Python pylab.hist方法代码示例

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def summary(self, Nbest=5, lw=2, plot=True, method="sumsquare_error"):
        """Plots the distribution of the data and Nbest distribution

        """
        if plot:
            pylab.clf()
            self.hist()
            self.plot_pdf(Nbest=Nbest, lw=lw, method=method)
            pylab.grid(True)

        Nbest = min(Nbest, len(self.distributions))
        try:
            names = self.df_errors.sort_values(
                by=method).index[0:Nbest]
        except:
            names = self.df_errors.sort(method).index[0:Nbest]
        return self.df_errors.loc[names] 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def plotVerticalHistSummary(all_result_outputs, label='', data_label='', y_label='', plot_label='', hist_width=1000, hist_bins=100, oligo_id_str='Oligo ID', val_str = 'Cut Rate', total_reads_str= 'Total Reads'):
   
    datas = [x[0][data_label][0] for x in all_result_outputs]
    sample_names = [shortDirLabel(x[1]) for x in all_result_outputs]
    
    merged_data = pd.merge(datas[0],datas[1],how='inner',on=oligo_id_str, suffixes=['', ' 2'])
    for i, data in enumerate(datas[2:]):
        merged_data = pd.merge(merged_data, data,how='inner',on=oligo_id_str, suffixes=['', ' %d' % (i+3)])
    suffix = lambda i: ' %d' % (i+1) if i > 0 else ''

    xpos = [x*hist_width for x in range(len(sample_names))]

    PL.figure(figsize=(12,8))
    for i,label1 in enumerate(sample_names):
        dvs = merged_data[val_str + suffix(i)]
        PL.hist(dvs, bins=hist_bins, bottom=i*hist_width, orientation='horizontal')
    PL.xticks(xpos, sample_names, rotation='vertical')
    PL.ylabel(y_label)   
    PL.title(label)
    PL.show(block=False)
    PL.savefig(getPlotDir() + '/%s_%s.png' % (plot_label, label.replace(' ','_')), bbox_inches='tight') 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def hist_overflow(val, val_max, **kwds):
    """ Make a histogram with an overflow bar above val_max """
    import pylab, numpy

    overflow = len(val[val>=val_max])
    pylab.hist(val[val<val_max], **kwds)

    if 'color' in kwds:
        color = kwds['color']
    else:
        color = None

    if overflow > 0:
        rect = pylab.bar(val_max+0.05, overflow, .5, color=color)[0]
        pylab.text(rect.get_x(),
                   1.10*rect.get_height(), '%s+' % val_max) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def show_statistics(self):
        """
        Shows several statistics of the data. The first plot shows the distribution of the number of cells per node
        in the topological representation. The second plot shows the distribution of the number of common cells
        between nodes that share an edge in the topological representation. The third plot contains the distribution
        of the number of nodes that contain the same cell. Finally, the fourth plot shows the distribution of
        transcripts in log_2(1+TPM) scale, after filtering.
        """
        x = map(len, self.dic.values())
        pylab.figure()
        pylab.hist(x, max(x)-1, alpha=0.6, color='b')
        pylab.xlabel('Cells per node')
        x = []
        for q in self.g.edges():
            x.append(len(set(self.dic[q[0]]).intersection(self.dic[q[1]])))
        pylab.figure()
        pylab.hist(x, max(x)-1, alpha=0.6, color='g')
        pylab.xlabel('Shared cells between connected nodes')
        pel = []
        for m in self.dic.values():
            pel += list(m)
        q = []
        for m in range(max(pel)+1):
            o = pel.count(m)
            if o > 0:
                q.append(o)
        pylab.figure()
        pylab.hist(q, max(q)-1, alpha=0.6, color='r')
        pylab.xlabel('Number of nodes containing the same cell')
        pylab.figure()
        r = []
        for m in self.dicgenes.keys():
            r += list(self.dicgenes[m])
        r = [k for k in r if 30 > k > 0.0]
        pylab.hist(r, 100, alpha=0.6)
        pylab.xlabel('Expression')
        pylab.show() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def pvalhist(pv,numbins=50,linewidth=3.0,linespec='--r', figsize=[5,5]):    
    '''
    Plots normalized histogram, plus theoretical null-only line.
    '''    
    h2=pl.figure(figsize=figsize)      
    [nn,bins,patches]=pl.hist(pv,numbins,normed=True)    
    pl.plot([0, 1],[1,1],linespec,linewidth=linewidth) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def __init__(self, data=None, X=None, Y=None, bins=None):
        """.. rubric:: **Constructor**

