def plot_std_meshlines(self, step=0.1):
'''
plot mesh circles for stdv
'''
color = self.std_color
nstdmax = self.stdmax
if self.negative:
axmin = -np.pi / 2.
else:
axmin = 0.
th = np.arange(axmin, np.pi / 2, 0.01)
for ra in np.arange(0, nstdmax + 0.1 * step, step):
self.ax.plot(ra * np.sin(th), ra * np.cos(th), ':', color=color)
if self.normalize:
self.ax.set_ylabel('$\sigma / \sigma_{obs}$', color=color)
self.ax.set_xlabel('$\sigma / \sigma_{obs}$', color=color)
else:
self.ax.set_ylabel('Standard Deviation', color=color)
self.ax.set_xlabel('Standard Deviation', color=color)
xticklabels = plt.getp(plt.gca(), 'xticklabels')
plt.setp(xticklabels, color=color)
yticklabels = plt.getp(plt.gca(), 'yticklabels')
plt.setp(yticklabels, color=color)
def plot_std_meshlines(self, step=0.1):
'''
plot mesh circles for stdv
'''
color = self.std_color
nstdmax = self.stdmax
if self.negative:
axmin = -np.pi / 2.
else:
axmin = 0.
th = np.arange(axmin, np.pi / 2, 0.01)
for ra in np.arange(0, nstdmax + 0.1 * step, step):
self.ax.plot(ra * np.sin(th), ra * np.cos(th), ':', color=color)
if self.normalize:
self.ax.set_ylabel('$\sigma / \sigma_{obs}$', color=color)
self.ax.set_xlabel('$\sigma / \sigma_{obs}$', color=color)
else:
self.ax.set_ylabel('Standard Deviation', color=color)
self.ax.set_xlabel('Standard Deviation', color=color)
xticklabels = plt.getp(plt.gca(), 'xticklabels')
plt.setp(xticklabels, color=color)
yticklabels = plt.getp(plt.gca(), 'yticklabels')
plt.setp(yticklabels, color=color)
def render_21_output(
ax, fig, y_out, M=100, y0range=[-1, 1], y1range=[-1, 1], cmap=None,
):
img = ax.imshow(
np.reshape(y_out, [M, M]),
origin='lower',
extent=[y0range[0], y0range[1], y1range[0], y1range[1]],
cmap=cmap,
)
ax.set_xlabel(r'$y_1$')
ax.set_ylabel(r'$y_2$')
axins1 = inset_axes(
ax,
width="40%", # width = 50% of parent_bbox width
height="5%", # height : 5%
loc='upper right',
)
imgmin = np.min(y_out)
imgmax = np.max(y_out)
color_bar = fig.colorbar(
img,
cax=axins1,
orientation="horizontal",
ticks=np.linspace(imgmin, imgmax, 3),
)
cbxtick_obj = plt.getp(color_bar.ax.axes, 'xticklabels')
plt.setp(cbxtick_obj, color="white")
axins1.xaxis.set_ticks_position("bottom")
def add_percent_ticks():
ticks=pylab.getp(pylab.gca(),'xticks')
ticklabels=len(ticks)*['']
ticklabels[0]='0%'
ticklabels[-1]='100%'
pylab.setp(pylab.gca(), xticklabels=ticklabels)
pylab.setp(pylab.gca(), yticklabels=['0%','100%'])
ticks=pylab.getp(pylab.gca(),'yticks')
ticklabels=len(ticks)*['']
#ticklabels[0]='0%'
ticklabels[-1]='100%'
pylab.setp(pylab.gca(), yticklabels=ticklabels)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
def add_percent_ticks():
ticks = pylab.getp(pylab.gca(), 'xticks')
ticklabels = len(ticks) * ['']
ticklabels[0] = '0%'
ticklabels[-1] = '100%'
pylab.setp(pylab.gca(), xticklabels=ticklabels)
pylab.setp(pylab.gca(), yticklabels=['0%', '100%'])
ticks = pylab.getp(pylab.gca(), 'yticks')
ticklabels = len(ticks) * ['']
#ticklabels[0]='0%'
ticklabels[-1] = '100%'
pylab.setp(pylab.gca(), yticklabels=ticklabels)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
def modifValZone(self, nameVar, ind, val):
"""modifier dans la zone nameVar la valeur
ou liste valeur, attention le texte de la zone contient nom et valeur"""
obj = self.listeZoneText[nameVar][ind].getObject()
if type(obj) == type([2, 3]):
for i in range(len(obj)):
pl.setp(obj[i], text=str(val[i]))
else:
nom = pl.getp(obj, 'text').split('\n')[0]
pl.setp(obj, text=nom + '\n' + str(val)[:16])
self.redraw()
