Python pylab.axes() Examples

def image_set_clim(zmin=None, zmax=None, axes="gca"):
    """
    This will set the clim (range) of the colorbar.

    Setting zmin or zmax to None will not change them.
    Setting zmin or zmax to "auto" will auto-scale them to include all the data.
    """
    if axes=="gca": axes=_pylab.gca()

    image = axes.images[0]

    if zmin=='auto': zmin = _n.min(image.get_array())
    if zmax=='auto': zmax = _n.max(image.get_array())

    if zmin==None: zmin = image.get_clim()[0]
    if zmax==None: zmax = image.get_clim()[1]

    image.set_clim(zmin, zmax)

    _pylab.draw() 

def set_all_line_attributes(attribute="lw", value=2, axes="current", refresh=True):
    """

    This function sets all the specified line attributes.

    """

    if axes=="current": axes = _pylab.gca()

    # get the lines from the plot
    lines = axes.get_lines()

    # loop over the lines and trim the data
    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):
            _pylab.setp(line, attribute, value)

    # update the plot
    if refresh: _pylab.draw() 

def smooth_all_traces(smoothing=1, trim=True, axes="gca"):
    """

    This function does nearest-neighbor smoothing of the data

    """
    if axes=="gca": axes=_pylab.gca()

    # get the lines from the plot
    lines = axes.get_lines()

    # loop over the lines and trim the data
    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):
            smooth_line(line, smoothing, trim, draw=False)
    _pylab.draw() 

def line_math(fx=None, fy=None, axes='gca'):
    """
    applies function fx to all xdata and fy to all ydata.
    """

    if axes=='gca': axes = _pylab.gca()

    lines = axes.get_lines()

    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):
            xdata, ydata = line.get_data()
            if not fx==None: xdata = fx(xdata)
            if not fy==None: ydata = fy(ydata)
            line.set_data(xdata,ydata)

    _pylab.draw() 

def apply(self, axes="gca"):
        """
        Applies the style cycle to the lines in the axes specified
        """

        if axes == "gca": axes = _pylab.gca()
        self.reset()
        lines = axes.get_lines()

        for l in lines:
            l.set_color(self.get_line_color(1))
            l.set_mfc(self.get_face_color(1))
            l.set_marker(self.get_marker(1))
            l.set_mec(self.get_edge_color(1))
            l.set_linestyle(self.get_linestyle(1))

        _pylab.draw() 

def __init__(self, norder = 2):
		"""Initializes the class when returning an instance. Pass it the polynomial order. It will 
set up two figure windows, one for the graph the other for the coefficent interface. It will then initialize 
the coefficients to zero and plot the (not so interesting) polynomial."""
		
		self.order = norder
		
		self.c = M.zeros(self.order,'f')
		self.ax = [None]*(self.order-1)#M.zeros(self.order-1,'i') #Coefficent axes
		
		self.ffig = M.figure() #The first figure window has the plot
		self.replotf()
		
		self.cfig = M.figure() #The second figure window has the 
		row = M.ceil(M.sqrt(self.order-1))
		for n in xrange(self.order-1):
			self.ax[n] = M.subplot(row, row, n+1)
			M.setp(self.ax[n],'label', n)
			M.plot([0],[0],'.')
			M.axis([-1, 1, -1, 1]);
			
		self.replotc()
		M.connect('button_press_event', self.click_event) 

def reverse_draw_order(axes="current"):
    """

    This function takes the graph and reverses the draw order.

    """

    if axes=="current": axes = _pylab.gca()

    # get the lines from the plot
    lines = axes.get_lines()

    # reverse the order
    lines.reverse()

    for n in range(0, len(lines)):
        if isinstance(lines[n], _mpl.lines.Line2D):
            axes.lines[n]=lines[n]

    _pylab.draw() 

def integrate_shown_data(scale=1, fyname=1, autozero=0, **kwargs):
    """
    Numerically integrates the data visible on the current/specified axes using
    scale*fun.integrate_data(x,y). Modifies the visible data using
    manipulate_shown_data(**kwargs)

    autozero is the number of data points used to estimate the background
    for subtraction. If autozero = 0, no background subtraction is performed.
    """

    def I(x,y):
        xout, iout = _fun.integrate_data(x, y, autozero=autozero)
        print("Total =", scale*iout[-1])
        return xout, scale*iout

    if fyname==1: fyname = "$"+str(scale)+"\\times \\int dx$"

    manipulate_shown_data(I, fxname=None, fyname=fyname, **kwargs) 

def image_shift(xshift=0, yshift=0, axes="gca"):
    """
    This will shift an image to a new location on x and y.
    """

    if axes=="gca": axes = _pylab.gca()

    e = axes.images[0].get_extent()

    e[0] = e[0] + xshift
    e[1] = e[1] + xshift
    e[2] = e[2] + yshift
    e[3] = e[3] + yshift

    axes.images[0].set_extent(e)