        One should provide either the parameter **data** alone, or the X and Y
        parameters, which are the histogram of some data sample.

        :param data: random data
        :param X: evenly spaced X data
        :param Y: probability density of the data
        :param bins: if data is providede, we will compute the probability using
            hist function and bins may be provided. 

        """

        self.data = data
        if data:
            Y, X, _ = pylab.hist(self.data, bins=bins, density=True)
            self.N = len(X) - 1
            self.X = [(X[i]+X[i+1])/2 for i in range(self.N)]
            self.Y = Y
            self.A = 1
            self.guess_std = pylab.std(self.data)
            self.guess_mean = pylab.mean(self.data)
            self.guess_amp = 1
        else:
            self.X = X
            self.Y = Y
            self.Y = self.Y / sum(self.Y)
            if len(self.X) == len(self.Y) + 1 :
                self.X = [(X[i]+X[i+1])/2 for i in range(len(X)-1)]

            self.N = len(self.X)
            self.guess_mean = self.X[int(self.N/2)]
            self.guess_std = sqrt(sum((self.X - mean(self.X))**2)/self.N)/(sqrt(2*3.14))
            self.guess_amp = 1.

        self.func = self._func_normal 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def hist(self):
        """Draw normed histogram of the data using :attr:`bins`


        .. plot::

            >>> from scipy import stats
            >>> data = stats.gamma.rvs(2, loc=1.5, scale=2, size=20000)
            >>> # We then create the Fitter object
            >>> import fitter
            >>> fitter.Fitter(data).hist()

        """
        _ = pylab.hist(self._data, bins=self.bins, density=self._density)
        pylab.grid(True) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def test2():
    from fitter import HistFit
    from pylab import hist
    import scipy.stats
    data = [scipy.stats.norm.rvs(2,3.4) for x in  range(10000)]
    Y, X, _ = hist(data, bins=30)
    hf = HistFit(X=X, Y=Y)
    hf.fit(error_rate=0.03, Nfit=20)
    print(hf.mu, hf.sigma, hf.amplitude) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def hist(arg):
    try:
        import pylab
    except ImportError:
        logger.warning("pylab is not installed and required")
        return
    plot(pylab.hist(arg)[1]) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def run(opts):
	arg_permutations = str(opts.input[0])
	perm_tfce_max = np.genfromtxt(arg_permutations, delimiter=',')
	p_array=np.zeros(perm_tfce_max.shape)
	sorted_perm_tfce_max=sorted(perm_tfce_max, reverse=True)
	num_perm=perm_tfce_max.shape[0]
	perm_tfce_mean = perm_tfce_max.mean()
	perm_tfce_std = perm_tfce_max.std()
	perm_tfce_max_val = int(sorted_perm_tfce_max[0])
	perm_tfce_min_val = int(sorted_perm_tfce_max[(num_perm-1)])

	for j in range(num_perm):
		p_array[j] = 1 - np.true_divide(j,num_perm)

	sig=int(num_perm*0.05)
	firstquater=sorted_perm_tfce_max[int(num_perm*0.75)]
	median=sorted_perm_tfce_max[int(num_perm*0.50)]
	thirdquater=sorted_perm_tfce_max[int(num_perm*0.25)]
	sig_tfce=sorted_perm_tfce_max[sig]
	pl.hist(perm_tfce_max, 100, range=[0,perm_tfce_max_val], label='Max TFCE scores')
	ylim = pl.ylim()