def modifValZone(self, nameVar, ind, val):
"""modifier dans la zone nameVar la valeur
ou liste valeur, attention le texte de la zone contient nom et valeur"""
obj = self.listeZoneText[nameVar][ind].getObject()
if type(obj) == type([2, 3]):
for i in range(len(obj)):
pl.setp(obj[i], text=str(val[i]))
else:
nom = pl.getp(obj, "text").split("\n")[0]
pl.setp(obj, text=nom + "\n" + str(val)[:16])
self.redraw()
def modifZoneAttr(self, nameVar, ind, val, media, xy):
# modify xy
zone = self.listeZone[nameVar][ind]
if len(xy[0]) == 3: x, y, z = zip(*xy)
else: x, y = zip(*xy)
zone.set_data(x, y)
# modify media
if type(media) != type([2]): media = [int(media)]
self.listeZmedia[nameVar][ind] = media
# modify text
textObj = self.listeZoneText[nameVar][ind]
if type(textObj) == type([2, 3]):
name = pl.getp(textObj[0], 'text').split('\n')[0]
pl.setp(textObj[0], text=name + '\n' + str(val)[:16])
for i in range(len(z)):
pl.setp(textObj[i + 1], text=str(z[i]))
else:
name = pl.getp(textObj, 'text').split('\n')[0]
pl.setp(textObj, text=name + '\n' + str(val)[:16])
self.redraw()
def plot_stack_weight(self,h=None):
if h is None:
try:
h = self.h
except:
h = self.get_hh()
if isinstance(h,pd.core.frame.DataFrame):
h_tmp = {}
h_tmp['weight'] = h
h = pd.Panel(h_tmp).transpose(1,2,0)
zhfont1 = matplotlib.font_manager.FontProperties(fname='C:\Windows\Fonts\simkai.ttf')#自定义字体为楷体
#画出权重的堆积曲线
# ax = hh.plot(kind='area')
# ax.set_ylim((0,1))
# ax.set_title(freq_dict[freq][0]+' change datalength '+str(freq_len*freq_dict[freq][1])+'days')
axes = {}
for method in h.minor_axis:
hh = h[:,:,method].T
hh = hh.loc[:,hh.mean().sort_values(ascending=True).index]
hh = hh.rename(columns=self.CODE_DICT)
fig,ax = plt.subplots()
#area图有个反人性的地方：legend的颜色顺序和图的颜色顺序相反，这里想办法设置一下
a = ax.stackplot(hh.index,hh.T)
try:
ax.legend([i.decode('utf-8') for i in hh.columns[::-1]],prop=zhfont1)
except:
ax.legend(list(hh.columns[::-1]),prop=zhfont1)
color = []
for i in range(self.n):
color.append(plt.getp(a[i],'facecolor'))
color = color[::-1]
for i in range(self.n):
plt.setp(a[i],'facecolor',color[i])
ax.set_xlim(tuple(hh.index[[0,-1]]))
ax.set_ylim((0,1))
plt.title(method)
axes[method] = ax
return h,axes
def icolorbar(mappable,
loc=2,
orientation='horizontal',
borderpad=1,
width=None,
height=None,
ax=None,
label=None,
axes_kwargs=None,
**kwargs):
'''An inset colorbar. Should be a simple replacement for colorbar
orientation='horizontal', width='30%', height='2%'
orientation='vertical', width='5%', height='25%'
bbox_to_anchor=(1.05, 0., 1, 1),
bbox_transform=ax2.transAxes,
borderpad=0,
http://matplotlib.org/examples/axes_grid/demo_colorbar_with_inset_locator.html
'''
if ax is None:
ax = pylab.gca()
if width is None:
width = '30%' if orientation == 'horizontal' else '2%'
if height is None:
height = '2%' if orientation == 'horizontal' else '25%'
if label is None:
try:
label = mappable.get_label()
if label.startswith('_'):
# hide internal names
label = ''
except:
label = ''
tmp = dict(
nticks=3,
ticks=None,
ticknames=None,
tickfmt=None,
tickfcn=None,
orientation=orientation,
rotate=None,
outline=True,
outline_props=dict(),
color=None,
)
tmp.update(kwargs)
nticks = tmp.pop('nticks')
ticks = tmp.pop('ticks')
tickfmt = tmp.pop('tickfmt')
tickfcn = tmp.pop('tickfcn')
ticknames = tmp.pop('ticknames')
rotate = tmp.pop('rotate')
outlinetext = tmp.pop('outline')
outline_props = tmp.pop('outline_props')
color = tmp.pop('color')
if 'cax' not in tmp:
if axes_kwargs is None:
axes_kwargs = {'borderpad': borderpad}
tmp['cax'] = inset_axes(ax,
