpA4BF/g9PeZl4eAJY/gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x28c14fd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ZAIBQmDXrn9qlG/RkqBMC+IkAQimzTHfrRv1m/RxOAYRA+keH01skgJIUiZXx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\nnq1uLT+58sor0tdf97V+LrwPv/nmYX3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ctKRwgGLTpuIZvMaTv+qg/qqDTtOffrq7XTrL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\nDMMwDMMwjE3+B36AYEF0gR9sAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x29544110>"
]
}
],
"prompt_number": 602
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print gene_peaks['Nclusters']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33\n"
]
}
],
"prompt_number": 603
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gene_peaks['clusters']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 616,
"text": [
"[chr1\t225674777\t225674797\tenah_1_0\t0.823178654087\t-\t225674784\t225674788,\n",
" chr1\t225675783\t225675809\tenah_2_14\t2.62644773634e-09\t-\t225675794\t225675798,\n",
" chr1\t225682089\t225682123\tenah_3_12\t6.61269047903e-07\t-\t225682104\t225682108,\n",
" chr1\t225683841\t225683878\tenah_4_12\t1.09308971733e-06\t-\t225683858\t225683862,\n",
" chr1\t225684008\t225684055\tenah_5_36\t3.46439348206e-29\t-\t225684032\t225684036,\n",
" chr1\t225684204\t225684249\tenah_6_14\t1.16162232322e-07\t-\t225684221\t225684225,\n",
" chr1\t225684432\t225684501\tenah_7_56\t3.33059534083e-48\t-\t225684466\t225684470,\n",
" chr1\t225684557\t225684669\tenah_8_134\t1.31448717496e-139\t-\t225684582\t225684586,\n",
" chr1\t225688414\t225688448\tenah_9_21\t4.4950401437e-15\t-\t225688427\t225688431,\n",
" chr1\t225690703\t225690835\tenah_10_270\t0.0\t-\t225690777\t225690781,\n",
" chr1\t225691077\t225691125\tenah_11_21\t2.2386906821e-13\t-\t225691095\t225691099,\n",
" chr1\t225692024\t225692051\tenah_12_6\t0.00426136224374\t-\t225692035\t225692039,\n",
" chr1\t225692590\t225692660\tenah_13_33\t2.92287662568e-22\t-\t225692628\t225692632,\n",
" chr1\t225722729\t225722784\tenah_14_11\t6.17555639625e-05\t-\t225722754\t225722758,\n",
" chr1\t225724526\t225724573\tenah_15_40\t7.24159655473e-34\t-\t225724548\t225724552,\n",
" chr1\t225727459\t225727497\tenah_16_18\t1.07456474544e-11\t-\t225727476\t225727480,\n",
" chr1\t225728532\t225728593\tenah_17_23\t7.8395302888e-14\t-\t225728548\t225728552,\n",
" chr1\t225731951\t225732021\tenah_18_58\t2.01594419809e-50\t-\t225731984\t225731988,\n",
" chr1\t225768017\t225768042\tenah_19_8\t0.000112112663672\t-\t225768028\t225768032,\n",
" chr1\t225781862\t225781918\tenah_20_22\t1.91660622745e-13\t-\t225781889\t225781893,\n",
" chr1\t225786831\t225786838\tenah_21_4\t0.00999954998454\t-\t225786832\t225786836,\n",
" chr1\t225805352\t225805379\tenah_22_13\t2.54321246862e-08\t-\t225805362\t225805366,\n",
" chr1\t225806514\t225806552\tenah_23_39\t1.022286549e-34\t-\t225806529\t225806533,\n",
" chr1\t225806681\t225806686\tenah_24_0\t0.701599485386\t-\t225806681\t225806685,\n",
" chr1\t225808218\t225808269\tenah_25_25\t7.99168985156e-17\t-\t225808241\t225808245,\n",
" chr1\t225821553\t225821584\tenah_26_18\t1.69940240221e-12\t-\t225821566\t225821570,\n",
" chr1\t225823170\t225823208\tenah_27_19\t1.26041218697e-12\t-\t225823192\t225823196,\n",
" chr1\t225835630\t225835633\tenah_28_0\t0.68003568042\t-\t225835630\t225835633,\n",
" chr1\t225837028\t225837062\tenah_29_35\t9.28224693529e-31\t-\t225837043\t225837047,\n",
" chr1\t225838735\t225838799\tenah_30_24\t2.01388316236e-14\t-\t225838768\t225838772,\n",
" chr1\t225839748\t225839780\tenah_31_8\t0.00040057955264\t-\t225839761\t225839765,\n",
" chr1\t225840253\t225840297\tenah_32_15\t1.55728518231e-08\t-\t225840272\t225840276,\n",
" chr1\t225840589\t225840643\tenah_33_17\t6.06428067722e-17\t-\t225840611\t225840615]"
]
}
],
"prompt_number": 616
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = wiggle[49832:50253]\n",
"pylab.plot(y)\n",
"x = np.arange(len(y))\n",
"ss = SmoothingSpline(x, y, smoothingFactor=len(y) ** (1/3), lossFunction=\"get_turn_penalized_residuals\")\n",
"z = ss.peaks()\n",
"ss.plot()\n",
