def plot_figures(figures, nrows=1, ncols=1):
"""Plot a dictionary of figures.
Parameters
----------
figures : <title, figure> dictionary
ncols : number of columns of subplots wanted in the display
nrows : number of rows of subplots wanted in the figure
"""
fig, axeslist = plt.subplots(ncols=ncols, nrows=nrows)
ravel = axeslist.ravel()
for ind, img in enumerate(figures):
ravel[ind].imshow(img, cmap=plt.jet())
ravel[ind].set_title(" %2d " % (ind))
ravel[ind].set_axis_off()
plt.tight_layout() # optional
def plot_map(self, dataset, attribute_data, min_value=None, max_value=None, file=None,
my_title="", filter=None, background=None):
""" Plots a 2D image of attribute given by 'name'. matplotlib required.
The dataset must have a method 'get_2d_attribute' defined that returns
a 2D array that is to be plotted. If min_value/max_value are given, all values
that are smaller/larger than these values are set to min_value/max_value.
Argument background is a value to be used for background. If it is not given,
it is considered as a 1/100 under the minimum value of the array.
Filter is a 2D array. Points where filter is > 0 are masked out (put into background).
"""
import matplotlib
matplotlib.use('Qt4Agg')
from matplotlib.pylab import jet,imshow,colorbar,show,axis,savefig,close,figure,title,normalize
from matplotlib.pylab import rot90
attribute_data = attribute_data[filter]
coord_2d_data = dataset.get_2d_attribute(attribute_data = attribute_data)
data_mask = coord_2d_data.mask
# if filter is not None:
# if isinstance(filter, ndarray):
# if not ma.allclose(filter.shape, coord_2d_data.shape):
# raise StandardError, "Argument filter must have the same shape as the 2d attribute."
# filter_data = filter
# else:
# raise TypeError, "The filter type is invalid. A character string or a 2D numpy array allowed."
# filter_data = where(ma.filled(filter_data,1) > 0, 1,0)
# data_mask = ma.mask_or(data_mask, filter_data)
nonmaskedmin = ma.minimum(coord_2d_data) - .2 * (ma.maximum(coord_2d_data) - ma.minimum(coord_2d_data))
if max_value == None:
max_value = ma.maximum(coord_2d_data)
if min_value == None:
min_value = nonmaskedmin
coord_2d_data = ma.filled(coord_2d_data,min_value)
if background is None:
value_range = max_value-min_value
background = min_value-value_range/100
coord_2d_data = ma.filled(ma.masked_array(coord_2d_data, mask=data_mask), background)
# Our data uses NW as 0,0, while matplotlib uses SW for 0,0.
# Rotate the data so the map is oriented correctly.
coord_2d_data = rot90(coord_2d_data, 1)
jet()
figure()
norm = normalize(min_value, max_value)
im = imshow(coord_2d_data,
origin='lower',
aspect='equal',
interpolation=None,
norm=norm,
)
tickfmt = '%4d'
if isinstance(min_value, float) or isinstance(max_value, float):
tickfmt='%1.4f'
colorbar(format=tickfmt)
title(my_title)
axis('off')
if file:
savefig(file)
close()
else:
show()
def plot_map(self,
dataset,
attribute_data,
min_value=None,
max_value=None,
file=None,
my_title="",
filter=None,
background=None):
""" Plots a 2D image of attribute given by 'name'. matplotlib required.
The dataset must have a method 'get_2d_attribute' defined that returns
a 2D array that is to be plotted. If min_value/max_value are given, all values
that are smaller/larger than these values are set to min_value/max_value.
Argument background is a value to be used for background. If it is not given,
it is considered as a 1/100 under the minimum value of the array.
Filter is a 2D array. Points where filter is > 0 are masked out (put into background).
"""
import matplotlib
matplotlib.use('Qt4Agg')
from matplotlib.pylab import jet, imshow, colorbar, show, axis, savefig, close, figure, title, normalize
from matplotlib.pylab import rot90
attribute_data = attribute_data[filter]
coord_2d_data = dataset.get_2d_attribute(attribute_data=attribute_data)
