Python pyplot.xscale函数代码示例

def make_coefficient_plot(table, positive_words, negative_words, l2_penalty_list):
    cmap_positive = plt.get_cmap('Reds')
    cmap_negative = plt.get_cmap('Blues')

    xx = l2_penalty_list
    plt.plot(xx, [0.]*len(xx), '--', lw=1, color='k')

    table_positive_words = table.filter_by(column_name='word', values=positive_words)
    table_negative_words = table.filter_by(column_name='word', values=negative_words)
    del table_positive_words['word']
    del table_negative_words['word']

    for i in xrange(len(positive_words)):
        color = cmap_positive(0.8*((i+1)/(len(positive_words)*1.2)+0.15))
        plt.plot(xx, table_positive_words[i:i+1].to_numpy().flatten(),
                 '-', label=positive_words[i], linewidth=4.0, color=color)

    for i in xrange(len(negative_words)):
        color = cmap_negative(0.8*((i+1)/(len(negative_words)*1.2)+0.15))
        plt.plot(xx, table_negative_words[i:i+1].to_numpy().flatten(),
                 '-', label=negative_words[i], linewidth=4.0, color=color)

    plt.legend(loc='best', ncol=3, prop={'size':16}, columnspacing=0.5)
    plt.axis([1, 1e5, -1, 2])
    plt.title('Coefficient path')
    plt.xlabel('L2 penalty ($\lambda$)')
    plt.ylabel('Coefficient value')
    plt.xscale('log')
    plt.rcParams.update({'font.size': 18})
    plt.tight_layout()

def main():

    sample='q'
    sm_bin='10.0_10.5'
    catalogue = 'sm_9.5_s0.2_sfr_c-0.75_250'

    #load in fiducial mock
    filepath = './'
    filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'_cov.npy'
    cov = np.matrix(np.load(filepath+filename))
    diag = np.diagonal(cov)
    filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
    filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
    data = ascii.read(filepath+filename)
    rbins = np.array(data['r'])
    mu = np.array(data['wp'])
    
    #load in comparison mock
    
    
    
    
    plt.figure()
    plt.errorbar(rbins, mu, yerr=np.sqrt(np.diagonal(cov)), color='black')
    plt.plot(rbins, wp,  color='red')
    plt.xscale('log')
    plt.yscale('log')
    plt.show()
    
    inv_cov = cov.I
    Y = np.matrix((wp-mu))
    
    X = Y*inv_cov*Y.T
    
    print(X)

def plot_figure_commute(file_name):
    f = open(file_name)
    lines = f.readlines()
    lines[0] = lines[0][:-1]
    f.close()

    probabilities = lines[0].split(" ")
    xx = xrange(len(probabilities))
    probabilities = [float(x) for x in probabilities]

    plt.figure(figsize=(12, 10))
    plt.plot(xx, probabilities, label="D(degree > x)")
    plt.ylabel('Percentage of vertices which degree> x')
    plt.xlabel('Degree')
    plt.title("Cumulative function for degrees")
    plt.legend(loc='upper right')

    name = "results_graphs/"
    if file_name == "temp_degrees/cumulative_temp.txt":
        name += "ful_cumulative"
    else:
        name += "good_cumulative"

    plt.savefig(name + '.png')

    plt.figure(figsize=(12, 10))
    plt.plot(xx, probabilities, label="log(D(degree > x))")
    plt.legend(loc='upper right')
    plt.title("Cumulative function for degrees in log/log coords")
    plt.ylabel('Log of percentage of vertices which degree> x')
    plt.xlabel('Log of degrees')
    plt.xscale('log')
    plt.yscale('log')

    plt.savefig(name + '_log' + '.png')

def pareto_graph(database, objectives=None):
	'''Constructs a visualization of the movement of the best front over generations