width=width,
height=height,
loc=loc,
**axes_kwargs)
cb = pylab.colorbar(mappable, **tmp)
cb.set_label(label)
if ticks is None:
ticks = np.linspace(cb.vmin, cb.vmax, nticks)
cb.set_ticks(ticks)
if tickfcn is not None:
ticks = map(tickfcn, ticks)
if tickfmt is not None:
cb.set_ticklabels(map(tickfmt.format, ticks))
if ticknames is not None:
cb.set_ticklabels(ticknames)
if rotate is not None:
if not isinstance(rotate, (int, float)):
rotate = 90
pylab.setp(pylab.xticks(axes=cb.ax)[1], rotation=rotate, ha='center')
if color is not None:
if orientation == 'horizontal':
pylab.setp(pylab.getp(cb.ax.axes, 'xticklabels'), color=color)
pylab.setp(pylab.getp(cb.ax.xaxis, 'label'), color=color)
else:
pylab.setp(pylab.getp(cb.ax.axes, 'yticklabels'), color=color)
pylab.setp(pylab.getp(cb.ax.yaxis, 'label'), color=color)
if outlinetext:
if orientation == 'horizontal':
items = [cb.ax.xaxis.get_label(), cb.ax.xaxis.get_ticklabels()]
else:
items = [cb.ax.yaxis.get_label(), cb.ax.yaxis.get_ticklabels()]
outline(items, **outline_props)
pylab.sca(ax)
return cb
def decisionSurface(classifier, fileName=None, **args):
global data
global numpy_container
classifier.train(data)
numContours = 3
if 'numContours' in args:
numContours = args['numContours']
title = None
if 'title' in args:
title = args['title']
markersize = 5
fontsize = 'medium'
if 'markersize' in args:
markersize = args['markersize']
if 'fontsize' in args:
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args:
contourFontsize = args['contourFontsize']
showColorbar = False
if 'showColorbar' in args:
showColorbar = args['showColorbar']
show = True
if fileName is not None:
show = False
if 'show' in args:
show = args['show']
# setting up the grid
delta = 0.01
if 'delta' in args:
delta = args['delta']
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float)
gridX = numpy.zeros((len(x) * len(y), 2), numpy.float)
n = 0
for i in range(len(x)):
for j in range(len(y)):
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if not numpy_container:
gridData = VectorDataSet(gridX)
gridData.attachKernel(data.kernel)
else:
gridData = PyVectorDataSet(gridX)
results = classifier.test(gridData)
n = 0
for i in range(len(x)):
for j in range(len(y)):
Z[i][j] = results.decisionFunc[n]
n += 1
#pylab.figure()
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear',
origin='lower',
cmap=pylab.cm.gray,
extent=(xmin, xmax, ymin, ymax))
if numContours == 1:
C = pylab.contour(numpy.transpose(Z), [0],
origin='lower',
linewidths=(3),
colors='black',
extent=(xmin, xmax, ymin, ymax))
elif numContours == 3:
C = pylab.contour(numpy.transpose(Z), [-1, 0, 1],
origin='lower',
linewidths=(1, 3, 1),
colors='black',
extent=(xmin, xmax, ymin, ymax))
else:
C = pylab.contour(numpy.transpose(Z),
numContours,
origin='lower',
linewidths=2,
extent=(xmin, xmax, ymin, ymax))
pylab.clabel(C, inline=1, fmt='%1.1f', fontsize=contourFontsize)
# plot the data
scatter(data, markersize=markersize)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
if title is not None:
pylab.title(title, fontsize=fontsize)
if showColorbar:
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None:
pylab.savefig(fileName)
if show:
pylab.show()
fitData['QF'][ejThreshold].append(bestQF)
# Plot figures if wished for
if plotFigures:
# Left: 2d distribution of chi square. (x = neutron flux // y = quenching factor)
# Right: Experimental residual spectrum + best fit simulation (x = NPE // y = counts)
fig = plt.figure(figsize=(6.5,3.25),edgecolor='k',facecolor='w')
ax0 = plt.subplot(121)
