Python pyplot.figtext函数代码示例

def plot_onetimeseries_right(fig,n,ThisOne,xarray,yarray,p):
    if not p.has_key('ts_ax'):
        ts_ax = fig.add_axes([p['ts_XAxOrg'],p['YAxOrg'],p['ts_XAxLen'],p['ts_YAxLen']])
        ts_ax.hold(False)
        ts_ax.yaxis.tick_right()
        TextStr = ThisOne+'('+p['Units']+')'
        txtXLoc = p['ts_XAxOrg']+0.01
        txtYLoc = p['YAxOrg']+p['ts_YAxLen']-0.025
        plt.figtext(txtXLoc,txtYLoc,TextStr,color='b',horizontalalignment='left')
    else:
        ts_ax = p['ts_ax'].twinx()
    colour = 'r'
    if p.has_key('ts_ax'): del p['ts_ax']
    ts_ax.plot(xarray,yarray,'r-')
    ts_ax.xaxis.set_major_locator(p['loc'])
    ts_ax.xaxis.set_major_formatter(p['fmt'])
    ts_ax.set_xlim(p['XAxMin'],p['XAxMax'])
    ts_ax.set_ylim(p['RYAxMin'],p['RYAxMax'])
    if n==0:
        ts_ax.set_xlabel('Date',visible=True)
    else:
        ts_ax.set_xlabel('',visible=False)
    TextStr = str(p['nNotM'])+' '+str(p['nMskd'])
    txtXLoc = p['ts_XAxOrg']+p['ts_XAxLen']-0.01
    txtYLoc = p['YAxOrg']+p['ts_YAxLen']-0.025
    plt.figtext(txtXLoc,txtYLoc,TextStr,color='r',horizontalalignment='right')
    if n > 0: plt.setp(ts_ax.get_xticklabels(),visible=False)

def MOorderplot(popul,path,picname=None,title=None):
    x=[]; y1=[]; y2=[]; y3=[]; y4=[]; y5=[]; y6=[]; c=[]
    for i,dude in enumerate(popul):
        x.append(i)
        y1.append(dude.no)
        y2.append(dude.oldno)
        y3.append(dude.ranks[0])
        y4.append(dude.ranks[1])
        y5.append(dude.score)
        y6.append(dude.overall_rank)
        c.append(dude.score)
    f=plt.figure(figsize=(8,12)); a1=f.add_subplot(321); a2=f.add_subplot(322); a3=f.add_subplot(323); a4=f.add_subplot(324); a5=f.add_subplot(325); a6=f.add_subplot(326)
    a1.scatter(x,y1,c=c,cmap=cm.gist_stern)
    a2.scatter(x,y2,c=c,cmap=cm.gist_stern)
    a3.scatter(x,y3,c=c,cmap=cm.gist_stern)
    a4.scatter(x,y4,c=c,cmap=cm.gist_stern)
    a5.scatter(x,y5,c=c,cmap=cm.gist_stern)
    a6.scatter(x,y6,c=c,cmap=cm.gist_stern)
    a1.set_xlabel('place in population'); a1.set_ylabel('dude.no')
    a2.set_xlabel('place in population'); a2.set_ylabel('dude.oldno')
    a3.set_xlabel('place in population'); a3.set_ylabel('dude.ranks[0]')
    a4.set_xlabel('place in population'); a4.set_ylabel('dude.ranks[1]')
    a5.set_xlabel('place in population'); a5.set_ylabel('dude.score')
    a6.set_xlabel('place in population'); a6.set_ylabel('dude.overall_rank')
    if title is not None: plt.figtext(0.5, 0.98,title,va='top',ha='center', color='black', weight='bold', size='large')
    if picname is not None:
        plt.savefig(join(path,picname))
    else:
        plt.savefig(join(path,'orderplot_c'+str(popul.ncase)+'_sc'+str(popul.subcase).zfill(3)+'_g'+str(popul.gg)+'.png'))
    plt.close()

def plot_1d(xdata, ydata, color, x_axis, y_axis, system, analysis, average = False, t0 = 0, **kwargs):
	""" Creates a 1D scatter/line plot:

	Usage: plot_1d(xdata, ydata, color, x_axis, y_axis, system, analysis, average = [False|True], t0 = 0)
	