	The argument 'objectives' must be a list indicating the indices of the fitness values to be used.
	The first two will be consumed. If the list has less than two elements, or if is not given, the
	graph will be produced using the first two fitness values.
	'''
	if objectives is None or len(objectives) < 2:
		objectives = [0, 1]

	generations = []
	if database.properties['highest_population'] < FRONT_COUNT:
		generations = list(range(1, database.properties['highest_population'] + 1))
	else:
		step = database.properties['highest_population'] / FRONT_COUNT
		generations = [round(i * step) for i in range(1, FRONT_COUNT + 1)]

	for i, gen in enumerate(generations, start=1):
		database.select(gen)
		individual_data = [val for key, val in database.load_report().items()
			if key.startswith('I') and val['rank'] == 1]
		x_values = [val['fitness'][objectives[0]] for val in individual_data]
		y_values = [val['fitness'][objectives[1]] for val in individual_data]
		plt.plot(x_values, y_values,
			color=str((FRONT_COUNT - i) / FRONT_COUNT),
			linestyle='None',
			marker='o',
			markeredgecolor='white')

	plt.title('Movement of best front')
	plt.xscale('log')
	plt.yscale('log')
	plt.xlabel(database.properties['objective_names'][objectives[0]])
	plt.ylabel(database.properties['objective_names'][objectives[1]])
	plt.show()

    def fluence_dist(self):
        """ Plots the fluence distribution and gives the mean and median fluence
        values of the sample """
        fluences = []
        for i in range(0,len(self.fluences),1):
            try:
                fluences.append(float(self.fluences[i]))

            except ValueError:
                continue

        fluences = np.array(fluences)
        mean_fluence = np.mean(fluences)
        median_fluence = np.median(fluences)
        print('Mean Fluence =',mean_fluence,'(15-150 keV) [10^-7 erg cm^-2]')
        print('Median Fluence =',median_fluence,'(15-150 keV) [10^-7 erg cm^-2]')

        plt.figure()
        plt.xlabel('Fluence (15-150 keV) [$10^{-7}$ erg cm$^{-2}$]')
        plt.ylabel('Number of GRBs')
        plt.xscale('log')
        minimum, maximum = min(fluences), max(fluences)
        plt.axvline(mean_fluence,color='red',linestyle='-')
        plt.axvline(median_fluence,color='blue',linestyle='-')
        plt.hist(fluences,bins= 10**np.linspace(np.log10(minimum),np.log10(maximum),20),color='grey',alpha=0.5)
        plt.show()

def dose_plot(df,err,cols,scale='linear'):
    n_rows = int(np.ceil(len(cols)/3.0))
    plt.figure(figsize=(20,4 * n_rows))
    subs = gridspec.GridSpec(n_rows, 3) 
    plt.subplots_adjust(hspace=0.54,wspace=0.27)

    for col,sub in zip(cols,subs):
        plt.subplot(sub)
        for base in df['Base'].unique():
            for drug in get_drugs_with_multiple_doses(filter_rows(df,'Base',base)):
                data = thread_first(df,
                                    (filter_rows,'Drug',drug),
                                    (filter_rows,'Base',base),
                                    (DF.sort, 'Dose'))
                error = thread_first(err,
                                     (filter_rows,'Drug',drug),
                                     (filter_rows,'Base',base),
                                     (DF.sort, 'Dose'))
                if scale == 'linear':
                    plt.errorbar(data['Dose'],data[col],yerr=error[col])
                    title = "{} vs. Dose".format(col)
                else: 
                    plt.errorbar(data['Dose'],data[col],yerr=error[col])
                    plt.xscale('log')
                    title = "{} vs. Dose (Log Scale)".format(col)
                    plt.xticks(data['Dose'].values,data['Dose'].values)
                    plt.xlim(0.06,15)
                label('Dose ({})'.format(data.Unit.values[0]), col,title,fontsize = 15)

                plt.legend(df['Base'].unique(), loc = 0)

def set_axis_properties(p,metric,varying_parameter,group):

    #Set major x-axis label
    plt.xlabel(xlabel_names[varying_parameter])

    #Set x-axis scale
    xscale_args = xscale_arguments[(metric,varying_parameter)]
    plt.xscale(xscale_args[0],**xscale_args[1])

    #Set x-axis tick labels
    #Get tick values
    ticks = list(sp.unique(group[varying_parameter]))

    #If an item is not in the tick dictionary for the bar plot, add it
    if pltkind[(metric,varying_parameter)] is 'bar':
        for item in ticks:
            if item not in varying_xlabels[varying_parameter].keys():
                varying_xlabels[varying_parameter][item] = '$' + str(item) +'$'

    xlabels = [ varying_xlabels[varying_parameter][item] for item in ticks]

    if pltkind[(metric,varying_parameter)] is 'bar':
        p.set_xticks(sp.arange(len(ticks))+0.5)
        plt.setp(p.set_xticklabels(xlabels), rotation=0)
    else:
        plt.xticks(ticks,xlabels)