    _pylab.draw() 

def image_click_xshift(axes = "gca"):
    """
    Takes a starting and ending point, then shifts the image y by this amount
    """
    if axes == "gca": axes = _pylab.gca()

    try:
        p1 = _pylab.ginput()
        p2 = _pylab.ginput()

        xshift = p2[0][0]-p1[0][0]

        e = axes.images[0].get_extent()

        e[0] = e[0] + xshift
        e[1] = e[1] + xshift

        axes.images[0].set_extent(e)

        _pylab.draw()
    except:
        print("whoops") 

def click_event(self, event):
		"""Whenever a click occurs on the coefficent axes we modify the coefficents and update the 
plot"""
		
		if event.xdata is None:#we clicked outside the axis
			return

		idx = M.getp(event.inaxes,'label')
		
		print idx, event.xdata, event.ydata
				
		self.c[idx] = event.xdata
		self.c[idx+1] = event.ydata

		self.replotf()
		self.replotc() 

def __init__(self, name = "Plot", LaTeX = False, **kwargs):
        global figureNo
        self.fapps = []
        self.name = name
        self.latex = LaTeX
        self.figureNo = figureNo
        figureNo += 1
        
        # LaTeX specific formatting settings
        # - figure dimensions (pt)
        col_width = 600.0 # column width (in pt) - get this from LaTeX using \showthe\columnwidth
        self.fig_width_pt = col_width/2
        self.aspect_ratio =  (sqrt(5)-1.0)/2.0 # aesthetic ratio
        self.fig_height_pt = None # None = determined by aspect ratio
        # - fonts
        self.font_family = 'serif'
        self.tick_font_size = 7
        self.label_font_size = 7
        # - axes and borders
        self.tick_extend_into_right_border_chars = 2 # rightmost x-axis tick label may extend into border - this is the number of characters that extend into the border
        self.left_tick_label_chars = 4 # the number of characters used in the y-axis labels (must make sure there's room for these labels in the border)
        self.border = 0.05 # fraction of space that is to be taken up by borders around the figure
        self.latex_preamble = None
        self.drawn = False
        
        self.__dict__.update(kwargs) 

def set_line_attribute(line=-1, attribute="lw", value=2, axes="current", refresh=True):
    """

    This function sets all the specified line attributes.

    """

    if axes=="current": axes = _pylab.gca()

    # get the lines from the plot
    line = axes.get_lines()[-1]
    _pylab.setp(line, attribute, value)

    # update the plot
    if refresh: _pylab.draw() 

def __init__(self, name = "Plot", LaTeX = False, **kwargs):
        global figureNo
        self.fapps = []
        self.name = name
        self.latex = LaTeX
        self.figureNo = figureNo
        figureNo += 1
        
        # LaTeX specific formatting settings
        # - figure dimensions (pt)
        col_width = 600.0 # column width (in pt) - get this from LaTeX using \showthe\columnwidth
        self.fig_width_pt = col_width/2
        self.aspect_ratio =  (sqrt(5)-1.0)/2.0 # aesthetic ratio
        self.fig_height_pt = None # None = determined by aspect ratio
        # - fonts
        self.font_family = 'serif'
        self.tick_font_size = 7
        self.label_font_size = 7
        # - axes and borders
        self.tick_extend_into_right_border_chars = 2 # rightmost x-axis tick label may extend into border - this is the number of characters that extend into the border
        self.left_tick_label_chars = 4 # the number of characters used in the y-axis labels (must make sure there's room for these labels in the border)
        self.border = 0.05 # fraction of space that is to be taken up by borders around the figure
        self.latex_preamble = None
        self.drawn = False
        
        self.__dict__.update(kwargs) 

def replotc(self):
		"""This replots the coefficients."""
	