	Arguments:
	xdata, ydata: self-explanatory
	color: color to be used to plot data
	x_axis, y_axis: strings to be used for the axis label
	system: descriptor for the system that produced the data
	analysis: descriptor for the analysis that produced the data
	average: [False|True]; Default is False; if set to True, the function will calc the average, standard dev, and standard dev of mean of the y-data	# THERE IS A BUG IF average=True; must read in yunits for this function to work at the moment.
	t0: index to begin averaging from; Default is 0
	
	kwargs:
		xunits, yunits: string with correct math text describing the units for the x/y data
		x_lim, y_lim: list w/ two elements, setting the limits of the x/y ranges of plot
		plt_title: string to be added as the plot title
		draw_line: int value that determines the line style to be drawn; giving myself space to add more line styles if I decide I need them

	"""
	# INITIATING THE PLOT...
	plt.plot(xdata, ydata, '%s' %(color))

	# READING IN KWARG DICTIONARY INTO SPECIFIC VARIABLES
	for name, value in kwargs.items():
		if name == 'xunits':
			x_units = value
			x_axis = '%s (%s)' %(x_axis, value)
		elif name == 'yunits':
			y_units = value
			y_axis = '%s (%s)' %(y_axis, value)
		elif name == 'x_lim':
			plt.xlim(value)
		elif name == 'y_lim':
			plt.ylim(value)
		elif name == 'plt_title':
			plt.title(r'%s' %(value), size='14')
		elif name == 'draw_line':
			draw_line = value
			if draw_line == 1:
				plt.plot([0,max(ydata)],[0,max(ydata)],'r-',linewidth=2)
			else:
				print 'draw_line = %s has not been defined in plotting_functions script' %(line_value)
	
	plt.grid(b=True, which='major', axis='both', color='#808080', linestyle='--')
	plt.xlabel(r'%s' %(x_axis), size=12)
	plt.ylabel(r'%s' %(y_axis), size=12)

	# CALCULATING THE AVERAGE/SD/SDOM OF THE Y-DATA
	if average != False:
		avg = np.sum(ydata[t0:])/len(ydata[t0:])
		SD = stdev(ydata[t0:])
		SDOM = SD/sqrt(len(ydata[t0:]))

		plt.axhline(avg, xmin=0.0, xmax=1.0, c='r')
		plt.figtext(0.680, 0.780, '%s\n%6.4f $\\pm$ %6.4f %s \nSD = %4.3f %s' %(analysis, avg, SDOM, y_units, SD, y_units), bbox=dict(boxstyle='square', ec='r', fc='w'), fontsize=12)

	plt.savefig('%s.%s.plot1d.png' %(system,analysis),dpi=300)
	plt.close()

    def plot_multiline(data_list, legend_list, picture_title, picture_save_path, text=None):
        """ Draw data series info """

        # plot file and save picture
        fig = plt.figure(figsize=(15, 6))

        plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
        plt.gca().xaxis.set_major_locator(mdates.YearLocator())
        if text is not None:
            plt.figtext(0.01, 0.01, text, horizontalalignment='left')

        date_series = data_list[0].index

        color_list = ['r-', 'b-', 'y-', 'g-']

        for i, data_series in enumerate(data_list):
            # get data series info
            plt.plot(date_series, data_series, color_list[i], label=legend_list[i])

        min_date = date_series[0]
        max_date = date_series[-1]
        plt.gca().set_xlim(min_date, max_date)
        plt.legend(loc=0)
        fig.autofmt_xdate()
        fig.suptitle(picture_title)

        # print dir(fig)
        fig.savefig(picture_save_path)
        plt.close()

    def write(self):
        self.writePreHook()

        for cut in self.contourData.filteredCombinedData:
            # Add cut to the output filename
            (outputFilename, ext) = os.path.splitext(self.outputFilename)
            outputFilename = "{0}_{1}_{2:.0f}{3}".format(outputFilename, cut.key, cut.value, ext)

            plt.ioff()
            plt.figure(figsize=(8,6))
            #plt.yscale('log')
            plt.ylim([0,1.0])
            plt.figtext(0.05, 0.96, 'Cut: {0} = {1:.0f}'.format(cut.key, cut.value), color='grey', size='small')
            plt.figtext(0.05, 0.93, 'Plot written at {:%d/%m/%Y %H:%M}'.format(datetime.datetime.now()), color='grey', size='small')

            for f in self.contourData.contributingRegionsFiltered[cut]:
                data = self.contourData.filteredData[f][cut]
                label = filterLabel(f, self.gridConfig)