    plt.ylabel(ylabel_names[metric])
    plt.grid('on')

def plotCurves(losses,rateOfExceedance,return_periods,lossLevels):

    plt.figure(1, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
    plt.scatter(losses,rateOfExceedance,s=20)
    if len(return_periods) > 0:
        annual_rate_exc = 1.0/np.array(return_periods)
        for rate in annual_rate_exc:
            if rate > min(rateOfExceedance):
                plt.plot([min(losses),max(losses)],[rate,rate],color='red') 
                plt.annotate('%.6f' % rate,xy=(max(losses),rate),fontsize = 12)

    plt.yscale('log')
    plt.xscale('log')
    plt.ylim([min(rateOfExceedance),1])
    plt.xlim([min(losses),max(losses)])
    plt.xlabel('Losses', fontsize = 16)
    plt.ylabel('Annual rate of exceedance', fontsize = 16)

    setReturnPeriods = 1/rateOfExceedance
    plt.figure(2, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
    plt.scatter(setReturnPeriods,losses,s=20)
    if len(return_periods) > 0:
        for period in return_periods:
            if period < max(setReturnPeriods):
                plt.plot([period,period],[min(losses),max(losses)],color='red') 
                plt.annotate(str(period),xy=(period,max(losses)),fontsize = 12)

    plt.xscale('log')
    plt.xlim([min(setReturnPeriods),max(setReturnPeriods)])
    plt.ylim([min(losses),max(losses)])
    plt.xlabel('Return period (years)', fontsize = 16)
    plt.ylabel('Losses', fontsize = 16)

def plot_netload_cache_size_sensitivity(show=True, save=True):
    """
    Plot sensitivity of network load vs cache size
    """
    # Parameters
    T = TOPOLOGIES
    C = C_RANGE
    A = ALPHA_C_PLOT
    LT = LINK_TYPES
    # Execution
    for t in T:
        for a in [str(al) for al in A]:
            for lt in LT:
                plt.figure()
                plt.title('Network load: LINK=%s T=%s A=%s' % (lt, t, a))
                plt.ylabel('Average link load (Mbps)')
                plt.xlabel('Cache to population ratio')
                plt.xscale('log')
                S = SummaryAnalyzer(path.join(SUMMARY_LOG_DIR, 'SUMMARY_NETWORK_LOAD.txt'))
                for strategy in NETLOAD_STRATEGIES:
                    cond = (S.param['T'] == t) &  (S.param['A'] == a) &  (S.param['S'] == strategy) & (S.data['LinkType'] == lt)
                    plt.plot(S.param['C'][cond], S.data['NetworkLoad'][cond], style_dict[strategy])
                plt.xlim(min(C), max(C))
                plt.legend(tuple(netload_legend_list), prop={'size': LEGEND_SIZE}, loc='lower left')
                if show: plt.show()
                if save: plt.savefig(path.join(GRAPHS_DIR ,'NETLOAD_C_SENS_LT=%[email protected]=%[email protected]=%s.pdf' % (lt, t, a)), bbox_inches='tight')

    def __save(self, n, plot, sfile):
        p.figure(figsize=sfile)

        p.xlabel(plot.xlabel)
        p.ylabel(plot.ylabel)
        p.xscale(plot.xscale)
        p.yscale(plot.yscale)
        p.grid()
        for curvetype, args, kwargs in plot.curves:
            if curvetype == "plot":
                p.plot(*args, **kwargs)
            elif curvetype == "imshow":
                p.imshow(*args, **kwargs)
            elif curvetype == "hist":
                p.hist(*args, **kwargs)
            elif curvetype == "bar":
                p.bar(*args, **kwargs)

        p.axes().set_aspect(plot.aspect)
        if plot.legend:
            p.legend(shadow=0, loc=plot.loc)

        if not os.path.isdir(plot.dir):
            os.mkdir(plot.dir)
        if plot.pgf:
            p.savefig(plot.dir + plot.name + ".pgf")
            print(plot.name + ".pgf")
        if plot.pdf:
            p.savefig(plot.dir + plot.name + ".pdf", bbox_inches="tight")
            print(plot.name + ".pdf")

        p.close()

def plot_citation_graph(citation_graph, filename, plot_title):
    # find the indegree_distribution
    indeg_dist = in_degree_distribution(citation_graph)    
    # sort freq by keys
    number_citations = sorted(indeg_dist.keys())
    indeg_freq = [indeg_dist[n] for n in number_citations]