extent = [sArr[0]/1000.0,sArr[-1]/1000.0,qfSamplingArr[0],qfSamplingArr[-1]]
h = np.reshape(x2Arr,(nQF,nS))
plt.imshow(h,aspect='auto',cmap=colorMap,origin='lower',extent=extent,norm=LogNorm(vmin=x2PlotLims[0],vmax=x2PlotLims[1]))
plt.text(0.7,0.0945,r'$\chi^2$',ha='center',va='center',fontsize=legendFS)
formatter = LogFormatter(10, labelOnlyBase=False)
cbaxes = fig.add_axes([0.17, 0.87, 0.25, 0.050])
cb=plt.colorbar(cax=cbaxes, orientation='horizontal',ticks=x2Ticks, format=formatter)
plt.setp(plt.getp(cb.ax.axes, 'xticklabels'), fontsize=labelFS,color=colors.black)
plt.sca(ax0)
plt.xlim(extent[0],extent[1])
plt.ylim(extent[2],extent[3])
plt.xlabel('Neutron flux [1000 n/s]')
plt.ylabel('Quenching factor')
ctr = plt.contour(h,[np.min(h)+1,np.min(h)+2],extent=extent,linestyles=['solid','dotted'],colors=['w','w'],origin='lower')
# Extract sigma levels from contour line
p = ctr.collections[0].get_paths()[0]
v = p.vertices
x = v[:,0]
y = v[:,1]
maxScaling = np.max(x) * 1000.0
minScaling = np.min(x) * 1000.0
def icolorbar(mappable, loc=2, orientation='horizontal', borderpad=1,
width=None, height=None, ax=None,
label=None, axes_kwargs=None,
**kwargs):
'''An inset colorbar. Should be a simple replacement for colorbar
orientation='horizontal', width='30%', height='2%'
orientation='vertical', width='5%', height='25%'
bbox_to_anchor=(1.05, 0., 1, 1),
bbox_transform=ax2.transAxes,
borderpad=0,
http://matplotlib.org/examples/axes_grid/demo_colorbar_with_inset_locator.html
'''
if ax is None:
ax = pylab.gca()
if width is None:
width = '30%' if orientation == 'horizontal' else '2%'
if height is None:
height = '2%' if orientation == 'horizontal' else '25%'
if label is None:
try:
label = mappable.get_label()
if label.startswith('_'):
# hide internal names
label = ''
except:
label = ''
tmp = dict(
nticks=3,
ticks=None,
ticknames=None,
tickfmt=None,
tickfcn=None,
orientation=orientation,
rotate=None,
outline=True,
outline_props=dict(),
color=None,
)
tmp.update(kwargs)
nticks = tmp.pop('nticks')
ticks = tmp.pop('ticks')
tickfmt = tmp.pop('tickfmt')
tickfcn = tmp.pop('tickfcn')
ticknames = tmp.pop('ticknames')
rotate = tmp.pop('rotate')
outlinetext = tmp.pop('outline')
outline_props = tmp.pop('outline_props')
color = tmp.pop('color')
if 'cax' not in tmp:
if axes_kwargs is None:
axes_kwargs = {'borderpad':borderpad}
tmp['cax'] = inset_axes(ax, width=width, height=height, loc=loc, **axes_kwargs)
cb = pylab.colorbar(mappable, **tmp)
cb.set_label(label)
if ticks is None:
ticks = np.linspace(cb.vmin, cb.vmax, nticks)
cb.set_ticks(ticks)
if tickfcn is not None:
ticks = map(tickfcn, ticks)
if tickfmt is not None:
cb.set_ticklabels(map(tickfmt.format, ticks))
if ticknames is not None:
cb.set_ticklabels(ticknames)
if rotate is not None:
if not isinstance(rotate, (int,float)):
rotate = 90
pylab.setp(pylab.xticks(axes=cb.ax)[1], rotation=rotate, ha='center')
if color is not None:
if orientation == 'horizontal':
pylab.setp(pylab.getp(cb.ax.axes, 'xticklabels'), color=color)
pylab.setp(pylab.getp(cb.ax.xaxis, 'label'), color=color)
else:
pylab.setp(pylab.getp(cb.ax.axes, 'yticklabels'), color=color)
pylab.setp(pylab.getp(cb.ax.yaxis, 'label'), color=color)
if outlinetext:
if orientation == 'horizontal':
items = [cb.ax.xaxis.get_label(),
cb.ax.xaxis.get_ticklabels()]
else:
items = [cb.ax.yaxis.get_label(),
cb.ax.yaxis.get_ticklabels()]
outline(items, **outline_props)
pylab.sca(ax)
return cb
def plotOrbitalEnergyChange(p, m2, label):
"""Plot the change in orbital energy.