"print len(y) ** (1/2.5), ss.smoothingFactor"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"11.2127680502 1008\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x25db3210>"
]
}
],
"prompt_number": 546
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"interval = pybedtools.Interval(\"chr12\", 12878863, 12982909, strand=\"+\")\n",
"interval.attrs['effective_length'] = interval.length\n",
"interval.attrs['gene_id'] = 'test'\n",
"bam_file = \"FOX2.bam\"\n",
"bam_fileobj = pysam.Samfile(bam_file, 'rb')\n",
"subset_reads = bam_fileobj.fetch(reference=interval.chrom, start=interval.start, end=interval.stop)\n",
"#need to document reads to wiggle\n",
"(wiggle, \n",
" jxns, \n",
" pos_counts, \n",
" read_lengths, \n",
" allreads) = readsToWiggle_pysam(subset_reads, interval.start, interval.stop, interval.strand, \"center\", False)\n",
"gap=10\n",
"gene_peaks = peaks_from_info(wiggle, pos_counts, read_lengths, interval, \n",
" interval.length, max_gap=gap, SloP=True)\n",
"_ = pylab.hist([y-x for (x,y) in sections], bins=70, facecolor=Colors().redColor, edgecolor=\"none\")\n",
"\n",
"f = pylab.figure(figsize=(18,4))\n",
"ax = pylab.gca()\n",
"for sect, dat in gene_peaks['sections'].iteritems() :\n",
" if dat['tried'] == True:\n",
" col = 'g'\n",
" else:\n",
" col = 'y'\n",
" ax.axvspan(sect[0], sect[1], color=col, alpha=0.5)\n",
"ax.plot(wiggle)\n",
"ax.set_xlim(xmax=len(wiggle))\n",
"\n",
"for peak in gene_peaks['clusters']:\n",
" print peak.thick_start - interval.start, peak.thick_stop- interval.start\n",
" ax.axvspan(peak.genomic_start - interval.start, peak.genomic_stop- interval.start, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5931 5933\n",
"30425 30429\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x286ed510>"
]
},
{
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2tNS2bfHvN2xo53m9W7bsJUlau3bvxLRNm/aV5P1JvTF/aiWxvDy8MQqS4vOt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\n88/T7zFGWrNnH6mKikPSvvvmm+R186u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ZmjQpueyGDfHPv//9PD31VHK6cT4tWXKgpPg1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\nAAAAAAAg4f8DE0OET2dv+YYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2aaac2ad8490>"
]
}
],
"prompt_number": 474
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gene_peaks['clusters']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 475,
"text": [
"[chr12\t12884794\t12884810\ttest_1_4\t0.00648875885127\t+\t12884794\t12884796,\n",
" chr12\t12909287\t12909324\ttest_2_20\t2.27552672242e-18\t+\t12909288\t12909292]"
]
}
],
"prompt_number": 475
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"interval = pybedtools.Interval(\"chr22\", 51113070, 51171641, strand=\"+\")\n",
"interval.attrs['effective_length'] = interval.length\n",
"interval.attrs['gene_id'] = 'shank3'\n",
"\n",
"bam_file = \"FOX2.bam\"\n",
"bam_fileobj = pysam.Samfile(bam_file, 'rb')\n",
"subset_reads = bam_fileobj.fetch(reference=interval.chrom, start=interval.start, end=interval.stop)\n",
"#need to document reads to wiggle\n",
"(wiggle, \n",
" jxns, \n",
" pos_counts, \n",
" read_lengths, \n",
" allreads) = readsToWiggle_pysam(subset_reads, interval.start, interval.stop, interval.strand, \"center\", False)\n",
"gap=15\n",
"gene_peaks = peaks_from_info(wiggle, pos_counts, read_lengths, interval, \n",
" interval.length, max_gap=gap, SloP=True)\n",
"_ = pylab.hist([y-x for (x,y) in sections], bins=70, facecolor=Colors().redColor, edgecolor=\"none\")\n",
"\n",
"f = pylab.figure(figsize=(18,4))\n",
"ax = pylab.gca()\n",
"for sect, dat in gene_peaks['sections'].iteritems() :\n",
" if dat['tried'] == True:\n",
" col = 'g'\n",
" else:\n",
" col = 'y'\n",
" ax.axvspan(sect[0], sect[1], color=col, alpha=0.5)\n",
"ax.plot(wiggle)\n",
"ax.set_xlim(xmax=len(wiggle))\n",
"\n",
"for peak in gene_peaks['clusters']:\n",
" print peak.thick_start - interval.start, peak.thick_stop- interval.start\n",
" ax.axvspan(peak.genomic_start - interval.start, peak.genomic_stop- interval.start, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"35974 35978\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x29427710>"
]
},
