def update(self, image, xlabel='', ylabel='', output=None):
# try to update the data within the existing plots (faster), otherwise
zoomed = image[self.ylim[0]:self.ylim[1], self.xlim[0]:self.xlim[1]]
# readjust things if a log scale is set
if self.log:
zoomedtoplot = logify(zoomed)
imagetoplot = logify(image)
else:
zoomedtoplot = zoomed
imagetoplot = image
# calculate summed histograms
xhist = np.sum(zoomed, 0)
yhist = np.sum(zoomed, 1)
self.current['image'].set_data(zoomedtoplot)
self.current['navigator'].set_data(imagetoplot)
self.current['xhist'].set_ydata(xhist)
self.current['yhist'].set_xdata(yhist)
self.speak('replaced existing image with new one')
self.ax['image'].set_xlabel(xlabel, fontsize=8)
self.ax['image'].set_ylabel(ylabel, fontsize=8)
#plt.draw()
if output is None:
pass
else:
chunks = output.split('/')
chunks[-1] = self.label + '_'+chunks[-1]
filename = '/'.join(chunks)
self.figure.savefig(filename)
self.speak('saved plot to {0}'.format(filename))
def plotIsoStaImpedance(ax, loc, array, flag, par='abs',
pSym='s', pColor=None, addLabel='', zorder=1):
appResFact = 1/(8*np.pi**2*10**(-7))
treshold = 1.0 # 1 meter
indUniSta = np.sqrt(np.sum((array[['x','y']].view((float,2))-loc)**2,axis=1)) < treshold
freq = array['freq'][indUniSta]
if par == 'abs':
zPlot = np.abs(array[flag][indUniSta])
elif par == 'real':
zPlot = np.real(array[flag][indUniSta])
elif par == 'imag':
zPlot = np.imag(array[flag][indUniSta])
elif par == 'res':
zPlot = (appResFact/freq)*np.abs(array[flag][indUniSta])**2
elif par == 'phs':
zPlot = np.arctan2(array[flag][indUniSta].imag,array[flag][indUniSta].real)*(180/np.pi)
if not pColor:
if 'xx' in flag:
lab = 'XX'
pColor = 'g'
elif 'xy' in flag:
lab = 'XY'
pColor = 'r'
elif 'yx' in flag:
lab = 'YX'
pColor = 'b'
elif 'yy' in flag:
lab = 'YY'
pColor = 'y'
ax.plot(freq,zPlot,color=pColor,marker=pSym,label=flag+addLabel,zorder=zorder)
def plotIsoStaImpedance(ax,
loc,
array,
flag,
par="abs",
pSym="s",
pColor=None,
addLabel="",
zorder=1):
appResFact = 1 / (8 * np.pi**2 * 10**(-7))
treshold = 1.0 # 1 meter
indUniSta = (np.sqrt(
np.sum((array[["x", "y"]].copy().view(
(float, 2)) - loc)**2, axis=1)) < treshold)
freq = array["freq"][indUniSta]
if par == "abs":
zPlot = np.abs(array[flag][indUniSta])
elif par == "real":
zPlot = np.real(array[flag][indUniSta])
elif par == "imag":
zPlot = np.imag(array[flag][indUniSta])
elif par == "res":
zPlot = (appResFact / freq) * np.abs(array[flag][indUniSta])**2
elif par == "phs":
zPlot = np.arctan2(array[flag][indUniSta].imag,
array[flag][indUniSta].real) * (180 / np.pi)
if not pColor:
if "xx" in flag:
lab = "XX"
pColor = "g"
elif "xy" in flag:
lab = "XY"
pColor = "r"
elif "yx" in flag:
lab = "YX"
pColor = "b"
elif "yy" in flag:
lab = "YY"
pColor = "y"
ax.plot(freq,
zPlot,
color=pColor,
marker=pSym,
label=flag + addLabel,
zorder=zorder)
def plotIsoStaImpedance(ax,
loc,
array,
flag,
par='abs',
pSym='s',
pColor=None,
addLabel='',
zorder=1):
appResFact = 1 / (8 * np.pi**2 * 10**(-7))
treshold = 1.0 # 1 meter
indUniSta = np.sqrt(
np.sum((array[['x', 'y']].view(
(float, 2)) - loc)**2, axis=1)) < treshold
freq = array['freq'][indUniSta]
if par == 'abs':
zPlot = np.abs(array[flag][indUniSta])
elif par == 'real':
zPlot = np.real(array[flag][indUniSta])
elif par == 'imag':
zPlot = np.imag(array[flag][indUniSta])
elif par == 'res':
zPlot = (appResFact / freq) * np.abs(array[flag][indUniSta])**2
elif par == 'phs':
zPlot = np.arctan2(array[flag][indUniSta].imag,
array[flag][indUniSta].real) * (180 / np.pi)
if not pColor:
if 'xx' in flag:
lab = 'XX'
pColor = 'g'
elif 'xy' in flag:
lab = 'XY'
pColor = 'r'
elif 'yx' in flag:
lab = 'YX'
pColor = 'b'
elif 'yy' in flag:
lab = 'YY'
pColor = 'y'
ax.plot(freq,
zPlot,
color=pColor,
marker=pSym,
label=flag + addLabel,
zorder=zorder)
def initialize(self, image, customize=True, xlim=None, ylim=None, xlabel='', ylabel='', log=True, vmin=None, vmax=None, maxresolution=1280, **kwargs):
'''Display one image, give user a chance to zoom in, and leave everything set for populating with later images.'''