	pl.plot([sig_tfce,sig_tfce], ylim, '--g', linewidth=3,label='P[FWE]=0.05')
	pl.text((sig_tfce*1.4),(ylim[1]*.5), r"$\mu=%0.2f,\ \sigma=%0.2f$" "\n" r"$Critical\ TFCE\ value=%0.0f$" "\n" r"$[%d,\ %d,\ %d,\ %d,\ %d]$" % (perm_tfce_mean,perm_tfce_std,sig_tfce,perm_tfce_min_val,firstquater, median, thirdquater, perm_tfce_max_val), size='medium')
	pl.ylim(ylim)
	pl.legend()
	pl.xlabel('Permutation scores')
	save("%s.hist" % arg_permutations, ext="png", close=False, verbose=True)
	pl.show() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import hist [as 别名]
def draw_var(self, data, name):
        plt.figure(figsize=(12, 9)) 
        ax = plt.subplot(111)    
        bar_width = 0.5
        bar_l = [i + 1 for i in range(len(data))]
        tick_pos = [ i + (bar_width/2) for i in bar_l ]

        acc = map(lambda a: (a.acc_in*a.tot_in + a.acc_out*a.tot_out)/(a.tot_in + a.tot_out), data)
        fp = map(lambda a: float(a.fp_in + a.fp_out)/(a.tot_in + a.tot_out), data)
        fn = map(lambda a: float(a.fn_in + a.fn_out)/(a.tot_in + a.tot_out), data)

        print("average/standard deviation:")
        print("| accuracy: {:.3g}/{:.3g}".format(numpy.mean(acc), numpy.std(acc)))
        print("| false positive: {:.3g}/{:.3g}".format(numpy.mean(fp), numpy.std(fp)))
        print("| false negative: {:.3g}/{:.3g}".format(numpy.mean(fn), numpy.std(fn)))

        ax.bar(bar_l, acc, width=bar_width, label="accuracy", 
                alpha=1, color=Chart.colors["acc"])
        ax.bar(bar_l, fn, width=bar_width, label="false negatives", 
                alpha=1, bottom=acc, color=Chart.colors["fn"])
        ax.bar(bar_l, fp, width=bar_width, label="false positives", 
                alpha=1, bottom=map(lambda a: a[0] + a[1], zip(acc, fn)), color=Chart.colors["fp"])
                
        # Limit the range of the plot to only where the data is.    
        # Avoid unnecessary whitespace.    
        plt.ylim(0.9, 1.01)    
        plt.xlim(0, len(data) * 1.05)

        ax.spines["top"].set_visible(False)    
        ax.spines["bottom"].set_visible(False)
        ax.spines["right"].set_visible(False)    
        ax.spines["left"].set_visible(False)

        # Ensure that the axis ticks only show up on the bottom and left of the plot.    
        # Ticks on the right and top of the plot are generally unnecessary chartjunk.    
        ax.get_xaxis().tick_bottom()    
        ax.get_yaxis().tick_left()    

        plt.xticks(tick_pos, map(lambda a: a.tot_in + a.tot_out, data), rotation="vertical")
        plt.tick_params(axis="both", which="both", bottom="off", top="off",    
                labelbottom="on", left="off", right="off", labelleft="on") 
        plt.legend()

        plt.savefig("test/chart/{}_{}.png".format(self._analysis, name), bbox_inches="tight") 

        return

        P.figure(figsize=(12,9))
        P.hist([[10, 20], [1, 5]], 5, stacked=True, histtype='bar')
        # plt.plot(range(1, len(data) + 1), acc_in, 'o', lw=1, color=Chart.colors[1], label="accuracy")
        # plt.plot(range(1, len(data) + 1), tot_in, 'o', lw=1, color=Chart.colors[3], label="number of functions")
        # plt.plot(range(1, len(data) + 1), fp_in, 'o', lw=1, color=Chart.colors[7], label="false positives")
        # plt.plot(range(1, len(data) + 1), fn_in, 'o', lw=1, color=Chart.colors[6], label="false negatives")
        # plt.legend()

        P.savefig("test/chart/{}_{}.png".format(self._analysis, name), bbox_inches="tight") 
        return