		M.figure(self.cfig.number)
		M.ioff()
		for n in xrange(self.order-1):
			M.axes(self.ax[n])
			#M.cla()
			M.plot([self.c[n]], [self.c[n+1]],'ko')
			M.xlabel("$c_%d$" %(n))
			M.ylabel("$c_%d$" %(n+1))
			M.axis([-1, 1, -1, 1]);
			del self.ax[n].lines[0]

		M.draw() 

def spikes_diagram(ts, gids, name, path):
    """
    Function for making spike diagrams
    :param ts:   (list) times
    :param gids: (list) global IDs of neurons
    :param name: (str) name of brain part
    :param path: (str) path to save results
    :return: None
    """
    pylab.figure()
    color_marker = "."
    color_bar = "blue"
    color_edge = "black"
    ylabel = "Neuron ID"
    hist_binwidth = 5.0
    location = pylab.axes([0.1, 0.3, 0.85, 0.6])
    pylab.plot(ts, gids, color_marker)
    pylab.ylabel(ylabel)
    xlim = pylab.xlim()
    pylab.xticks([])
    pylab.axes([0.1, 0.1, 0.85, 0.17])
    t_bins = numpy.arange(numpy.amin(ts), numpy.amax(ts), hist_binwidth)
    n, bins = pylab.histogram(ts, bins=t_bins)
    num_neurons = len(numpy.unique(gids))
    heights = (1000 * n / (hist_binwidth * num_neurons))
    # FixMe t_bins[:-1] should work without cutting the end value
    pylab.bar(t_bins[:-1], heights, width=hist_binwidth, color=color_bar, edgecolor=color_edge)
    pylab.yticks([int(a) for a in numpy.linspace(0.0, int(max(heights) * 1.1) + 5, 4)])
    pylab.ylabel("Rate (Hz)")
    pylab.xlabel("Time (ms)")
    pylab.grid(True)
    pylab.axes(location)
    pylab.title(name)
    pylab.xlim(xlim)
    pylab.draw()
    pylab.savefig("{0}{1}.png".format(path, name), dpi=dpi_n, format='png')
    pylab.close() 

def plot_weights(weights_list, title="Neurons weights progress", y_lim = None):

    # Plot
    # Make a list of colors cycling through the rgbcmyk series.
    colors = [colorConverter.to_rgba(c) for c in ('k', 'r', 'g', 'b', 'c', 'y', 'm')]

    axes = pl.axes()
    ax4 = axes # unpack the axes

    ncurves = 1
    offs = (0.0, 0.0)

    segs = []
    for i in range(ncurves):
        curve = weights_list
        segs.append(curve)

    col = collections.LineCollection(segs, offsets=offs)
    ax4.add_collection(col, autolim=True)
    col.set_color(colors)
    ax4.autoscale_view()
    ax4.set_title(title)
    ax4.set_xlabel('Time ms')
    ax4.set_ylabel('Weight pA')
    y_lim = 105.
    if y_lim :
        ax4.set_ylim(-5, y_lim)
    pl.savefig(f_name_gen('dopa-weights', is_image=True), format='png')
    # pl.show()

# =======
# DEVICES
# ======= 

def plot_weights(weights_list, title="Neurons weights progress"):
    # Make a list of colors cycling through the rgbcmyk series.
    colors = [colorConverter.to_rgba(c) for c in ('k', 'r', 'g', 'b', 'c', 'y', 'm')]
    axes = pl.axes()
    ax4 = axes  # unpack the axes
    ncurves = 1
    offs = (0.0, 0.0)
    segs = []
    for i in range(ncurves):
        curve = weights_list
        segs.append(curve)

    col = collections.LineCollection(segs, offsets=offs)
    ax4.add_collection(col, autolim=True)
    col.set_color(colors)
    ax4.autoscale_view()
    ax4.set_title(title)
    ax4.set_xlabel('Time ms')
    ax4.set_ylabel('Weight pA')
    y_lim = 105.
    if y_lim:
        ax4.set_ylim(-5, y_lim)
    pl.savefig(f_name_gen('dopa-weights', is_image=True), format='png')
    # pl.show()

# =======
# DEVICES
# ======= 

def legend(location='best', fontsize=16, axes="gca"):
    if axes=="gca": axes = _pylab.gca()

    axes.legend(loc=location, prop=_mpl.font_manager.FontProperties(size=fontsize))
    _pylab.draw()