if customize | (xlim is None )| (ylim is None):
self.xlim = [0, image.shape[1]]
self.ylim = [0, image.shape[0]]
else:
self.xlim = xlim
self.ylim = ylim
zoomed = image[self.ylim[0]:self.ylim[1], self.xlim[0]:self.xlim[1]]
# create the figure, using xlim and ylim to determine the scale of the figure
plt.ion()
# create an empty dictionary to store the axes objects
self.ax = {}
# calculate aspect ratio (y/x)
self.ysize, self.xsize = self.ylim[1] - self.ylim[0], self.xlim[1] - self.xlim[0]
aspect = np.float(self.ysize)/self.xsize
# set up the geometry of the plotting figure, including the desired resolution, histograms, and space for labels
scale = 7.5
dpi = maxresolution/np.maximum(scale, scale*aspect)
margin = 0.07
histheight = 0.1
inflate = 2*margin + histheight +1
self.figure = plt.figure(figsize=(scale*inflate, scale*aspect*inflate), dpi=dpi)
gs = plt.matplotlib.gridspec.GridSpec(2,2,width_ratios=[1,histheight], height_ratios=[histheight, 1], top=1-margin, bottom=margin, left=margin, right=1-margin, hspace=0, wspace=0)
# define panes for the image, as well as summed x and y histogram plots
self.ax['image'] = plt.subplot(gs[1,0])
self.ax['xhist'] = plt.subplot(gs[0,0], sharex=self.ax['image'] )
self.ax['yhist'] = plt.subplot(gs[1,1], sharey=self.ax['image'] )
self.ax['navigator'] = plt.subplot(gs[0,1])
# clear the axes labels
for k in self.ax.keys():
plt.setp(self.ax[k].get_xticklabels(), visible=False)
plt.setp(self.ax[k].get_yticklabels(), visible=False)
# set default image display keywords
self.imagekw = dict(cmap='gray',interpolation='nearest',extent=[0, self.xsize, 0, self.ysize], aspect='equal')
# replace any of these, as defined through the input keywords
for k in kwargs.keys():
self.imagekw[k] = kwargs[k]
#if customize:
# self.imagekw['aspect'] = 'auto'
# set the default line plotting keywords
self.linekw = dict(color='black', linewidth=1, alpha=0.5)
# make sure the min and max values for the color scale are set
if vmin is None:
vmin = np.min(image)
if vmax is None:
vmax = np.max(image)
self.vmin, self.vmax = vmin, vmax
# keep track of whether we're using a log scale
self.log = log
# calculate summed histograms
xhist = np.sum(zoomed, 0)
yhist = np.sum(zoomed, 1)
# readjust things if a log scale is set
if self.log:
zoomedtoplot = logify(zoomed)
imagetoplot = logify(image)
self.imagekw['vmin'] = np.log(np.maximum(vmin, 1))
self.imagekw['vmax'] = np.log(vmax)
else:
zoomedtoplot = zoomed
imagetoplot = image
self.imagekw['vmin'] = vmin
self.imagekw['vmax'] = vmax
# keep the navigator like the image, but adjust its extent back to the regular
self.navigatorkw = self.imagekw.copy()
self.navigatorkw['extent'] = [0,image.shape[1], 0, image.shape[0]]
# keep track of the data that goes into each plot
self.current = {}
# plot the histograms, once zoomed in
self.current['xhist'] = self.ax['xhist'].plot(np.arange(len(xhist)), xhist, **self.linekw)[0]
self.current['yhist'] = self.ax['yhist'].plot(yhist, np.arange(len(yhist)), **self.linekw)[0]
self.ax['xhist'].set_xlim(0, zoomed.shape[1]-1)
self.ax['xhist'].set_ylim(vmin*zoomed.shape[0], vmax*zoomed.shape[0])
self.ax['yhist'].set_xlim(vmin*zoomed.shape[1], vmax*zoomed.shape[1])
self.ax['xhist'].set_yscale('log')
self.ax['yhist'].set_xscale('log')
self.ax['yhist'].set_ylim(0, zoomed.shape[0]-1)
# plot the (zoomed) image
self.current['image'] = self.ax['image'].imshow(zoomedtoplot, **self.imagekw)
self.current['navigator'] = self.ax['navigator'].imshow(imagetoplot, **self.navigatorkw)
self.current['rectangle'] = self.ax['navigator'].add_patch(plt.matplotlib.patches.Rectangle((self.xlim[0], self.ylim[0]), self.xlim[1] - self.xlim[0], self.ylim[1]-self.ylim[0], edgecolor='red', facecolor='none', alpha=0.5, linewidth=5))
self.speak('created new image and plots')
if customize:
self.name = self.input('Please zoom to desired limits, enter a label to identify this window, and press return:')
xlim, ylim = np.array(self.ax['image'].get_xlim()), np.array(self.ax['image'].get_ylim())
xlim[0] = np.maximum(xlim[0], 0)
ylim[0] = np.maximum(ylim[0], 0)
xlim[1] = np.minimum(xlim[1], self.xsize)
ylim[1] = np.minimum(ylim[1], self.ysize)
self.initialize(image, customize=False, xlim=xlim, ylim=ylim, log=log, vmin=vmin, vmax=vmax, maxresolution=maxresolution, **kwargs)
self.speak('the display has been fully initialized -- ready for new plots!')