                if not cut.isSimple():
                    plt.xticks( np.arange(len(data)), ["%d_%d" % (x[self.gridConfig.x], x[self.gridConfig.y]) for x in data.values()])
                    xLabel = "Grid point"
                else:
                    var = self.gridConfig.x
                    if cut.key == var: 
                        var = self.gridConfig.y
                    xLabel = var
                    plt.xticks( np.arange(len(data)), ["%d" % (x[var]) for x in data.values()])
                print [x[self.contourData.combineOn] for x in data.values()]
                plt.plot( [x[self.contourData.combineOn] for x in data.values()], label=label )

                plt.plot( [0.05 for x in data.values()], color='r', linestyle='--', linewidth=2)

def plot_tiegcm(time, lat, lon, den, temp, ht, pres, image_name):
    """Plot density, temperature, and geopotential height
    for all latitudes and longitudes at relevant time"""
    y_values = [-90, -45, 0, 45, 90]
    x_values = [-180, -135, -90, -45, 0, 45, 90, 135, 180]
    fig = plt.figure()
#    fig.subplots_adjust(left = 0.25, right = 0.7, bottom = 0.07, top = 0.9, wspace = 0.2, hspace = 0.08)
    sub_den = fig.add_subplot(3, 1, 1)
    plot_settings(sub_den, den*1e12, lon, lat, 
                  y_values, x_values = [], 
                  ylabel = "", xlabel = "",
                  title = 'Density',
                  ctitle = r"x 10$^{-12}$ kgm$^{-3}$ ",
                  minmax = [2., 4.])
    pres = format(pres, '.2e')
    plt.title('{} at {} Pa'.format(time, pres), fontsize = 11)
    sub_temp = fig.add_subplot(3, 1, 2)
    plot_settings(sub_temp, temp, lon, lat,
                  y_values, x_values = [],
                  ylabel = 'Latitude [$^\circ$]', xlabel = "",
                  title = 'Temperature',
                  ctitle = "Kelvin",
                  minmax = [750., 1250.])
    sub_ht = fig.add_subplot(3, 1, 3)
    plot_settings(sub_ht, ht/100000., lon, lat,
                  y_values, x_values, 
                  ylabel = " ", xlabel = 'Longitude [$^\circ$]', 
                  title = 'Geopotential Height', 
                  ctitle = "km",
                  minmax = [350., 450.])
    plt.figtext(.6, .032, r'$\copyright$ Crown Copyright. Source: Met Office', size = 8)
    plt.tight_layout()
#    insert_logo()
    save_to_web(fig, time, image_name)

    def plotEvolution(self, var, name, name_long="new plot", figlabel="Resolution", ylabel="", loc="", reference=False):
        """
        Plots evolution in time of the given variable.
        """
        plt.clf()
        var = np.array(var)
        if var.ndim == 1:  # in case a single set of data is provided
            var = [var]
        ls = ["-.", "--", "-"]
        kwargs = {}
        if "bw" in self.file_name:
            kwargs["color"] = "k"
        for i in range(len(self.t)):
            plt.plot(self.t[i], var[i], label="{0} {1}".format(figlabel, i + 1), ls=ls[i], **kwargs)
        plt.title(self.title.format(name_long))
        plt.xlabel(self.xlabel)
        plt.ylabel(ylabel)

        if self.info:
            plt.figtext(self.text_pos[0], self.text_pos[1], self.info)
        plt.xlim(np.round(np.min(self.t[0])), np.round(np.max(self.t[0])))
        if reference:
            self.plotReference()
        plt.legend(loc=loc or self.loc)
        for e in self.ext:
            plt.savefig(os.path.join(self.out_dir, self.file_name.format(name=name, ext=e)))

def i2pcontrol_stats(conn, output=''):
	things=[
		{'stat':'activepeers','xlab':'time','ylab':'total',},
		{'stat':'tunnelsparticipating','xlab':'time','ylab':'total',},
		{'stat':'decryptFail','xlab':'time','ylab':'total',},
		{'stat':'failedLookupRate','xlab':'time','ylab':'total',},
		{'stat':'streamtrend','xlab':'time','ylab':'total',},
		{'stat':'windowSizeAtCongestion','xlab':'time','ylab':'total',},
		#{'stat':'','xlab':'','ylab':'',}, # Template to add more.
		]

	tokens = query_db(conn, 'select owner,token from submitters;')
	for thing in things:
		combined=[]
		dfs=[]
		for token in tokens:
			q = 'select datetime(cast(((submitted)/({0})) as int)*{0}, "unixepoch") as sh, {1} from speeds where submitter="{2}" group by sh order by sh desc;'.format(interval, thing['stat'], token[1])
			df = pd.read_sql_query(q, conn)
			# unix -> human
			df['sh'] = pd.to_datetime(df['sh'], unit='s')
			dfs.append(df)