#
# Style cycle, available for use in plotting
# 

def modify_legend(axes="gca"):
    # get the axes
    if axes=="gca": axes = _pylab.gca()

    # get the lines
    lines = axes.get_lines()

    # loop over the lines
    for line in lines:
        if isinstance(line, _mpl.lines.Line2D):

            # highlight the line
            fatten_line(line)

            # get the label (from the legend)
            label = line.get_label()

            print(label)

            new_label = input("New Label: ")
            if new_label == "q" or new_label == "quit":
                unfatten_line(line)
                return

            if not new_label == "\n": line.set_label(new_label)

            unfatten_line(line)
            format_figure() 

def save_plot(axes="gca", path=None):
    """
    Saves the figure in my own ascii format
    """

    global line_attributes

    # choose a path to save to
    if path==None: path = _s.dialogs.Save("*.plot", default_directory="save_plot_default_directory")

    if path=="":
        print("aborted.")
        return

    if not path.split(".")[-1] == "plot": path = path+".plot"

    f = file(path, "w")

    # if no argument was given, get the current axes
    if axes=="gca": axes=_pylab.gca()

    # now loop over the available lines
    f.write("title="  +axes.title.get_text().replace('\n', '\\n')+'\n')
    f.write("xlabel="+axes.xaxis.label.get_text().replace('\n','\\n')+'\n')
    f.write("ylabel="+axes.yaxis.label.get_text().replace('\n','\\n')+'\n')

    for l in axes.lines:
        # write the data header
        f.write("trace=new\n")
        f.write("legend="+l.get_label().replace('\n', '\\n')+"\n")

        for a in line_attributes: f.write(a+"="+str(_pylab.getp(l, a)).replace('\n','')+"\n")

        # get the data
        x = l.get_xdata()
        y = l.get_ydata()

        # loop over the data
        for n in range(0, len(x)): f.write(str(float(x[n])) + " " + str(float(y[n])) + "\n")

    f.close() 

def melgram_v1(audio_file_path, to_file):
    sig, fs = librosa.load(audio_file_path)

    pylab.axis('off')  # no axis
    pylab.axes([0., 0., 1., 1.], frameon=False, xticks=[], yticks=[])  # Remove the white edge
    S = librosa.feature.melspectrogram(y=sig, sr=fs)
    librosa.display.specshow(librosa.power_to_db(S, ref=np.max))
    pylab.savefig(to_file, bbox_inches=None, pad_inches=0)
    pylab.close() 

def coarsen_all_traces(level=2, exponential=False, axes="all", figure=None):
    """
    This function does nearest-neighbor coarsening of the data. See 
    spinmob.fun.coarsen_data for more information.
    
    Parameters
    ----------
    level=2
        How strongly to coarsen.
    exponential=False
        If True, use the exponential method (great for log-x plots).
    axes="all"
        Which axes to coarsen.
    figure=None
        Which figure to use.
    
    """
    if axes=="gca": axes=_pylab.gca()
    if axes=="all":
        if not figure: f = _pylab.gcf()
        axes = f.axes

    if not _fun.is_iterable(axes): axes = [axes]

    for a in axes:
        # get the lines from the plot
        lines = a.get_lines()

        # loop over the lines and trim the data
        for line in lines:
            if isinstance(line, _mpl.lines.Line2D):
                coarsen_line(line, level, exponential, draw=False)
    _pylab.draw() 

def test_varying_inclination(self):
        #""" Test that the waveform is consistent for changes in inclination
        #"""
        sigmas = []
        incs = numpy.arange(0, 21, 1.0) * lal.PI / 10.0

        for inc in incs:
            # WARNING: This does not properly handle the case of SpinTaylor*
            # where the spin orientation is not relative to the inclination
            hp, hc = get_waveform(self.p, inclination=inc)
            s = sigma(hp, low_frequency_cutoff=self.p.f_lower)
            sigmas.append(s)

        f = pylab.figure()
        pylab.axes([.1, .2, 0.8, 0.70])
        pylab.plot(incs, sigmas)
        pylab.title("Vary %s inclination, $\\tilde{h}$+" % self.p.approximant)
        pylab.xlabel("Inclination (radians)")
        pylab.ylabel("sigma (flat PSD)")

        info = self.version_txt
        pylab.figtext(0.05, 0.05, info)

        if self.save_plots:
            pname = self.plot_dir + "/%s-vary-inclination.png" % self.p.approximant
            pylab.savefig(pname)