{
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xOyvcrAc88Dq7bFZBfX1loYtRdFpaytHamszlp9t52Jz1wKzXmprKUVfX1fXa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\n",
"text": [
"<matplotlib.figure.Figure at 0x2956bf90>"
]
}
],
"prompt_number": 617
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gene_peaks['clusters']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 618,
"text": [
"[chr22\t51149033\t51149062\tshank3_1_17\t2.80782849202e-12\t+\t51149044\t51149048]"
]
}
],
"prompt_number": 618
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"interval = pybedtools.Interval(\"chr10\", 105727470, 105787342, strand=\"+\")\n",
"interval.attrs['effective_length'] = interval.length\n",
"interval.attrs['gene_id'] = 'slk'\n",
"\n",
"bam_file = \"FOX2.bam\"\n",
"bam_fileobj = pysam.Samfile(bam_file, 'rb')\n",
"subset_reads = bam_fileobj.fetch(reference=interval.chrom, start=interval.start, end=interval.stop)\n",
"#need to document reads to wiggle\n",
"(wiggle, \n",
" jxns, \n",
" pos_counts, \n",
" read_lengths, \n",
" allreads) = readsToWiggle_pysam(subset_reads, interval.start, interval.stop, interval.strand, \"start\", False)\n",
"gap=15\n",
"gene_peaks = peaks_from_info(wiggle, pos_counts, read_lengths, interval, \n",
" interval.length, max_gap=gap, SloP=True)\n",
"_ = pylab.hist([y-x for (x,y) in sections], bins=70, facecolor=Colors().redColor, edgecolor=\"none\")\n",
"\n",
"f = pylab.figure(figsize=(18,4))\n",
"ax = pylab.gca()\n",
"for sect, dat in gene_peaks['sections'].iteritems() :\n",
" if dat['tried'] == True:\n",
" col = 'g'\n",
" else:\n",
" col = 'y'\n",
" ax.axvspan(sect[0], sect[1], color=col, alpha=0.5)\n",
"ax.plot(wiggle)\n",
"ax.set_xlim(xmax=len(wiggle))\n",
"\n",
"for peak in gene_peaks['clusters']:\n",
" print peak.thick_start - interval.start, peak.thick_stop- interval.start\n",
" ax.axvspan(peak.genomic_start - interval.start, peak.genomic_stop- interval.start, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"42951 42955\n",
"43098 43102\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x2aaac0da93d0>"
]
},
{
"metadata": {},
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sdDLDxJ5/VVUJfxEagBRBjwIAKYUnUM1fTU16souQErwxjANsCtzlKGg+3xdI\nlLDJDEO8HjHRPQq4RgPNW8BbmFLl9Yg0GgBI4Z+goHmorSX2LDXnoQdcz2Ggp2LihMpRwPkHoEEs\ndUi4V7RaEShoZg4dytaiRWcluxiOvF7plVfOV1UVTx1borQ0pyzOkKSPPpKKinKSXYwG27jxhGQX\nocHKylrro49+2KBlNNehB/v2MbSkKVm3Tvrkk27JLgYchGobe71S9eE2SelR4OS7747RqlU/aPT1\nAo3pu++kBQvOTXYxGmzBgjN05Eim49/efbe7Du860fd7xb6jtWNHR7333tlhl9k8H3u0YAsX9lN+\n/s3JLoaj6up03X33SG3denSyiwKklIEDpZEjr0p2MRps//62yS5Cg73wQp6uvfaaBi0j4CagGT14\n5Sly0zJ6tHTVVf+b7GLAQahAQXW18SClncpdzxMvTuf3/fdfo1//+v8Sul4g2V59Vbrzzj8muxgN\ndvPNl+ubb050/Nuf/vQ7Hd7t/5vX69H8+ZfoxhtvDLvMBmUu6datm4466iilp6crMzNTRUVFDVkc\n4qCiIkwm3SQzu+PSPbllokdBaOXlkpSd7GI0WHp607+RDJuN3CVrjwJvM3r3uZvhQ7wZIXUcOJC4\nZTOePTG83jSlZVQrrcbb6G89cLJnz1GNvk6gsVVVJbsE8eSynvB6XN2PNShQ4PF4VFhYqGOOOaYh\ni0EcpfJNuNl4bq7dchEeDcvmjyfOhubao4ChhEBieb2SJ0RALhk9CqjT0RLU1CS7BPHjOljvddcu\nb/BdpZfHBykllbOOEyho2UhmiJYioEdBM7q55nLftLC/UleoesGoO4wdlwo9CggUoCVoDq+yNet7\nt20OrzdNHk/knr4NChR4PB5dcsklGjJkiF577bWGLMrn00+loqKT4rKslmj//naupnv11fNUXt4q\nwaUJZGbYJFDQUtHgCIcGWWqIx429dRnrl5+akjdsb7/93yopiS6Bppu6u6ioW4wlQlPS0uqrwkIj\nGVhDVVYaicZC1QleryfkU76yPe218q0+DS5DKOZ6V68+SQsWnFH/Wfj9/Nln/bVvn7t2J1JHXZ1H\ns2cP0J49nZJdlJSwY0eyS9BwZrvD9T1WY/QoWLp0qVatWqWHHnpIY8aM0Q7HLV0gqUDTphWrsLAw\n4jJ/8xtp2LCmn9QrWdxevO+66/+0fHmPBJcmkHnwpvLwCCROVlYz6tsFhGEPNtRVO2chTqabbx6l\nKVN+E9U5NapoAAAgAElEQVQ8zal3BBCNYcOMZGAN9cknvSWFO5c8Uv1TPnuPgtWFvfXaI0MaXIZQ\nsrKMx6pTpvxGN998uaTIPQFHjXpGkydfnrAyITEOHGirCRN+qy+/DJ/xvqVIT93O2K6Z91a1tW6+\nTKG+/3auPvtsnoz79NAadMd2wgnGq7B69uypyy67TK+//rrDVAXq0mWMRo3qo7y8vIjL3LMntRPy\npTp3B0hymBdGGpstU6tWzSpbTNy1tCd0qSoe+8Ee0a9N0SFhqXy9aA7Iy9J8xCvZWaSnfXV1oXsU\n1FYn9nw122bV1db0ZZHrQ65dTQ/t8UDNoa4292VtrZsvk6dOp16rc865UgkLFBw+fFhlZWWSpF27\nduntt9/WgAEDHKeNZgfUkRi9QVL5ab059MDdQYzmhsYEWoqgHgU1DcobnDJoVMLUHBrW0Yh32zTU\nueT1eqQQ18qaqsTWI05PJFvafm4poh3P3tyl4vDAaJnnr3mvFVGYYU5WMdc6O3fu1K9//WtJ0rHH\nHqtbbrlFJ57o/O7GaG4QCBQ0DMkMgaaJBlnzYa/jmk+gINklAJIjXm1Tsz0cOkdB6DZzonsmOT2R\ndNN+52az6aFHgbOmfI0z2x3R3Ae6eW15zI+fTz75ZBUXF6u4uFjvv/++hg8fHuuiAjhVxp99dpre\nfLNXXJbf3Dm989as6Neu/UFjFyeAP9FG6vZ6aC6qqjL0wgu5yS6G3nijv1as+FGyi9EgW7ZIixad\nmexiNBFN+Cobg02bOuv554PPs6qqwJwE1RXZjVWkhPJ6PSop6aLi4lMkSUVF5+r110/X2rX/palT\nf53w9X/5pVRYeGrC1xNvZWWtk10E1zZskJYu7Z3sYsRVXZ00Y8Zl2rSpc4OWEcmuXdLEiZdr164O\nLpYYeIO2fPlPtHbtcQE9CjIUmNen5oi7XCdPPCEtWxZ9UvDt241XnVt7FKxY0bi5rNA4CBQESqUe\nFs8/f7ZWr47+/DXvrT791N39srcurXFej9gY7r//ao0a9f+SXYwmIT099NXsttv+1IglCeZPZpj8\nE7G5W7nyVN1xx+BkF0O33XadJk26ItnFaJCxY6X8/DHJLgZS0EsvXaQ77ww+z+yNDW8zCY7W1Xk0\natRQjRw5VpL0j38U6KabrtCSJWdqxozE1zc33CANH/6HhK8nXurTOGnDhhOSsv5Yno796U/S//3f\nrRGna0pDycrLpalTf6Oiop4xL8NNoKC4WPrnPwfp66+7hZwmVMP8xhvn6t57B9a/9cDYtg/pTg2b\n8kTUZR05Upo69aKo5zNzE/Awp+VIhRvjVOAPFCS3HJJ0552X6Zlnfh71fObQg6eeck4D4CQjozbi\nNI1SG0R7QbFPX1lJckO3nLa1WREcPty4r0O04/WIjcfNyd94jGMyUvbkVMWQAIQS7hVn4X5vqrxe\nac+etjp82HhCbtbp1u3gpitjrA4cSNiiEyKtvoUVmBwutdU0w5fTxOMmwE2gIJr1ONUJhw9nGe2j\n+nZcR+3XiadviaaYDWKWqaYmuluDVLi5QnTMdnhzuTY1lHl+N+UgWbT70uhREPnkbaRAQXTT228o\nUjlBX1NgHvjV9Rlz/SdE41YQ/hwF7M9ES6VAQUZG/auemtATqGRg+zQfQXWrt7nUeWESsDWCpnYT\na15rqxOcrT6emto2diMeNwFuAgVu2lbhchRkZNQZPQoCpo+ikA3kz1GQXv97460bjYuhB4FSqUeB\nFNt5n6h75ZTsUWAPFKRygr6mIfCVN7X195CNfcPuz1FAxZRoqRQoSE9PnbLEgh4FCCXUsWFvbHib\nSZ1nr7v9Nz3+zxPZ8GxqN7H+QEFyehTE0uitdVldN6V6MR6JCN31EjB/Rt44TtOkpRmBAsWh910s\ngWf/8FDz7Qdu1xX1qpBk/uOPnSdZ64hU2R7Rn79R9yhwOX2j3Clu3dpRFRXuhw/YK7gdO46Nab1r\n1kivvXZuTPM2tvfekx577IIGL8e64+fPv1gffXSBLyCwZ4+RYKey0vj7jh0dG7y+aJhDSLZuPdr3\n2ezZSomke6lu7tyf64jLREaSP1CwatXZkqSZMwdp3752CSlbJOnpxvn82mvnSZIOHGgTctp33/2x\n3n8/+HjweqXJk3+jzz47LTGFTILZs2/Qzp3+c4HIfnjz51+s8vLohk9VVkoTJvw/7d/f1vU8X311\nsiR/Yi/Tk0/ma926/wr47J13fuy4DK+tB4HX69Ezz/xMZWWttWKFNGvWea7L4+Spp6R//9sYgzxn\nzgBt3drJ97c9e4yEatHmgtm48Xi9805g4rIDB9ro0UeHqrLSSMa4b5/zuWsNIMTzOJ4zZ4BKS7v4\nfv/22+Bpdu2S/vWvn8RtnW49++xPIyatW73a+LlrV2zX2jff7Kf16zv5ljV+/JVRdws31damady4\nn+m7744J+tuKFdKttw7WW2/11CefRLfcUOfknDkDtH59cnIz2Jk3AW++2U9PP31OTMuofxu4q/V4\nvR7Nm/dT7dnTXlOm/EZPPNHfN415U20GFRYvPkMvv3y+JGnFipNUWZmVtN5l5nn8/ffG8Wq2FaXg\nQMmGDcf6koyvWNE95nW++eYQrVr1w5jnX768uyZPDp+P4f33f9Sk25m7dnXQlCl3BtxHPf30pdq8\n+Tg9/PDv9N13XaNeZrKT97377o+1du1xSVm3E3N7JPNB5uHD0v33Gzl4rA/Mt2w5TsOH36q5c38W\ndv5w7ZyqquAH7uU7j9Ojj0bO/9doj5TXrHF+daKT1q2r47LO8eOlO+74Y1yWlWhPPin94x8/bfBy\nrCf93/52rR5//PqgCn7vXuNnTSO/ssvsGVJZ6V/viBFKiaR7qe6hh64KukkJp1WrKknS0qXGMTVl\nyuX69tvoLybx0KFDecDv77zTN+S0N930Z91+e/A5W1srzZp1mQoL+8S9fJEk6mnJP/95c0DSq+bQ\n0yaRT5b+9rdrXWfzNW3fLs2ePVCbNh3veh4zIWxJSU7A57Nm3aKlS08P+GzzZuflBj199Hr04IN/\n0Acf5GrePGncuOgTFVkNHy7de+8wSdL48b8NOC9KSoyEamVloQNyTiZPvlzXXffbgM9KS4/Xv/71\nS23ZYgRP7N/LqUdBPI0f/1u9885/h51m0SLpwQf/JyHrD+eBB66OWB+ZN5exdmcdOzZfU6YYN0GF\nhdKcOb+Ier+aDh9uq1mzzndMtDd6tPTSS7m64YYro15uqHNy/Pjf6u23Q9f1jcnc/itW9FBBwcCE\nr8fr9ej++6/W0qVnaObMy/TAA/7kYmYOj1NO+U6SsZ3uuWek7++bNnVRPN4eE1tdbMxkHmPWnCD2\nHqgPPXSpL8n4li2x3/D9/e8TNG1a7G9Mefvtvpo8+eKw09x//4Am3c785pscPffcCG3f7n9o+vDD\nv9dLL12op5/+H339dfRvZUp2D9+bbvqz7rvvF0lZt5NkB04kafNmad48IxhgDRYWF5+qTz/trYce\nCp/I9+BBI1AwePCSoL/t29c+6LNd357iqlyNFiiIputd27ZHgj5r3boq6nWmylgTNxLVwPZ4vEFP\nt5I1btKfzLC5jNdtXLEkA/R6/edBsnJ92J+ORCqHU0VtfVLTHDg97c3ObmL9qpMg2mM4lnwsZm8c\np3rK7fFnnzfebz3Itr1t0bpdYu3u7jS9+T3MoHKoYzSR56X1VW2S1K5dZYgpG1+kOjkeT6nMJ0Fm\nN/BozgFrGyjc9bfStkmzstw8rDEW3hSGhsZj6EGGi9PJXt849Qyoq/OoTZsKZWYa55I9+afX65En\nyUMPTFWWZre9HWl9w5aZhyhW9tfJRqMl5PYx94u9zW7WP/Y60g1yFATytzGTXwYp8J7QbZnMur1d\nu4qQf7Ny2y5pxECB+wM5XjfNadyPSgquCJI1btLsMhlr18mWLtyrL0Opq0vzXWRSpUEXKVDk9D39\neTUa/6KWiCCeP18I50I0og0yxhIoiEc26KAcBXFujNmPSetxZJ4r0QaCnd5WYC63piaz/qd9+ye2\nR4HzOgMlc3x0pDc81NUZOVoalkQvcLx4rNvavNGLdx3aFAL/ycpR4HQtq6tLq09aaGw35+TdyelR\nEC5QYN/P1hv0ht5c8crs8Mzjyd5mN/dxXV3sgYLUGZOfXP5zN3n1WWCgwGv53F2ZzOu188O24GXU\nuWx/JnyLmF82GTcp6alxX5Rk3qADJFmBAl6PGBuzAvN43Ld2/NHiNF/lkSqv6IrUKHBuXBk/kxH9\nTmSgoCk0slNJrD0Kojlu6urSlJ5e51hPuV1O0OsR4/wqKvsxaT2OYq3fQ93USMYTq/T0mpDHayLr\n9EhPy5IZKIgUvDUCBXUN2u9mfWnu11iDi9EEwKK58WsKwc54BAqiWY+5jZ0CSXV1nvrgkTGNPfFw\nVVVm0o5p+01SYKAgsFBm7iFjvoYVuCHXQX9bp0FFSGn+HgX2QEFdwN+jkQpd7VNpn6VejwJroMBt\nT8bQw0kcl+EyKJLwGt6Mlu7eHT7pT2LW3eirlGQkk7rnnuF66aULNGnS5a7mSdSF4ZtvemvbtpN8\nv3/44Wm+g3HbNiNJ0ty50uzZ/Z1mj4u9e6W//e1qHT5sJD0K94Ro9Wrpr38dpsOH3Se/jFZpaRdN\nmHCvams9mjBBevXV6Md3NaYdO4yf1ov4o48O1eLFZ0iSliwJTOZVUSH97W/XSJKee26E1q41chM4\njU2N1pIl0t//fnXYm/3y8izNnDko5IXo66+7adKky7V791GaOnWIvvzy5IC/m42r6up0jRv3N61c\neWrMr7f6+mvjXDx40D+ud/164zO3yR3fecdcd8NP0i+/lP7yl1/qlluulyR9+GEfPfts+DHY0fj6\n6+P1l784nz9er/TAA1c1WkLITz+VevV6Ups2dXb8+8cfn6xnnrlGDzwg3Xzzb7RggXE8f//90brz\nzkEBiXnGjTN+rlwZXdnN4+b77zvq7ruHByT9C8Xr9YQJFDjPc+hQtm26wHnLdwWv9557hgck6rOa\nNm2Iiop6OP5NCr5eLFp0lv7612GqrMz0fec9e44KOb9pyZIzNGfOAD3//Nm+3CEHD7bRzp3Svfde\nq7Ky1pKMoQcZGdXas6etDh5sbSlH6B4Fc+dK06efr1tv/ZMuv3yEpk79td5//0eSpA8/lO6//398\nSdycvPyy8fO9986O+D0a25b6V9u77VGwc2dH/fWvwxzrnLo6j8aN+63Kylrr2Wd/GnS+fPBBd33x\nxckxDT2wCtejYNs2+7SR6zrzGFy2rKcOHGij22+/TOvXn6DHHpOKiow2x4IFofdvY3I6b99772wt\nW3ZS8B8iLGP8+CsdE4NJ/vrm0CGjrWMNJHm90tSpQ/Tuu/8tr9ejrVuP069+9YB27gxMdLl27Q8k\np+70LrrYz5w5Rl9/beRWCdclv7paeuih3wclGbdvp88+s/4tTbNm/UpLlvxQK1b8SIsXn+r725Ej\nWbrnnuHas8c/DrquzqP77/+DVq8+K2K5nfIn7dpltDVCbWt/uQIDaU3BypVGO9dp+z/66F/06ac9\nAz432z0HDhjXxNLSwOVNmHBf0DyRmNvt4YcfUEmJP/fO0qW9tWhR5H0WD8kMUqxda1zjzCThTu3V\nJ54YpdLSwESb1dVSQcG1Ki4+RTNmSEVFgbmMorV1q3F/VFWVHjJQEKqXw0svXaDZs/35T8INL3v2\n2fCJEMNptEBBND0K4jXm6KT6a0BjR4jWrpVefvlCTZ58uR5/fFBU8ya6rHPm9A+66brtNiPZS6Ks\nWyfNn/9T31sWwt3sffKJ9OKLF/my7ibC6tXd9NJLRuDi1lulSZPyEraueNi40fhpbSD+61+/9DXs\np0wJTOa1fbv06ae9fb9//rlxc2U2+hvivfek5577qSoqskNOs2ZNF02ZcnnIJ1gHDrTT448P0rff\nnqgZM4bo/fcDbwQ6d95fP11bvfrq77VsWc+YupBLxs3qyy9fGHCD+PnnxmdG0qjIWtdvtnDf2a3C\nQunZZ/tqyRLjpnjBgvP1l7/8SlJ86r3ly3P00kvO509VlfTssz+POiFgrCZONH6uXn2y49+ffLKf\npkwZo3vukRYsOFN33HGZJKOh/PzzPw7YP3fcYfyMlGXezjxu1q37gV555UKVlES+Mair8wR0DZZC\nP305/vg9kqQdOwIzyVuP08xWVdq3Mad++r2+z19++ULfGxbspk8fov/8p1/IMtoDBatWnaIXX7xI\nu3Yd7fvO5psKIhk//rd68MFLfb9v3dpJ33wj/fvfF+u774zkWbW1GcrICJ1Dw7pdsrON8e1//7s0\nYcLP9J///EQrV56oGTMGa9q0CyUZgZ/Zs38SkMTN7s47jZ8bNvwgbPmT8fT1iy+Mn5Fe/VpXZ4zf\nXru2q1588SLHOufIkUw99dQAbdvWSQ88cLXmzr00aJpZsy6LaeiB2xwFxzUg+fh33x2rLVs669//\nPltffvlDjRolzZhhBAi2bHEOEjY2p5vIG2+8UWPG/CbqZc2Z84uQ7RNze3/3nXG9sR4fdXUezZgx\nRB9+mKtTTvlOe/Z00IYNP9Du3UcHLGPv3qMcrwW/uvkNXTLi/YD12D311PX6+OPTnf8YsA5p7txL\ng+pT+7GxZYt02mlbfH+bPPkKzZ//Y91110iVlwfWLy+/fKFKS/3JXY8cydS8eT/TZ59Ffgh1+HBw\n22TNGqOtsX9/cBK2wDI3vUDBRx8Z7Vx7kKi6Ol3//ve1QUlzzXPePC5WrTI+N/dXZWWboHkiMZdZ\nW5uhlSv915r8/JuVn39zVMuKVY8eOxtlPU6WLzeucbt3GwF1p3wy//rXaH3ySeAbNfbulV544WJ9\n+mkvXX+9NGNGw95W99VXxv3RgQPtAo5hM4eJvUxWjz46VBMm/DZoOqc6/umnY0/422hDD2JJttHw\ndat+3Y3btcA/ntr9euPV7SXS/F5v8LrcJOlpCHN7HDmSVf976IZOQ5+auCtPWsDPhibiSbRox+fb\n330cz27u0ewf+1CTE07YLclfJ4T6PmYiLX+357SYx+c6nYvRHmMdOkQ3fTLZj+3Avxk/G2u4Q3V1\nYJnsgnOnBJbdqZzRJvQ06zozUO3uuA3uURAq8dMJJ+x1LKt1uv/60Xeqq1+/vX4Oty/C3QCHylFg\nPVeiOV6zsqyNEv8yzLqjpiZDmZn+BHfHHbcvoBzm923btsz370yH/GTm9G56+5nTtm6dOskLTW7P\nJbNHgZkvwmmfuK2fGzr0wAx8OdWhbWJ7kYIk46l5uHonFYS6iYwm74/13A3Vk8Rf3wTXYf56zeN7\n44GTqqoMx94D7TuV6YI/fCR5vL6hTLEyhxQED0sNXG5trfSzn32mdu0qA67nZu9Qu8AhUO6TVzsF\nId3WY00xUBCq/rBfA/2fe2x/Nz63PoCN9tyz1gPWcjTmU/727ZNXt9v3QahhivZgsL093tCeptbl\nWY9hp7rDLjMzsGxuh3enZ0aXOLsRexS4X1W8nhDU1G+Lxh6bHUtDzd8ISECBAtaTFtSjINFPZMz9\nYHazCncsNMbNjP0ClupZc6M9nkIFCuLRiItm//i7BKbZfjcj2c7LsAf4Ao/Z2AIFTsne3CeIiW3d\nyRAuomyeh4lszFvPJXN97gNc9sZQw8tpf8OLu+PWSDZmLXeoRkGoGyRrY8OT5lVdrfPNYKzf0V5n\n+7uVB9fv0bIGG6yBgoyMass0gYF/c7ukpdW6amhGlz8o/PKS0aPA/U2McTNqNuid9ol5bIa7Lno8\n3gYnM4ymYetu6EFwsq1UzbkSOlAQ27U/1MMFM5jgFJi0biN7XgKrI0cyQ7RJjM+Mv0W3f+zMQIG9\np699v9fWGsdverpXbm5CrMewv26Mrf3t9jrtr/tiWk1S+OsP5+tJqACOf5san1v3X7TnnnVfW+vz\nxmznJDO/jL0OD9VrMPitXYHzxbMc1mPYWg63db41GBlOWpSJ0RstUJCMC4i9sdPY642lR0FDK7tI\niV2sUavGiv7bexSE2y7J6FGQ6uzHk79CCz+9qbF7FPjfshAYIPBnUE8P+Bl6Of79FOvNTzx6FMTz\nmEx0Y6Zp9SgI/N1Nj4JoNaRHQeATF+cATKjPAwIFHq+vR0Hwk6L4BApMtbXW+j3WG8rgQIEx9MAa\nKAgMspr70thukdcRTaDAbS+7xhxiGE2PgoyM2rDHn5v62ejhYq7b/TFj3SbhchQ0PGt9al9TQ32/\nRPUoMG+8rPvUHAt95Ehm2EBBVVVm2HwEHpc9CsLdhIXqUeAUKEhL8yotzRv2+DEF9igI/0AgklA3\n08HrbP49CqyBYOv8gQ9Aog0UBK/Xuq7mruE9CkL30Iq1HIGBAv+/3QZv7A/nQon29asJPyLMpFpm\nwUeMkK666nlVVhqV5rffSlOmBI4B2bKlo3JzH9fBg220dKnxWUVFlrZu7aR77x2oRx6RXn31DN/0\nr7wijRo1VLfe+iffeBPJSLxmrKOrbr/9j6qszNRjj0m//OU7evXV8yRJ48dLH310iiR/4jNzB915\np/TWW5EThOzZo4B12yu4uXN/LkkaN+63Wr/+hIB5n3xSevjhn1nmibg6LVggPf74uQGf1dRId901\n0jem9IorRjjOu29f66AD3byg3HnnyKhfqeWG+WTRTJDy0kt9Qh74ib6Z2bFD+stfRtSvwxz3FXr6\nhQsDEwU6efBB6Z13QiceC+XLL43jpqrKaCgeOSLdfvsfVVh4lm65Jd+XEMncJvPnX6KCgl9EvGm2\nH0PmOLhoLyRTphjJLx955Eq99tq5AcuuqMjWXXeN9JXRtHix9Oc/D5Vk5CKQ5Dsmd+0yymE2mkPl\nCPjii1P03HM/1tix10kKvHGJVClXVEi33fZHbd9+TEB549OjIH6BFifmcfjSS9KLL/aRJD388O+0\natUpIef597+liRMvliTdd598CQET2aPgn/+UHn3UWKfXayRl+v77o+t/9+8fM1DgVJZBgx7QBx90\nD/rc65XuvntkfTmd9/XDD0tPPOEu+ao9UBDpgvv119LHH5+uiopMX7n37pVuvvkGSUaSsltv/ZNK\nS7vI65W++uqH9U970zR9unEduuOO/wsITtfWpOvQbuN4XLXq1ID17d59lO68c6SvDnDL7Lp/zTV3\n2L5vmqvjdfz4K0P+zdqjwBzD/Ne/TlJ1tT/xVlVVpv7wh7u0caOR/8TMe5GeXqOamnRfotxQrIGC\nb7/tqr59Z6h//8f0wQen6ZFHpKVL/bkbKiuzNXXqhb7fy8uNOmfKFGMsfKTAaSKY2ydUjglTuB4F\nFRVG/b9w4TmS5Bv3/tFHxjl88KB/OWlpdb51lpW10S235GvcuN+qV68nw9YPpjvu+D9de+1rktzd\nCNTWpuvAgTaaO1c666x/6s03+2nmTGnatAt1550jNWXKb/TNN/7x6Ndcc1fQ9wu/fOmuu0b46ulE\ns99EFhRcK0nasKGT7r5buu66K/Xwwz93vby33jrH8XN7j4KPP/bnCjL3r/lWlVC++eZEVVcEj9k3\nG/fGT6MemzXL2Cder9F2NcoQ+abCHONeU5OmJ5+U5s8/W7fd9seghHhmj4K0NK/+8Y97JUnvvddD\ne/c6J0rdseNY/fGPt2jEiBd958acOX/Wd98F7+fvv5dGj74hZBnt9dhDD/1eixadqaVLjTazZLSp\nP/mkV/10/nn37ZNuuSVfmzZ11rx50qZNxvo/+cRoY02dOkRPP903zBYKr6bGOKe+/tqf8+azz4wE\nrW44tUv275fGjDGSHBcW9lGvXk9q9eqT6s+VwGtiqB4FVVVGnXLFFe+rpMQ5yV51tbENzCTXkvT4\n42NUVZURtn2ybp30l78Mi2s9+/zz0SeqHT/e3x51q6Dg2oBzUQq+TzO/18GDbVRV5T82J0woCEi4\nHerB0bvvSs899+OoymVfnn3oQVGRgtoT8+dLDz30c+3YoYBcKXv3SqNHj5KkiNeEdseUR1XGBrUY\nFy9erJ49e+q0007T1KlTQ053+unf6aijDkmSZs+W1q7t7svI/Mor0qRJFwfNc+RIlrZvP0bz5/s/\n++qrkzV37jm6/XZp6lR/cOGNN6Q33+yt//znJwE3Hzvr82SsWnWKXn/9XO3e3UGjRkm7dnXWiy8a\nDY/bbvPfCH72mZGQxbxZfvhh46Y2ko0bFbBu/443lvPQQ1dJkp56aoBWrAhsHM+cKT3++PlRDT14\n6CHp4YcDEx4dPCi9+ur5WrfOOPlXrjwx4O8PPmg0EjIzg59MmDcoCxacr7KyBgxWDMHa6OzX72vt\n29cmZMM40T0Kvv3Wuq7I63joocBEgU7uvlt64onwwQQny5cbx42ZCXv3bun118/VK69coLfe6udL\n/mhukzfe+ImefrpfyC5TJvsxZN7ERXuje9NNxnn25JO/0CuvnB+w7J07O+rVV8/3ldE0bZr0/fft\n67+PcZNhreSuuGKlOnQ4FPS53f33/4/vpiqwR0H4RtCOHdIbb5yrDRtOCChvqvQocHN+X3eddNtt\nQyQZCWiWLu0dctoZM/x1YUGBfI338D0KGhYBnzBBmjbNWOeRI0ZSpo0bjfW2aXPEN124HgXr1zsn\nqKupkQ4ebFtfzuD50tLqdOedkc9JU7Q9Csws34cOZfu208aN0ocfGg2aHTuO0X/+8xOtWXOi7/v1\n7r1ddXVpuu8+4zr02mvnBbxlo1X7StVWGYHxvXvbB5yvmzYdrwULznf9Bg7TsUbsTStWBAYoredK\nuO86Z84vbJ8Y3/W448ocgw21tRnas6ezunc3LqoVFdn6/PMf+eb+8kujYZKeXqtu3fZo3brwCQjb\nWb7uJ5/00qFDrXXwYFu9/voZuv12I+mu1cSJlxiltBy6t94qjR//s6iunfFirsseKLUzcxQ4HX87\ndxr1v/lWB7POOuaYMknS5s3+5eTk7PStc9u2TnrrrX566ikjAbGbt5i89tp5KivrUF+m8D0Kunb1\nr7etrxsAABRfSURBVGfUKKm6OlNvvtlPN9wgPfroJVqw4HzNnGkkHh0yZEnA03V/o9v4+YMf7HIs\nj9FeucD3nRPNHih44QWjzZmZWVsf6O+pxx8/L+wyrDk3li1zTghrz1Hw1Vcn+/J5mAFzSTrmGCMK\nZB+b/5OfbNThw63UpuP+oGWbx761R8G99xr7pLLSaLsaZQhMfOfku+/M8qZp2DDprrsu0xtvnKu1\nawPbjUaPgjp5PF69996vQi7PtG7df2nJkjP09dd9At5Q41Tfr10rX0Jma5JX67qNn8a2nDv3Un3w\nwdl65RWjzSwZbert2zvVfxf/vBs3Sm+91U9r1uRo7lz/50uWGG2sGTOGaPLk4PsOtw4cMM4p6w3Z\nu+8aCVrdcGqXlJZKS5caQcJt24zsosXFp6qsrL6Xiaz1sbmcwBwFe/cadcq2bSfpiy9+6LjuffuM\nbWAPcu7d216HDoUuc3Gx9NJLF8X1YeLOnUdF3R6ZM+cXevll9wkEvV7jfA8VKLD31C0ra6O9e/3H\nptebps8++1HI+Ux33indfXd0yevty7MPPVi61Ky7/YHDp56S/vnP87RmTeByNmzwl8meJNN+bRw+\n9Qn9+q6XXZexQS3fm266SbNmzdJ7772nxx57TLt373acrlevHUEb1c1wAPs01ptLa1Ir68a1zpOe\nbiRGO3w42+FvwZWo2R3LOp2bMTT2+ZyifaGYiQQbOvTALIPZU8OuZ0+jgefxeMM+kU5kj4LDh1up\nR4/NatfuSMj9n+geBdb3Aq9cuS30hFGKJc+B/bgxf7cfr8GNdtX/dN5X9krBn0Qy+m1q7VJsXXZl\nZVZAGZ2YXS2tx9xPfrLRd4yFmzdwbKdHbgMFoc5F69PpWHsUNFagwJim0NXyQiWFS2SPAmuD2dze\nZkPG2qU2XI+CUKznp1M5zWVlZ7tLxhPcoyB8WazrN292rJ9Zj/uqKql16yPKyqpVba1HWZY3XZnH\nviS173RQtVVmfpb0gJsys76Odnhc6KFlsQ3Tqa31KC2tTqeeulu1tZ6g89w0bNin9f8qdFxOWlqd\nTjttV8TriLVHgTUxWqR61Jp0L7s+6XoyAwWRh08Z49mdhhf463vj+5vb2kwsaT8W/W+zCNwn4Y4d\np+PETcO8U6f9qq7OsCSfrHNMTnn++V/aupub1yhjxqOPdn5y5dTWiqfCwsKA30O1q447rsz1Mq3n\nd6in9vYeBYcPZ+u///tbdex42HcdlqS2bY0kbg899M+A+YcOXanKyiy1PiY4UODj8f3Pt0+sx4qb\n896cPlKScWuPAiedOwduP2u9Zz1OneqDSGV2vk6Hrh+s+9h/fKUHXCOt67QmcI1WQ4/f9esLJQVe\n46xlM5nXGVNwjoLAc886baiymdNY95V1Xa1bVzoml4znOVtXJ3k8dcrMrHV1n2QX6bW0VqHaPfZ2\nnTWfkX1fBOZzCJzPPOdjvf6E7lHgCWpfSf42n72M1t9btz4S8Lfq6sBp2x59WJ1P/t51GWNuMR44\ncECSdOGFF+qkk07SpZdeqmXLljlOa82Ka3JzQ2o/IEO9nizUyVFVJbVtW+G7EEc6wGM9EULfnPi/\nY6TuOtEMPQhXhlDdCv1vn7A+cQruep+IC7f5nQ4fzlZmZq0yM2sjBgpiHV8bifVYKS7eKil5yQxD\nBwoCj1f7MeG/yLsbemBesOORpCaaQIFZuVVXp6u62nh6kp5eZ/m+ZpQ8uFzW+iHwrQfubvRCBVms\nn7k9xmprjbInfuiB9TgsbFAXP6fvloggnL3R4fH4r3ShehSE+16RG4/RlT3aHgWB6w8OFJjf02xU\nZWbWKC2tLqis1gt7ekataqqy5PEYx761MWDePFjPIzdd6UPnJ4ntrQfV1ekB38WfVyawMelP4lYY\nqmRh63eTtfFuBkaDluTwHZ3e0BOv/D7RqK2VWrUKHfA2Bfco8J+X9sDwkSOZat260lf3Bd4gpPv2\nifXYkmIJMoXvUVBXZ7zisro6I2SCL1OrVkcCjjN7Mq1Qx2BjBwri0V3aPPbS0upCltueo+Dw4Va+\n88r6EKdVK2MDmAEDU2ZmrQ4fbqU0x1eR+pMZ2nMUON1MuslREOkaaAYKQm0/e9Z16ytZrXWH0/Zy\nqmvt6zZ+Wm+gwuVHCF528MPGkLNHxRqIsHNzrG3YUCjJOXBoZQ8UBL/1wDLErdYT8l7Iqez2Xr3m\nutq0OaKamoyg7xHPc7amxjiu3FwrGipUuUP1KLBvc8n+donA+RoqXI8C/wPgrKD5wgUK3Bz36WHy\npNjF/E2XL1+uHj383R579eqlTz/91HHatDRv0EY1v0i4ysz+ZUO9kiVwA6UHfN62baXlCa1zcMIs\ng3OPgshnvb3ScLoZCHVwmeuO5qmI0zY7ciT4Myt/Uknr09ng3W9vhMSD/+0TmcrMrFZWVk3IfdGY\nPQrcZXZ2t9xYsrdG7lHgfDxVVKj+c3eBAvOCHVuPgsDxW/YbCPt+tG4H+01VVla10tO9vu9rNpzs\nyWSs6zX/7rZHgXkeBAftGpajIDOzttF6FJjb0B8Jjz7insgeBYH72Pjpr5sj5yioCfMgx22PArfB\nPf9TAncJPZ0aZYGBAvPGPt0XKEhP9waV1dpITs+sVW1Vplq1qg4KFDj1KHBzfIa6KXbTo8CpIVtV\nlaGsLP93CdWjINKTnLS0uvr6PbihGar8Tt33PR7n72h9qh3LtTNeamvNm+nIPQpC5SiwB4YrKrLV\ntm1l0PXAmC90L49oewFGOgeM71blC/BKRkPZ6RrXqlV1QJvB/vaDZAUK7NwGkcIds2agwNhHztvc\nqUeBeV5ZexRkZzsHCrKyjIC00+vLfDkKHN56YD1WakK8itVpeqdri/Vpsjn0INQxExwoCNWjIHyg\nwOmpsrUeNI/D6mr/cWg/30MFCqzTRWonu+V0/EbzsM/fnooUKEgPKLO9R4H9rRqh7oWcy24POBrX\ntOzsamVk1AQ9hY7nOWsGoMLdCziJJeAXKqgTukdBcKAg8MFV4HymWN/gEG2PApP9WI46UBDFKxIb\npZbOzKzV/PkXq7jY/9mDD16lJ580kkdJ0iCHoR2PPjpUe+uHLrVpU6UXX/TnJVi//jjfPMuX++eZ\nMWOwFi40/r1li3TSSRVavNhIomfmCpCkNWu6+uZfuvSHGjRIWr/e+P322/+oTvW5mFau7OpYNisz\nF4K57u3bg6e5zBjSp3nzfqqVK/2fr15t/CwqMn5edZW/S2UoZjzGWq6yCD3ozK7jmzYdo1tvNT77\n5pscDRpkjI0yFRRcq5kzwy8rWuZ4OMm4EGZl1eqOO/6o447zf25+FzOHwD/+8f/0wgvxLYe9LIWF\nRv6JTZuOCbmPFy0KLF8obo4Tu3XrjJ9///vV+te/jHFvkrR5s5HrYsKEKzV/fmCZJeMYkYzxa4MG\nyZfw01y/ec4ce+wh7dnTVps3d1br1pVasaJ71GX84gtjbOHXX3fToEH+JEj/+tcvA8po+vBD/7+n\nTx8syUi4MnSose8zMmp9SXbef98Ym/vOO/+tQYNCN+YKC/topJHLR59+2ivsd9i3zyzfQBUWGuMg\nJemxx36tN94w/r1hg/Fz5szL9M474b+/ZGzPtm2PqKDgfzV9euTpw7GPK7M6dChbgwb5999gY/Pp\njTf6a8sW53nMMfX2beJ0/pjjDxcvPjPq48DKrCcHDZIOHzb+PXu2MeZ93bof+JZtHisvvHCRvvrK\nP3+4htQf/uD/98yZl+nttwP/bo67rKjIcvUd9uwxfppjDJ977pKA+tfO/G5ZWTV67rlL9PnnRtIt\n05o1xhjeZ5/9mT76yAh+ZWTUauLEodq61T/dvHk/9f07I6tGh3Yfq6PbV2j58u6+a54krV7dTZK/\nDpD858HSpaeH/I5O1xjJOB/NxtScOQN8CX2tnBpb1dXpOuoo47s89tivfTdF9nGs7duHb2l36LBf\nWVm1mjNngDZtCv77F1/8lwYNUsA+MOthSfrkE2N9H310SlDjxnpuDBrkT/b3yCPGz6FD5dg9PhE2\nbTJu8latOjXscVhUJHXpUu3LX2Stc8y6av9+I2HD4sVnqn37CpWWHq9Bg4ycNab33vux77tZ20GS\ntHBhv5BlsLaNTPa2mBSYu2fvXqlz5yOaNOkKX1D6iy9+qEqHV55nZVUHJEV75RVj7PCKFUYdv3Xr\ncY5lK68fkWDW0/G2Zo2/bpQCE0Na7dnTNuD3QYNCN/jNZbRufUQlJTmO38vMK1FUZDxEq6ho5asj\n5s79udq2rdChQ63VunVwoKBjxz3KyjIqx/QM212ajHpEMl5tVjj+Bg2aJ1+dc801/unMRIurVv0g\n5HFRUmL8fPTRoUF/y8qq9j2pfuMNadiwWmVnB5dHCg7YLl/uf3i4ZIk/4fgTTwzU4sWB81rrsP37\n2wWV1Wz3PPjg731148cfn64vvjD+bZ/e2nY26+xnnvmZr86QjOS/pp07j4r5Omi2txcsOM/XnjCv\n7ZddFvmtLma7/9FHh/qu07sc0nm89tq5AdfOZ575uZYt87fZly/35zz75JPeys/3T2vkLAtepnkc\n28fs//3vV6tDB2P/Z2VVa/DgjIAeXOY6rfdHsaqpMYYpZmXVauzYfB3jMqepeW0sKTnJ9b4zb6g/\n+iiw3WNe6x955Hd69llpxQrj96lTf6Nnnglcxmuvnev7/mY73bxuLV16SsA1LdpjyqwzHnzwqoDt\n/cknvfXll2bZ/eeSea94//3+aQcN8t+Hdu5cpt272waUw6nuzshyH1n3eL2xdco6cOCA8vLytLJ+\n6/z5z3/WgAED9Mtf/tI3zamnnqr15t4AAAAAAAAp4ayzzlKxPYJcL+YeBR06GBl0Fy9erJycHL37\n7ru69957A6ZZ5xTOAgAAAAAAKatBQw8mTZqkP/3pT6qurtaNN96oTg3tjwIAAAAAAJIq5qEHAAAA\nAACg+UlIavnFixerZ8+eOu200zR16tRErAKQJA0fPlxdunTRGWf4k32UlZVp8ODBysnJ0ZAhQ1Re\n7n+X85QpU3TaaaepV69eWmLJ9FVSUqKzzz5bP/zhD3X33Xf7Pq+urtaIESN00kknKS8vTzt27Gic\nL4Ymb8uWLbr44ovVu3dv5eXlad68eZI4PpEaKisr1a9fP/Xp00f9+/fXxIkTJXF8InXU1tYqNzdX\ng+ozc3FsIlV069ZNZ555pnJzc3XOOUYCSY5PNEcJCRTcdNNNmjVrlt577z099thj2m1N3wvE0bBh\nw7TQfM1FvRkzZignJ0dr165V165dNbP+NQ7ff/+9pk+frvfff18zZszQjTfe6Jvnlltu0e23367l\ny5dr0aJFWlGfAvWVV17RgQMHVFJSogEDBuh+a6pRIIzMzExNnDhRq1ev1osvvqh77rlHZWVlHJ9I\nCa1atdKHH36o4uJiLVq0SE888YTWrl3L8YmUMXnyZPXq1Uue+lcRcGwiVXg8HhUWFmrlypUqqk9F\nz/GJ5ijugYID9e+OuPDCC3XSSSfp0ksv1bJly+K9GkCSdMEFF6hjx44BnxUVFWnEiBHKzs7W8OHD\nfcffsmXLNGDAAOXk5Oiiiy6S1+v1RXzXrFmjK6+8Uscee6z+f3t38BLVGsZx/DezUBAlEFEHVAwD\nZyycmcXMCC2CWYQIOqILFXQRRSsXuvMvaOOi3AoOhC7cBRqFBTIkiqOrhFEXoqKCSBbRlJKKz11I\np/LSpbimR/l+dvNyzhwGvog8nHPelpaWn87p7OxUXl6eHj58SMv4baWlpQqFTrbQKSoq0s2bNzU/\nP0+fcI28vDxJ0ufPn3V0dKTc3Fz6hCtsbW3pxYsXevDggb49IUubcJPTT27TJ66iMx8UzM/Py+//\nvpdqTU2NZmdnz/oywC/92KDf73emvel0WoFAwDmuurpa6XRaKysrKi4udtZ/bHZubk41NTWSpMLC\nQu3s7Ojr1//eSxw4bWVlRZlMRtFolD7hGsfHxwoGgyopKVF3d7cqKiroE67Q29ur/v5+eb3f/02l\nTbiFx+NRPB5Xc3OzxsbGJNEnrqb/tesB4EZ/8n7Ob7c0nj7/27qZ/fR9vPsTfyqbzaqtrU2PHz9W\nfn4+fcI1vF6v3r59q/X1dTU0NOj27dv0iQv3/PlzFRcXKxwOK5VKOeu0CbeYnp6Wz+fT0tKSGhsb\nFY1G6RNX0pnfURCJRLS8vOx8zmQyqqurO+vLAL8UiUS0tLQk6eRFMZFIRJIUi8W0uLjoHLe8vKxI\nJKIbN25oZ2fHWV9cXFQsFvvXOR8+fFBJSYlyc3PP66fgkjs8PFRra6u6urqUSCQk0Sfcp7KyUg0N\nDUqn0/SJCzczM6OxsTFdv35dHR0dmpycVFdXF23CNXw+nyQpEAioqalJ4+Pj9Ikr6cwHBdeuXZN0\nsvPB+vq6Xr9+7YQPnIdYLKZkMqn9/X0lk0lnUBWNRjUxMaGNjQ2lUil5vV4VFBRIOrlNbHR0VLu7\nu3r27NlPf6xHRkb05csXDQ4OMvTCbzMz3b9/X7du3VJPT4+zTp9wg93dXX38+FGS9P79e7169UqJ\nRII+ceEePXqkzc1Nra2taXR0VPF4XMPDw7QJV9jb21M2m5UkvXv3ThMTE6qvr6dPXE32F6RSKfP7\n/VZVVWUDAwN/4xKAmZm1t7ebz+eznJwcKysrs2QyaZ8+fbKmpiYrLy+3RCJh2WzWOf7JkydWVVVl\ngUDA3rx546xnMhkLh8NWWVlpfX19zvrBwYHdu3fPysvL7c6dO7a9vX2uvw+X19TUlHk8HgsGgxYK\nhSwUCtnLly/pE66wsLBg4XDYamtr7e7du/b06VMzM/qEq6RSKWtsbDQz2oQ7rK6uWjAYtGAwaPF4\n3IaGhsyMPnE1ecx48AUAAAAAAJw480cPAAAAAADA5cWgAAAAAAAAOBgUAAAAAAAAB4MCAAAAAADg\nYFAAAAAAAAAcDAoAAAAAAICDQQEAAAAAAHAwKAAAAAAAAI5/AIDDzc9T2f9FAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x25916050>"
]
}
],
"prompt_number": 632
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(len(y) ** (1/2.5))+10"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 622,
"text": [
"17.576398942412954"
]
}
],
"prompt_number": 622
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes = pylab.subplots(10, 2, figsize=(8,20))\n",
"axes = itertools.chain(*axes)\n",
"for section in sections:\n",
" sect_length = section[1] - section[0]\n",
" nReads = np.sum(pos_counts[section[0]:section[1]])\n",
" if nReads < 20:\n",
" continue\n",
" sect_read_lengths = rs(read_lengths, nReads)\n",
" sect_read_lengths = [sect_length - 1 if read > sect_length else read for read in sect_read_lengths]\n",
" thresh = get_FDR_cutoff_binom(sect_read_lengths, sect_length, 0.001)\n",
" ax = axes.next()\n",
" data = wiggle[section[0]:section[1]]\n",
" xvals = np.arange(len(data))\n",
" ax.plot(data)\n",
" isf = np.sqrt(len(data))\n",
" ss = SS(xvals, data, smoothingFactor=isf, threshold=thresh)\n",
" ss.plot(ax=ax)\n",
" ax.set_title(\"t:%d, s:%f\" % (thresh, ss.smoothingFactor))\n",
" ax.axhline(thresh)\n",
"pylab.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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2Rvv27WmmdDmAEGDZMmDonMu48zIR/x4djwULIFPbUwBTjyo8nMmJUxU+Fj4Y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\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10yk3HqejQxSBFsuZ4amsj1AW/rjPe7Ig5FKqPAaYV25n7CJwQkNDSU4W1WRgYCBTp04l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\n4BAREZHisMAhIiIixWGBQ0RERIrz/wHtJOXIjpNSKAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x25c4c410>"
]
}
],
"prompt_number": 283
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, ax = pylab.subplots(figsize=(18,4))\n",
"ax = pylab.gca()\n",
"ax.plot(wiggle)\n",
"for peak in gene_peaks['clusters']:\n",
" print peak.thick_start - interval.start, peak.thick_stop- interval.start\n",
" ax.axvspan(peak.genomic_start - interval.start, peak.genomic_stop- interval.start, color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"25996 26000\n",
"35832 35836\n",
"35975 35979\n",
"36215 36219\n",
"56997 57001\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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0S1sSli71nxEeJsq5uakJGD/ef265jcv2dSUrlCtWAMuXi/erVhU+irGl0PfV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\n3TRd3oIMSbZkyFOebkni7h9T8NWWv7PUXSLKXV3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v36q8vFxFRUW/6qvgN/fp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\nC4YMAAAAAAAgL/4Cq8J1h9lrdmoAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2560a410>"
]
}
],
"prompt_number": 255
}
],
"metadata": {}
}
]
}
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