data_mask = coord_2d_data.mask
# if filter is not None:
# if isinstance(filter, ndarray):
# if not ma.allclose(filter.shape, coord_2d_data.shape):
# raise StandardError, "Argument filter must have the same shape as the 2d attribute."
# filter_data = filter
# else:
# raise TypeError, "The filter type is invalid. A character string or a 2D numpy array allowed."
# filter_data = where(ma.filled(filter_data,1) > 0, 1,0)
# data_mask = ma.mask_or(data_mask, filter_data)
nonmaskedmin = ma.minimum(coord_2d_data) - .2 * (
ma.maximum(coord_2d_data) - ma.minimum(coord_2d_data))
if max_value == None:
max_value = ma.maximum(coord_2d_data)
if min_value == None:
min_value = nonmaskedmin
coord_2d_data = ma.filled(coord_2d_data, min_value)
if background is None:
value_range = max_value - min_value
background = min_value - value_range / 100
coord_2d_data = ma.filled(
ma.masked_array(coord_2d_data, mask=data_mask), background)
# Our data uses NW as 0,0, while matplotlib uses SW for 0,0.
# Rotate the data so the map is oriented correctly.
coord_2d_data = rot90(coord_2d_data, 1)
jet()
figure()
norm = normalize(min_value, max_value)
im = imshow(
coord_2d_data,
origin='lower',
aspect='equal',
interpolation=None,
norm=norm,
)
tickfmt = '%4d'
if isinstance(min_value, float) or isinstance(max_value, float):
tickfmt = '%1.4f'
colorbar(format=tickfmt)
title(my_title)
axis('off')
if file:
savefig(file)
close()
else:
show()
#% Red Color for segment 3.
barColorMap[3,:] = np.array(np.hstack((.9, .9, .14)))
#% Yellow Color for segment 4.
#% I have not defined any more than 4 colors in this demo.
#% For any number of bars beyond 4, just make up random colors.
if numberOfBars > 4.:
barColorMap[4:numberOfBars,0:3.] = np.random.rand((numberOfBars-4.), 3.)
elif button == 2.:
#% Example of using colormap with random colors
barColorMap = np.random.rand(numberOfBars, 3.)
elif button == 3.:
#% Example of using pre-defined jet colormap
barColorMap = plt.jet(numberOfBars)
elif button == 4.:
#% Example of using pre-defined Hot colormap
barColorMap = plt.hot(numberOfBars)
else:
#% Example of using pre-defined lines colormap
barColorMap = lines(numberOfBars)
#% Plot each number one at a time, calling bar() for each y value.
for b in np.arange(1., (numberOfBars)+1):
#% Plot one single bar as a separate bar series.
#% Fancy up the graph.
a1=coeff[0,1]
phi=coeff[1,1]
a0[a0==0]=1
a0[np.isnan(a0)]=1
a1[np.isnan(a1)]=1
a0[np.isinf(a0)]=1
a1[np.isinf(a1)]=1
pp.figure(num=None, figsize=(10, 12.5), dpi=80, facecolor='w', edgecolor='k')
mean_int = a0.mean()
pp.subplot(3,2,1)
pp.imshow(a0, vmin=mean_int*0.65, vmax=mean_int*1.35, interpolation='none')
pp.colorbar()
pp.title('Flat Field Intensity')
pp.jet()
pp.grid()
pp.subplot(3,2,2)
pp.imshow(a1, interpolation='none',vmin=0,vmax=3000)
pp.colorbar()
pp.title('Flat Field Amplitude')
pp.grid()
pp.subplot(3,2,3)
pp.imshow(phi, vmax=np.pi, vmin=-np.pi, interpolation='none')
cbar=pp.colorbar()
cbar.set_ticks([-np.pi, -0.75*np.pi, -0.5*np.pi, -0.25*np.pi, 0, 0.25*np.pi, 0.5*np.pi, 0.75*np.pi, np.pi])
cbar.set_ticklabels(['-pi', '-pi3/4', '-pi/2', '-pi/4', '0 pi', 'pi/4', 'pi/2', 'pi3/4', 'pi'])
pp.title('Flat Field Phase')
pp.grid()
def msCompareResults(vol, sigma, shrink_factor=0.5, gui=True, edge_quantil=0.05):
start = time()
grad = msGaussianGradient(vol, sigma)
elapsed = time() - start
print "msGaussianGradient took %f seconds to finish" % elapsed
shp = vol.shape
weight = np.array(range(shp[2]))
for ch in range(shp[2]/2):
grad[...,ch,:] *= weight[ch]
start = time()
mean_g = msMeanGradient(grad)
elapsed = time() - start
print "msMeanGradient took %f seconds to finish" % elapsed
stddev_g = msVector2ImageMagnitude(np.std(grad,2))
start = time()
max_g = msMaxGradient(grad)
elapsed = time() - start
print "msMaxGradient took %f seconds to finish" % elapsed
start = time()
mv_g , semi_major_g, semi_minor_g = msMVGradient(grad)
elapsed = time() - start
print "msMVGradient took %f seconds to finish" % elapsed
eccentricity_g = np.sqrt(1-semi_minor_g**2/semi_major_g**2)
mean_el = np.array(vg.analysis.cannyEdgelList(vg.Vector2Image(mean_g),np.max(msVector2ImageMagnitude(mean_g))*edge_quantil))
max_el = np.array(vg.analysis.cannyEdgelList(vg.Vector2Image(max_g), np.max(msVector2ImageMagnitude(max_g))*edge_quantil))
mv_el = np.array(vg.analysis.cannyEdgelList(vg.Vector2Image(mv_g), np.max(msVector2ImageMagnitude(mv_g))*edge_quantil))
if (gui):
F_mag = pl.figure()
F_mag.clf()
pl.jet()
grid_mag = ImageGrid(F_mag, 111, \
nrows_ncols = (1, 3),\
direction="row",\
axes_pad = 0.05,\
add_all=True,\
label_mode = "1",\
share_all = True,\
cbar_location="right",\
cbar_mode="single",\
cbar_size="10%",\
cbar_pad=0.05)
max_v = np.max(msVector2ImageMagnitude(mv_g))
min_v = np.min(msVector2ImageMagnitude(mv_g))
import matplotlib.colors
norm = matplotlib.colors.normalize(vmax=max_v, vmin=min_v)
grid_mag[0].imshow(msVector2ImageMagnitude(mean_g),norm=norm)
pl.title("Mean Gradient Mag.")