Keyword arguments:
p -- MESA profile
m2 -- mass of the secondary
label -- text appearing in legend
"""
import mesa_reader as mr
import matplotlib.pylab as plt
import numpy as np
import os
from math import log
from scipy.integrate import cumtrapz
import math
# import function from another file
from ipynb.fs.full.functions import getMaxRadiusProfile
G = 6.67408e-11 # gravitational constant
# change G to cgs units
G = G * 1e3
coreMass = p.he_core_mass + p.c_core_mass + p.o_core_mass + p.si_core_mass + p.fe_core_mass
# change from Msuns to grams
coreMass = coreMass * 1.989e33
# get rshred
r2 = getR2(m2)
m2 = m2 * 1.989e33 # units
r2 = r2 * 69.551e9 # units
rshred = r2 * (2 * coreMass / m2)**(1 / 3)
radius = p.radius
radius = radius * 69.551e9
masses = p.mass
masses = masses * 1.989e33
ri = p.radius[0] * 69.551e9
mi = p.initial_mass * 1.989e33
deltaEOrb = (mi / ri) - (masses / radius)
deltaEOrb = deltaEOrb * G * m2 / 2
deltaEOrb = abs(deltaEOrb)
# look through everything in the radius array and compare it to rshred
# when the value is <= rshred, save that index
# chop the integrand and radius arrays at that index
i = 0
for x in radius:
if x > rshred:
i += 1
radius = radius[100:i]
deltaEOrb = deltaEOrb[100:i]
point = plt.plot(radius[len(radius) - 1], deltaEOrb[len(deltaEOrb) - 1],
'o')
y = plt.getp(point[0], 'color')
plt.loglog(radius, deltaEOrb, label=label, color=y)
def plotDragLum(p, m2, label):
"""Plot the drag luminosity.
Keyword arguments:
p -- MESA profile
m2 -- mass of the secondary
label -- text appearing in legend
"""
import mesa_reader as mr
import matplotlib.pylab as plt
import numpy as np
import os
from math import log
from scipy.integrate import cumtrapz
import math
# import function from another file
from ipynb.fs.full.functions import getMaxRadiusProfile
G = 6.67408e-11 # gravitational constant
# change G to cgs units
G = G * 1e3
coreMass = p.he_core_mass + p.c_core_mass + p.o_core_mass + p.si_core_mass + p.fe_core_mass
# change from Msuns to grams
coreMass = coreMass * 1.989e33
# get rshred
r2 = getR2(m2)
m2 = m2 * 1.989e33 # units
r2 = r2 * 69.551e9 # units
rshred = r2 * (2 * coreMass / m2)**(1 / 3)
radius = p.radius
radius = radius * 69.551e9
masses = p.mass
masses = masses * 1.989e33
vkep_r = np.sqrt(G * masses / radius)
rho = p.logRho
rho = 10**rho
xi = 4
rAcc = 2 * G * m2 / vkep_r**2
dragLum = xi * math.pi * rAcc**2 * rho * vkep_r**3
# stop at rshred
i = 0
for x in radius:
if x > rshred:
i += 1
radius = radius[100:i]
dragLum = dragLum[100:i]
point = plt.plot(radius[len(radius) - 1], dragLum[len(dragLum) - 1], 'o')
y = plt.getp(point[0], 'color')
plt.loglog(radius, dragLum, label=label, color=y)
def plotTInspiral(p, m2, label):
"""Plot the inspiral time scale.
