def plot_scaling_result(template: ImageStack, scaled: ImageStack):
f, (before, after) = plt.subplots(ncols=4, nrows=2)
for channel, ax in enumerate(before):
title = f'Before scaling\nChannel {channel}'
intensity_histogram(
template,
sel={
Axes.CH: channel,
Axes.ROUND: 0
},
ax=ax,
title=title,
log=True,
bins=50,
)
ax.set_xlim(0, 0.007)
for channel, ax in enumerate(after):
title = f'After scaling\nChannel {channel}'
intensity_histogram(
scaled,
sel={
Axes.CH: channel,
Axes.ROUND: 0
},
ax=ax,
title=title,
log=True,
bins=50,
)
f.tight_layout()
return f
def plot_intensity_histograms(stack: starfish.ImageStack, r: int, title: str):
fig = plt.figure(dpi=150)
ax1 = fig.add_subplot(131, title='ch: 0')
ax2 = fig.add_subplot(132, title='ch: 1', sharex=ax1, sharey=ax1)
ax3 = fig.add_subplot(133, title='ch: 2', sharex=ax1, sharey=ax1)
intensity_histogram(stack,
sel={
Axes.ROUND: r,
Axes.CH: 0
},
log=True,
bins=50,
ax=ax1)
intensity_histogram(stack,
sel={
Axes.ROUND: r,
Axes.CH: 1
},
log=True,
bins=50,
ax=ax2)
intensity_histogram(stack,
sel={
Axes.ROUND: r,
Axes.CH: 2
},
log=True,
bins=50,
ax=ax3)
fig.tight_layout()
fig.suptitle(title)
def plot_intensity_histograms(ref: starfish.ImageStack,
scaled_cr: starfish.ImageStack,
scaled_c: starfish.ImageStack,
scaled_r: starfish.ImageStack, r: int):
fig = plt.figure()
ax10 = fig.add_subplot(4, 3, 10)
intensity_histogram(scaled_cr,
sel={
Axes.ROUND: r,
Axes.CH: 0
},
log=True,
bins=50,
ax=ax10)
ax10.set_ylabel('ch and r', rotation=90, size='large')
ax11 = fig.add_subplot(4, 3, 11, sharex=ax10)
intensity_histogram(scaled_cr,
sel={
Axes.ROUND: r,
Axes.CH: 1
},
log=True,
bins=50,
ax=ax11)
ax12 = fig.add_subplot(4, 3, 12, sharex=ax10)
intensity_histogram(scaled_cr,
sel={
Axes.ROUND: r,
Axes.CH: 2
},
log=True,
bins=50,
ax=ax12)
ax1 = fig.add_subplot(4, 3, 1, sharex=ax10)
intensity_histogram(ref,
sel={
Axes.ROUND: r,
Axes.CH: 0
},
log=True,
bins=50,
ax=ax1)
ax1.set_title('ch: 0')
ax1.set_ylabel('unscaled', rotation=90, size='large')
ax2 = fig.add_subplot(4, 3, 2, sharex=ax10)
intensity_histogram(ref,
sel={
Axes.ROUND: r,
Axes.CH: 1
},
log=True,
bins=50,
ax=ax2)
ax2.set_title('ch: 1')
ax3 = fig.add_subplot(4, 3, 3, sharex=ax10)
intensity_histogram(ref,
sel={
Axes.ROUND: r,
Axes.CH: 2
},
log=True,
bins=50,
ax=ax3)
ax3.set_title('ch: 2')
ax4 = fig.add_subplot(4, 3, 4, sharex=ax10)
intensity_histogram(scaled_c,
sel={
Axes.ROUND: r,
Axes.CH: 0
},
log=True,
bins=50,
ax=ax4)
ax4.set_ylabel('ch', rotation=90, size='large')
ax5 = fig.add_subplot(4, 3, 5, sharex=ax10)
intensity_histogram(scaled_c,
sel={
Axes.ROUND: r,
Axes.CH: 1
},
log=True,
bins=50,
ax=ax5)
ax6 = fig.add_subplot(4, 3, 6, sharex=ax10)
intensity_histogram(scaled_c,
sel={
Axes.ROUND: r,
Axes.CH: 2
},
log=True,
bins=50,
ax=ax6)
ax7 = fig.add_subplot(4, 3, 7, sharex=ax10)
intensity_histogram(scaled_r,
sel={
Axes.ROUND: r,
Axes.CH: 0
},
log=True,
bins=50,
ax=ax7)
ax7.set_ylabel('r', rotation=90, size='large')
ax8 = fig.add_subplot(4, 3, 8, sharex=ax10)
intensity_histogram(scaled_r,
sel={
Axes.ROUND: r,
Axes.CH: 1
},
log=True,
bins=50,
ax=ax8)
ax9 = fig.add_subplot(4, 3, 9, sharex=ax10)
intensity_histogram(scaled_r,
sel={
Axes.ROUND: r,
Axes.CH: 2
},
log=True,
bins=50,
ax=ax9)
fig.tight_layout()
# the previous example. The intensity histograms for ``Axes.ZPLANE: 2`` and ``Axes.ZPLANE: 8``
# are plotted before and after running :py:class:`.MatchHistograms`.
#
# ``Axes.ZPLANE: 2`` contains no spots or peaks so the histogram is that of Gaussian noise
#
# ``Axes.ZPLANE: 8`` has many spots and the histogram shows a long tail of high pixel values
# Plot intensity distributions of z-planes from z: 2 and z: 8 before and after scaling
import matplotlib
import matplotlib.pyplot as plt
from starfish.util.plot import intensity_histogram, imshow_plane
matplotlib.rcParams["figure.dpi"] = 150
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
f.suptitle('Intensity Histograms')
intensity_histogram(bs_stack, sel={Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 2}, log=True, bins=50, ax=ax1,
title='Unscaled\nz: 2')
intensity_histogram(bs_stack, sel={Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 8}, log=True, bins=50, ax=ax2,
title='Unscaled\nz: 8')
intensity_histogram(scaled_z, sel={Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 2}, log=True, bins=50, ax=ax3,
title='Scaled\nz: 2')
intensity_histogram(scaled_z, sel={Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 8}, log=True, bins=50, ax=ax4,
title='Scaled\nz: 8')
f.tight_layout()
####################################################################################################
# As expected, the distributions after scaling are made more similar. The higher values in the
# Gaussian noise are shifted higher while the long tail representing high spot intensities is
# reduced. Overall the SNR decreased. This does not mean it is never appropriate to
# :py:class:`.MatchHistograms` across :py:class:`.Axes.ZPLANE` but any use of
# :py:class:`.MatchHistograms` should be done so with caution.
print(stack)
# Plot
import matplotlib
import matplotlib.pyplot as plt
from starfish.util.plot import intensity_histogram
matplotlib.rcParams["figure.dpi"] = 150
f, (ax1, ax2) = plt.subplots(ncols=2)
f.suptitle('Intensity Histogram')
# Plot intensity distribution of entire as a histogram with 50 bins
intensity_histogram(stack,
sel={
Axes.ROUND: 0,
Axes.CH: 1
},
log=True,
bins=50,
ax=ax1,
title='Full Image')
# Plot intensity distribution of 200x200 pixel ROI with 10 bins
intensity_histogram(stack,
sel={
Axes.ROUND: 0,
Axes.CH: 1,
Axes.X: (700, 900),
Axes.Y: (100, 300),
Axes.ZPLANE: 0
},
log=True,