		# Reverse so it's put in left to right
		combined = reduce(lambda left,right: pd.merge(left,right,on='sh',how='outer'), dfs)
		combined.columns=['time'] + [i[0] for i in tokens]
		combined = combined.set_index('time')

		combined.head(num_intervals).plot(marker='o')
		plt.figtext(.1,.03,'{}\n{} UTC'.format(site,generation_time))
		plt.title(thing['stat'])
		plt.xlabel(thing['xlab'])
		plt.ylabel(thing['ylab'])
		plt.savefig('{}/{}.png'.format(output, thing['stat']))
		plt.close()

	def DBLR_f(self):

		global a

		#if (self.graph_sw.get()==True):
		b=a
		a=a+1
		#else:
		#	b=0

		aux=self.base_path.get()
#		path=''.join([aux,str(self.meas.get()),'.txt'])
#		g=pd.read_csv(path)
		f=read_panel_hdf5(aux, self.point.get(), self.meas.get())
		f=4096-f

		recons,energia = DB.BLR( signal_daq=f.flatten().astype(float),
						  coef=self.coef.get(),
						  n_sigma=self.n_sigma.get(),
						  NOISE_ADC=self.noise.get(),
						  thr1=self.thr1.get(),
						  thr2=self.thr2.get(),
						  thr3=self.thr3.get(),
						  plot=False)
		plt.figure(a)
		plt.plot(recons)
		plt.figtext(0.2,0.75, ('ENERGY = %0.2f ' % (energia)))
		plt.show()

def execute(model, data, savepath, *args, **kwargs):

    descriptors = ['Cu (At%)', 'Ni (At%)', 'Mn (At%)', 'P (At%)', 'Si (At%)', 'C (At%)', 'Temp (C)', 'log(fluence)',
                   'log(flux)']
    xlist = np.asarray(data.get_data(descriptors))

    model = model
    model.fit(data.get_x_data(), np.array(data.get_y_data()).ravel())
    error = model.predict(data.get_x_data()) - np.array(data.get_y_data()).ravel()

    for x in range(len(descriptors)):
        plt.scatter(xlist[:, x], error, color='black', s=10)
        xlim = plt.gca().get_xlim()
        plt.plot(xlim, (20, 20), ls="--", c=".3")
        plt.plot(xlim, (0, 0), ls="--", c=".3")
        plt.plot(xlim, (-20, -20), ls="--", c=".3")
        m, b = np.polyfit(np.reshape(xlist[:, x], len(xlist[:, x])), np.reshape(error, len(error)),1)  # line of best fit

        matplotlib.rcParams.update({'font.size': 15})
        plt.plot(xlist[:, x], m * xlist[:, x] + b, color='red')
        plt.figtext(.15, .83, 'y = ' + "{0:.6f}".format(m) + 'x + ' + "{0:.5f}".format(b), fontsize=14)
        plt.title('Error vs. {}'.format(descriptors[x]))
        plt.xlabel(descriptors[x])
        plt.ylabel('Predicted - Actual (Mpa)')
        plt.savefig(savepath.format(plt.gca().get_title()), dpi=200, bbox_inches='tight')
        plt.close()

def pie_graph(conn, query, output, title='', lower=0, log=False):
	labels = []
	sizes = []

	res = query_db(conn, query)
	# Sort so the graph doesn't look like complete shit.
	res = sorted(res, key=lambda tup: tup[1])
	for row in res:
		if row[1] > lower:
			labels.append(row[0])
			if log:
				sizes.append(math.log(row[1]))
			else:
				sizes.append(row[1])
	# Normalize.
	norm = [float(i)/sum(sizes) for i in sizes]

	plt.pie(norm,
			labels=labels,
			shadow=True,
			startangle=90,
	)
	plt.figtext(.1,.03,'{}\n{} UTC'.format(site,generation_time))
	plt.axis('equal')
	plt.legend()
	plt.title(title)
	plt.savefig(output)
	plt.close()

def show_spider(p):
    L = Logging('log')
    data = L.get_stacked_bar_data('Template', p, 'spider')