        if self.show_plots:
            pylab.show()
        else:
            pylab.close(f)

        self.assertAlmostEqual(sigmas[-1], sigmas[0], places=7)
        self.assertAlmostEqual(max(sigmas), sigmas[0], places=7)
        self.assertTrue(sigmas[0] > sigmas[5]) 

def melgram_v1(audio_file_path, to_file):
    sig, fs = librosa.load(audio_file_path)

    pylab.axis('off')  # no axis
    pylab.axes([0., 0., 1., 1.], frameon=False, xticks=[], yticks=[])  # Remove the white edge
    S = librosa.feature.melspectrogram(y=sig, sr=fs)
    librosa.display.specshow(librosa.power_to_db(S, ref=np.max))
    pylab.savefig(to_file, bbox_inches=None, pad_inches=0)
    pylab.close() 

def image_scale(xscale=1.0, yscale=1.0, axes="gca"):
    """
    Scales the image extent.
    """
    if axes == "gca": axes = _pylab.gca()

    e = axes.images[0].get_extent()
    x1 = e[0]*xscale
    x2 = e[1]*xscale
    y1 = e[2]*yscale
    y2 = e[3]*yscale

    image_set_extent([x1,x2],[y1,y2], axes) 

def image_file(path=None, zscript='self[1:]', xscript='[0,1]', yscript='d[0]', g=None, **kwargs):
    """
    Loads an data file and plots it with color. Data file must have columns of the
    same length!

    Parameters
    ----------
    path=None
        Path to data file.
    zscript='self[1:]' 
        Determines how to get data from the columns
    xscript='[0,1]', yscript='d[0]' 
        Determine the x and y arrays used for setting the axes bounds
    g=None   
        Optional dictionary of globals for the scripts

    See spinmob.plot.image.data() for additional optional keyword arguments.
    See spinmob.data.databox.execute_script() for more information about scripts.
    """
    if 'delimiter' in kwargs: delimiter = kwargs.pop('delimiter')
    else:                           delimiter = None

    d = _data.load(paths=path, delimiter = delimiter)
    if d is None or len(d) == 0: return

    # allows the user to overwrite the defaults
    default_kwargs = dict(xlabel = str(xscript),
                          ylabel = str(yscript),
                          title  = d.path,
                          clabel = str(zscript))
    default_kwargs.update(kwargs)


    # get the data
    X = d(xscript, g)
    Y = d(yscript, g)
    Z = _n.array(d(zscript, g))
#    Z = Z.transpose()

    # plot!
    image_data(Z, X, Y, **default_kwargs) 

def add_text(text, x=0.01, y=0.01, axes="gca", draw=True, **kwargs):
    """
    Adds text to the axes at the specified position.

    **kwargs go to the axes.text() function.
    """
    if axes=="gca": axes = _pylab.gca()
    axes.text(x, y, text, transform=axes.transAxes, **kwargs)
    if draw: _pylab.draw() 

def differentiate_shown_data(neighbors=1, fyname=1, **kwargs):
    """
    Differentiates the data visible on the specified axes using
    fun.derivative_fit() (if neighbors > 0), and derivative() otherwise.
    Modifies the visible data using manipulate_shown_data(**kwargs)
    """

    if neighbors:
        def D(x,y): return _fun.derivative_fit(x,y,neighbors)
    else:
        def D(x,y): return _fun.derivative(x,y)

    if fyname==1: fyname = '$\\partial_{x(\\pm'+str(neighbors)+')}$'

    manipulate_shown_data(D, fxname=None, fyname=fyname, **kwargs) 

def image_format_figure(figure=None, draw=True):
    """
    This formats the figure in a compact way with (hopefully) enough useful
    information for printing large data sets. Used mostly for line and scatter
    plots with long, information-filled titles.

    Chances are somewhat slim this will be ideal for you but it very well might
    and is at least a good starting point.

    figure=None     specify a figure object. None will use gcf()

    """
    _pylab.ioff()

    if figure == None: figure = _pylab.gcf()

    set_figure_window_geometry(figure, (0,0), (550,470))

    axes = figure.axes[0]

    # set up the title label
    axes.title.set_horizontalalignment('right')
    axes.title.set_size(8)
    axes.title.set_position([1.27,1.02])
    axes.title.set_visible(1)

    if draw:
        _pylab.ion()
        _pylab.draw()