Python pylab.axes方法代码示例

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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") 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
def generateImages(picklefile, pickledir, filehash, imagedir, pietype):

	leaf_file = open(os.path.join(pickledir, picklefile), 'rb')
	(piedata, pielabels) = cPickle.load(leaf_file)
	leaf_file.close()

	pylab.figure(1, figsize=(6.5,6.5))
	ax = pylab.axes([0.2, 0.15, 0.6, 0.6])

	pylab.pie(piedata, labels=pielabels)

	pylab.savefig(os.path.join(imagedir, '%s-%s.png' % (filehash, pietype)))
	pylab.gcf().clear()
	os.unlink(os.path.join(pickledir, picklefile)) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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) 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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() 

# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axes [as 别名]
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)