#pl.figure()
#pl.subplot(132)
grid_mag[1].imshow(msVector2ImageMagnitude(max_g), norm=norm)
pl.title("Max Gradient Mag.")
#pl.figure()
#pl.subplot(133)
img_mag = grid_mag[2].imshow(msVector2ImageMagnitude(mv_g), norm=norm)
pl.title("MV Gradient Mag.")
grid_mag[2].cax.colorbar(img_mag)
print 'check'
F_ang = pl.figure()
F_ang.clf()
pl.hsv()
grid_ang = ImageGrid(F_ang, 111, \
nrows_ncols = (1, 3),\
direction="row",\
axes_pad = 0.05,\
add_all=True,\
label_mode = "1",\
share_all = True,\
cbar_location="right",\
cbar_mode="single",\
cbar_size="10%",\
cbar_pad=0.05)
norm = matplotlib.colors.normalize(vmax=180, vmin=-180)
grid_ang[0].imshow(msVector2ImageAngle(mean_g), norm=norm)
pl.title("Mean Gradient Angle")
grid_ang[1].imshow(msVector2ImageAngle(max_g), norm=norm)
pl.title("Max Gradient Angle")
img_ang = grid_ang[2].imshow(msVector2ImageAngle(mv_g), norm=norm)
pl.title("MV Gradient Angle")
grid_ang[2].cax.colorbar(img_ang)
F_edg = pl.figure()
F_edg.clf()
pl.hsv()
grid_edg = ImageGrid(F_edg, 111, \
nrows_ncols = (1, 3),\
direction="row",\
axes_pad = 0.05,\
add_all=True,\
label_mode = "1",\
share_all = True,\
cbar_location="right",\
cbar_mode="single",\
cbar_size="10%",\
cbar_pad=0.05)
grid_edg[0].axis([0, shp[0]-1, shp[1]-1, 0])
grid_edg[1].axis([0, shp[0]-1, shp[1]-1, 0])
grid_edg[2].axis([0, shp[0]-1, shp[1]-1, 0])
plotEdgelList(mean_el, grid_edg[0])
plotEdgelList(max_el, grid_edg[1])
plotEdgelList(mv_el, grid_edg[2])
#pl.figure()
#pl.subplot(131,aspect="equal")
#pl.axis([0, shp[0]-1, shp[1]-1, 0])
#plotEdgelList(mean_el)
#pl.title("Mean Gradient Edgel")
#pl.subplot(132,aspect="equal")
#pl.axis([0, shp[0]-1, shp[1]-1, 0])
#plotEdgelList(max_el)
#pl.title("Max Gradient Edgel")
#pl.subplot(133,aspect="equal")
#pl.axis([0, shp[0]-1, shp[1]-1, 0])
#plotEdgelList(mv_el)
#pl.title("MV Gradient Edgel")
## MV semi-major and semi-minor axis
pl.figure()
pl.subplot(121)
pl.imshow(semi_major_g)
pl.copper()
pl.colorbar(shrink=shrink_factor)
pl.title("Gradient MV semi-major axis")
pl.subplot(122)
pl.imshow(semi_minor_g)
pl.colorbar(shrink=shrink_factor)
pl.title("Gradient MV semi-minor axis")
## Std. dev of MS Gradients vs. Eccentricity of the MV eigenvals
pl.figure()
pl.subplot(121)
pl.imshow(stddev_g)
pl.copper()
pl.colorbar(shrink=shrink_factor)
pl.title("Gradient std. dev.")