    N = 12
    theta = radar_factory(N, frame='polygon')

    spoke_labels = data.pop('column names')

    fig = plt.figure(figsize=(7, 7))
    fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)

    colors = ['r', 'g', 'b', 'm', 'y']
    # Plot the four cases from the example data on separate axes
    for n, title in enumerate(data.keys()):
        ax = fig.add_subplot(1, 1, n + 1, projection='radar')
        # plt.rgrids([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
        ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
                     horizontalalignment='center', verticalalignment='center')
        for d, color in zip(data[title], colors):
            ax.plot(theta, d, color=color)
            ax.fill(theta, d, facecolor=color, alpha=0.25)
        ax.set_varlabels(spoke_labels)

    # add legend relative to top-left plot
    plt.subplot(1, 1, 1)
    labels = ('Losers', 'Winners')
    legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
    plt.setp(legend.get_texts(), fontsize='small')

    plt.figtext(0.5, 0.965, 'Winner vs Losers',
                ha='center', color='black', weight='bold', size='large')
    plt.show()

def plot_results(datas, factor=None, algo='FFT'):
    xlabel = r'Array Size (2^x)'
    ylabel = 'Speed (GFLOPs)'
    backends = ['numpy', 'numpy+mkl']

    plt.clf()
    fig1, ax1 = plt.subplots()
    plt.figtext(0.90, 0.94, "Note: higher is better", va='top', ha='right')
    w, h = fig1.get_size_inches()
    fig1.set_size_inches(w*1.5, h)
    ax1.get_xaxis().set_major_formatter(ticker.ScalarFormatter())
    ax1.get_xaxis().set_minor_locator(ticker.NullLocator())
    ax1.set_xticks(datas[0][:,0])
    ax1.grid(color="lightgrey", linestyle="--", linewidth=1, alpha=0.5)
    if factor:
        ax1.set_xticklabels([str(int(x)) for x in datas[0][:,0]/factor])
    plt.xlabel(xlabel, fontsize=16)
    plt.ylabel(ylabel, fontsize=16)
    plt.xlim(datas[0][0,0]*.9, datas[0][-1,0]*1.025)
    plt.suptitle("%s Performance" % ("FFT"), fontsize=28)

    for backend, data in zip(backends, datas):
        N = data[:, 0]
        plt.plot(N, data[:, 1], 'o-', linewidth=2, markersize=5, label=backend)
        plt.legend(loc='upper left', fontsize=18)

    plt.savefig(algo + '.png')

def plotta(im,xx,yy,ext):
	#make plot
	f = ap.FITSFigure(im,figure=fig,subplot=[xx,yy-dy,dx,dy])


	f.show_colorscale(cmap = 'hot', vmin = -1.245E-02, vmax  = 2.405E-02 )
	f.recenter(ra, dec, re_cen)

	#if ext == 'E':
	f.show_ellipses(ra, dec, a/60.0, b/60.0, angle=pa-90, edgecolor='grey', lw=2)

	#if ext == 'P':
		#f.show_markers(ra, dec, c='grey',marker='x')

	#f.add_scalebar(1.0/60.0)
	f.scalebar.set_label('1 arcmin')
	#f.scalebar.set_color('white')

	#f.show_grid()
	#f.set_tick_labels_format(xformat='hh:mm:ss',yformat='dd:mm:ss')
	#f.show_markers(ra, dec, c='red',marker='x')
	#f.add_beam(major=beam, minor=beam,angle=0.0)
	f.show_beam(major=beam, minor=beam,angle=0.0,fc='white')
	f.axis_labels.hide()
	f.tick_labels.hide()

	# add text 
	plt.figtext(xx+0.012 , yy-dy+0.14, name, size=20, weight="bold", color='white')

def beaut(fig, axes):
    for row_i, axes_row in enumerate(axes):
        for col_i, ax in enumerate(axes_row):
            ax.set_xticks([])
            ax.set_yticks([])

            if col_i == 0:
                ax.set_ylabel(str(row_i))

            if row_i == 9:
                ax.set_xlabel(str(col_i))

    plt.figtext(
        0.5,
        0,
        "public goods effect threshold",
        figure=fig,
        fontsize=17,
        horizontalalignment='center',
        verticalalignment='top',
    )


    plt.figtext(
        0,
        0.5,
        "quorum sensing threshold",
        figure=fig,
        fontsize=17,
        horizontalalignment='right',
        verticalalignment='center',
        rotation=90,
    )

    fig.tight_layout()