    # normalize
    total = sum(indeg_freq)
    indeg_freq_norm = [freq / float(total) for freq in indeg_freq]
    
    # calculate log/log, except for the first one (0)
    #log_number_citations = [math.log10(x) for x in number_citations[1:]]
    #log_indeg_freq_norm = [math.log10(x) for x in indeg_freq_norm[1:]]
    
    plot(number_citations[1:], indeg_freq_norm[1:], 'o')
    
    xscale("log")
    yscale("log")
    
    xlabel("log10 #citations")
    ylabel("log10 Norm.Freq.")
    title(plot_title)
    grid(True)
    savefig(filename)
    show()

def setDiffsPlot(CF,d0,ySym = True):
    CFtext = [str(j)+',' for j in CF]
    bText = [CFtext[0],r'...,$a_i$+c,...',CFtext[-1]]
    CFtext = ''.join(CFtext)
    bText= ''.join(bText)
    CFtext = '[' + CFtext[:-1] + ']'
    bText = '[' + bText[:-1] + ']'
    print(CFtext)

    plt.ylabel(r'$d^{crit}_b - d^{crit}_a$',fontsize=20)
    plt.xlabel(r'Element changed',fontsize=20)
    xmin, xmax, ymin, ymax = plt.axis()
    if ySym:
        plt.yscale('symlog',linthreshy=1e-15)
        yLoc = [y*ymax for y in [.1, .01, .001]]
    else:
        yLoc = [y*(ymax-ymin)+ymin for y in [0.95, 0.85, 0.75]]
    plt.plot([0,xmax],[0,0],'k--',label='_')
    plt.text((xmax-xmin)*0.15,yLoc[0],r'$a = [a_i] =$'+CFtext,fontsize=15)
    plt.text((xmax-xmin)*0.15,yLoc[1],r'$b_i = $'+bText,fontsize=15)
    plt.text((xmax-xmin)*0.15,yLoc[2],r'$d_a^{crit} = $'+str(float(d0)),fontsize=15)
    plt.legend(loc='best')
    plt.xscale('symlog',linthreshx=1e-14)
    # plt.yscale('log')
    plt.show()

def main():
    counter = n36.main()
    plt.figure()
    plt.xscale('log')
    plt.yscale('log')
    plt.plot(sorted(list(counter.values()), reverse=True), range(1, len(list(counter)) + 1))
    plt.savefig('fig39.png')

def nlp39(words):
    # Zipfの法則：https://ja.wikipedia.org/wiki/%E3%82%B8%E3%83%83%E3%83%97%E3%81%AE%E6%B3%95%E5%89%87

    # 語彙頻度
    freq = {}
    for word in words:
        if word['pos'] == '動詞':
            if not word['base'] in freq:
                freq[word['base']] = 1
            else:
                freq[word['base']] += 1

    count = 1
    x = []
    y = []
    for word in sorted(freq, key=freq.get, reverse=True):
        x.append(count)
        y.append(freq[word])
        count += 1

    plt.xscale('log')
    plt.yscale('log')

    plt.plot(x, y, 'o')
    plt.show()

def PlotEDepSummary(gFiles,nFiles,figureName='EDepSummary.png',tParse=GetThickness,
  histKey='eDepHist'):
  """ PlotEDepSummary
  Plotss the energy deposition summary
  """
  # Extrating the average values
  gT = list()
  gDep = list()
  gDepError = list()
  nT = list()
  nDep = list()
  nDepError = list()
  for fname in gFiles:
    f = TFile(fname,'r')
    hist = f.Get(histKey)
    gT.append(GetThickness(fname))
    gDep.append(hist.GetMean())
    gDepError.append(hist.GetMeanError())
  for fname in nFiles:
    f = TFile(fname,'r')
    hist = f.Get(histKey)
    nT.append(GetThickness(fname))
    nDep.append(hist.GetMean())
    nDepError.append(hist.GetMeanError())
  # Plotting
  plt.errorbar(gT,gDep,yerr=gDepError,fmt='r+')
  plt.hold(True)
  plt.errorbar(nT,nDep,yerr=nDepError,fmt='go')
  plt.xlabel("Thickness (mm)")
  plt.ylabel("Average Energy Deposition (MeV)")
  plt.legend(["Co-60","Cf-252"])
  plt.xscale("log")
  plt.yscale("log")
  plt.grid(True)
  plt.savefig(figureName)