pl.subplot(122)
pl.imshow(eccentricity_g)
pl.colorbar(shrink=shrink_factor)
pl.title("Gradient MV eccentricity")
pl.show()
def plotClassifier(self,
classifier,
dataType="train",
catInds=[0, 1, 2],
prefix="posterior"):
# データの選択
if dataType == "train":
cat1 = self.cat1_train
cat2 = self.cat2_train
cat3 = self.cat3_train
else:
cat1 = self.cat1_test
cat2 = self.cat2_test
cat3 = self.cat3_test
for catInd in catInds:
#fig = plt.figure()
# 学習データを描画
# カテゴリ1
x1, x2 = np.vstack(cat1).transpose()
plt.plot(x1,
x2,
'o',
color="#FFA500",
markeredgecolor='k',
markersize=14)
# カテゴリ2
x1, x2 = np.vstack(cat2).transpose()
plt.plot(x1,
x2,
's',
color="#FFFF00",
markeredgecolor='k',
markersize=14)
# カテゴリ3
x1, x2 = np.vstack(cat3).transpose()
plt.plot(x1,
x2,
'^',
color="#FF00FF",
markeredgecolor='k',
markersize=14)
# 識別境界を描画
N = len(cat1) + len(cat2) + len(cat3)
#メッシュの作成
X1, X2 = plt.meshgrid(plt.linspace(-6, 6, 50),
plt.linspace(-6, 6, 50))
width, height = X1.shape
X1.resize(X1.size)
X2.resize(X2.size)
Z = np.array([
classifier.predict(np.array([[x1], [x2]]))[catInd]
for (x1, x2) in zip(X1, X2)
])
X1.resize((width, height))
X2.resize((width, height))
Z.resize((width, height))
# contourプロット
levels = [x / 10.0 for x in np.arange(0, 11, 1)]
CS = plt.contourf(X1, X2, Z, levels)
# contourの数値ラベル
plt.clabel(CS,
colors='black',
inline=True,
inline_spacing=0,
fontsize=14)
# contourのカラーバー
CB = plt.colorbar(CS)
CB.set_ticks(levels)
CB.ax.tick_params(labelsize=14)
for line in CB.lines:
line.set_linewidth(20)
# 色空間の設定
plt.jet()
# plotラベルの設定
plt.title("p(y={}|x)".format(catInd + 1), fontsize=14)
plt.xlabel("x1", fontsize=14)
plt.ylabel("x2", fontsize=14)
plt.tick_params(labelsize=14)
# 表示範囲の設定
plt.xlim(-6, 6)
plt.ylim(-6, 6)
# レジェンド
plt.legend(["category 1", "category 2", "category 3"])
# 画像として保存
fullpath = os.path.join(self.visualPath,
"{}_{}.png".format(prefix, catInd + 1))
plt.savefig(fullpath)
plt.close()
#% save q and chi data files
chi0name = np.array(np.hstack((dumpname, '\\', imgname, 'chi0.mat')))
q0name = np.array(np.hstack((dumpname, '\\', imgname, 'Q0.mat')))
plt.save(q0name, 'Q0')
plt.save(chi0name, 'chi0')
#%% Normalize and save qchi with all phis
Qchi0_allphi = matdiv(Qchi0_allphi, numphi)
#% normalize the combined-phi image
for n in np.arange(1., (lchi0int)+1):
#% make a log plot
#% Plot qchi
H = imagesc(Q0, chi0, Qchi0log_allphi)
plt.xlabel('Q (A^-1)')
plt.ylabel('chi (deg)')
plt.colorbar
colormap(plt.jet(256.))
#%xlim([2 8]) % these values specific to your data; CHECK!!!
#%ylim([-80 30]) % these values specific to your data; CHECK!!!
caxis(np.array(np.hstack((0.5, 4.))))
#% these values specific to your data; CHECK!!!
savepath = np.array(np.hstack((dumpname, '\\', imgname, 'allphi_cropped.tif')))
saveas(H, savepath)
#% Save qchi data
qx0phiname = np.array(np.hstack((dumpname, '\\', imgname, 'qx0_allphi.mat')))
qx0logphiname = np.array(np.hstack((dumpname, '\\', imgname, 'qx0log_allphi.mat')))
plt.save(qx0phiname, 'Qchi0_allphi')
plt.save(qx0logphiname, 'Qchi0log_allphi')
toc
#% end timer