Keyword arguments:
p -- MESA profile
m2 -- mass of the secondary
label -- text appearing in the legend
"""
import mesa_reader as mr
import matplotlib.pylab as plt
import numpy as np
import os
from math import log
from scipy.integrate import cumtrapz
import math
# import function from another file
from ipynb.fs.full.functions import getMaxRadiusProfile
G = 6.67408e-11 # gravitational constant
# change G to cgs units
G = G * 1e3
coreMass = p.he_core_mass + p.c_core_mass + p.o_core_mass + p.si_core_mass + p.fe_core_mass
# change from Msuns to grams
coreMass = coreMass * 1.989e33
radius = p.radius
radius = radius * 69.551e9
# setting up constants
r2 = getR2(m2)
m2 = m2 * 1.989e33 # units
r2 = r2 * 69.551e9 # units
xi = 4
rshred = r2 * (2 * coreMass / m2)**(1 / 3)
k = 4 * xi * math.pi * G * m2
# density
rho = p.logRho
rho = 10**rho
# masses
masses = p.mass
masses = masses * 1.989e33
# keplerian velocity
vkep_r = np.sqrt(G * masses / radius)
# dM/dr
dMdr = np.diff(masses) / np.diff(radius)
# make all the array sizes the same
vkep_r = vkep_r[:-1]
radius = radius[:-1]
rho = rho[:-1]
masses = masses[:-1]
# integrand
integrand = (dMdr - (masses / radius)) * vkep_r / (k * radius * rho)
# look through everything in the radius array and compare it to rshred
# when the value is <= rshred, save that index
# chop the integrand and radius arrays at that index
i = 0
for x in radius:
if x > rshred:
i += 1
# radius = radius[100:i] # cut first
# integrand = integrand[100:i] # cut first
# actually integrate
tInspiral = cumtrapz(y=integrand, x=radius)
radius = radius[100:i] # int first
tInspiral = tInspiral[100:i] # int first
# radius = radius[:-1] # cut first
radius = np.flip(radius)
point = plt.plot(radius[0], tInspiral[0], 'o')
y = plt.getp(point[0], 'color')
plt.loglog(radius, tInspiral, label=label, color=y, linestyle=':')
def decisionSurface(classifier, fileName = None, **args) :
global data
global numpy_container
classifier.train(data)
numContours = 3
if 'numContours' in args :
numContours = args['numContours']
title = None
if 'title' in args :
title = args['title']
markersize=5
fontsize = 'medium'
if 'markersize' in args :
markersize = args['markersize']
if 'fontsize' in args :
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args :
contourFontsize = args['contourFontsize']
showColorbar = False
if 'showColorbar' in args :
showColorbar = args['showColorbar']
show = True
if fileName is not None :
show = False
if 'show' in args :
show = args['show']
# setting up the grid
delta = 0.01
if 'delta' in args :
delta = args['delta']
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float)
gridX = numpy.zeros((len(x) *len(y), 2), numpy.float)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if not numpy_container :
gridData = VectorDataSet(gridX)
gridData.attachKernel(data.kernel)
else :
gridData = PyVectorDataSet(gridX)
results = classifier.test(gridData)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
Z[i][j] = results.decisionFunc[n]
n += 1
#pylab.figure()
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear', origin='lower',
cmap=pylab.cm.gray, extent=(xmin,xmax,ymin,ymax) )
if numContours == 1 :
C = pylab.contour(numpy.transpose(Z),
[0],
origin='lower',
linewidths=(3),
colors = 'black',
extent=(xmin,xmax,ymin,ymax))
elif numContours == 3 :
C = pylab.contour(numpy.transpose(Z),
[-1,0,1],
origin='lower',
linewidths=(1,3,1),
colors = 'black',
extent=(xmin,xmax,ymin,ymax))
else :
C = pylab.contour(numpy.transpose(Z),
numContours,
origin='lower',
linewidths=2,
extent=(xmin,xmax,ymin,ymax))
pylab.clabel(C,
inline=1,
fmt='%1.1f',
fontsize=contourFontsize)
# plot the data
scatter(data, markersize=markersize)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
if title is not None :
pylab.title(title, fontsize=fontsize)
if showColorbar :
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None :
pylab.savefig(fileName)
if show :
pylab.show()
def FillTree(self):
# For ease of coding
T = self.TreeCtrlMain
# starting from the top, grab ALL the wx windows available
w = wx.GetTopLevelWindows()
# find all the windows that are plot windows for wxagg
self.plot_windows = []
for x in w:
if type(x) == matplotlib.backends.backend_wxagg.FigureFrameWxAgg:
self.plot_windows.append(x)
# get all the figures associated with these windows
self.figures = []
for x in self.plot_windows:
self.figures.append(x.canvas.figure)
# all items in the tree must have data associated with it
# we use a dictionary of important values for ease of coding
# children, parents and selfs are all tree ID's
T.SetItemData(self.tree_root,
wx.TreeItemData({"string":"Current Session",
"type":"tree_root_title",
"self":self.tree_root,
"parent":None,
"children":[],
"window":None,
"figure":None,
"axes":None,
"line":None}))
# fill the tree items!
# kill all the root's children
T.DeleteChildren(self.tree_root)
# now loop over the figures and add a branch for each
for nf in range(0,len(self.figures)):
f = self.figures[nf]
w = self.plot_windows[nf]
# add the tree item corresponding to the figure
cf_title = self.plot_windows[nf].GetTitle()
cf = T.AppendItem(self.tree_root, cf_title)
# we need to append this to the children array in the root
self.AppendTreeItemData(self.tree_root, "children", cf)
# we also need to set the tree item data for this item
T.SetItemData(cf,
wx.TreeItemData({"string":cf_title,
"type":"figure_title",
"self":cf,
"parent":self.tree_root,
"children":[],
"window":w,
"figure":f,
"axes":None,
"line":None}))
# now loop over the axes
for na in range(0,len(f.axes)):
a = f.axes[na]
# add the axes tree item to the figure item
ca_title = "axes "+str(na)
ca = T.AppendItem(cf, ca_title)
# now append this id to the children array of the figure
self.AppendTreeItemData(cf, "children", ca)
# we also need to set the tree item data for this item
T.SetItemData(ca,
wx.TreeItemData({"string":ca_title,
"type":"axes_tree_label",
"self":ca,
"parent":cf,
"children":[],
"window":w,
"figure":f,
"axes":a,
"line":None}))
# add the "axes title" item to the tree and axes children list
p = T.AppendItem(ca, "Axes Title")
self.AppendTreeItemData(ca, "children", p)
T.SetItemData(p,
wx.TreeItemData({"string":a.title.get_text(),
"type":"axes_title",
"self":p,
"parent":ca,
"children":[],
"window":w,
"figure":f,
"axes":a,
"line":None}))
# add the "y-scale" item to the tree and axes children list
p = T.AppendItem(ca, "y-scaling = 1.0")
self.AppendTreeItemData(ca, "children", p)
T.SetItemData(p,
wx.TreeItemData({"type":"y_scale",
"string":"1.0",
"last_value":1.0,
"self":p,
"parent":ca,
"children":[],
"window":w,
"figure":f,
"axes":a,
"line":None}))
# now loop over the lines
lines = a.get_lines()
for nl in range(0,len(lines)):
l = lines[nl]
# add the axes tree item to the figure item
cl_title = "line "+str(nl)
cl = T.AppendItem(ca, cl_title)
# now append this id to the children array of the figure
self.AppendTreeItemData(ca, "children", cl)
# we also need to set the tree item data for this item
T.SetItemData(cl,
wx.TreeItemData({"string":cl_title,
"type":"line_title",
"self":cl,
"parent":ca,
"children":[],
"window":w,
"figure":f,
"axes":a,
"line":l}))
# now we set the individual line attributes
for x in self.line_attributes:
# make the tree branch
y = T.AppendItem(cl, x+": '"+str(pylab.getp(l,x))+"'")
self.AppendTreeItemData(cl, "children", y)
T.SetItemData(y,
wx.TreeItemData({"string": str(pylab.getp(l,x)),
"type":x,
"self":y,
"parent":cl,
"children":[],
"window":w,
"figure":f,
"axes":a,
"line":l}))
# now expand the bitch
T.Expand(self.tree_root)