def flush_inline_matplotlib_plots():
"""
Flush matplotlib plots immediately, rather than asynchronously.
Basically, the inline backend only shows the plot after the entire
cell executes, which means we can't easily use a contextmanager to
suppress displaying it. See https://github.com/jupyter-widgets/ipywidgets/issues/1181/
and https://github.com/ipython/ipython/issues/10376 for more details. This
function displays flushes any pending matplotlib plots if we are using
the inline backend.
Stolen from https://github.com/jupyter-widgets/ipywidgets/blob/4cc15e66d5e9e69dac8fc20d1eb1d7db825d7aa2/ipywidgets/widgets/interaction.py#L35
"""
if 'matplotlib' not in sys.modules:
# matplotlib hasn't been imported, nothing to do.
return
try:
import matplotlib as mpl
from ipykernel.pylab.backend_inline import flush_figures
except ImportError:
return
# except KeyError:
# return
if mpl.get_backend() == 'module://ipykernel.pylab.backend_inline':
flush_figures()
def drawGraph(self, sqlX, sqlY, sparqlX, sparqlY, kind='bar', figsize=(12, 6)):
fig, axes = plt.pyplot.subplots(1, 2, figsize=figsize)
self.sqlResult[sqlY].value_counts().plot(ax=axes[0], kind=kind)
self.sparqlResult[sparqlY].value_counts().plot(ax=axes[1], kind=kind)
axes[0].set_title("SQL Result Graph: " + sqlY)
axes[1].set_title("SPARQL Result Graph: " + sparqlY)
flush_figures()
def draw_plot_with_oryginal(image, oryg_image, name="image.png"):
fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(10, 10))
plt.tight_layout()
ax.imshow(image, cmap=plt.cm.gray)
ax2.imshow(oryg_image, cmap=plt.cm.gray)
plt.show()
#plt.savefig(name)
flush_figures()
def flush_all():
"""Flushes stdout/stderr/matplotlib."""
sys.stdout.flush()
sys.stderr.flush()
# pylint: disable=g-import-not-at-top
try:
from ipykernel.pylab.backend_inline import flush_figures
except ImportError:
# older ipython
from IPython.kernel.zmq.pylab.backend_inline import flush_figures
flush_figures()
def show_inline_matplotlib_plots():
"""Show matplotlib plots immediately if using the inline backend.
With ipywidgets 6.0, matplotlib plots don't work well with interact when
using the inline backend that comes with ipykernel. Basically, the inline
backend only shows the plot after the entire cell executes, which does not
play well with drawing plots inside of an interact function. See
https://github.com/jupyter-widgets/ipywidgets/issues/1181/ and
https://github.com/ipython/ipython/issues/10376 for more details. This
function displays any matplotlib plots if the backend is the inline backend.
"""
if 'matplotlib' not in sys.modules:
# matplotlib hasn't been imported, nothing to do.
return
try:
import matplotlib as mpl
from ipykernel.pylab.backend_inline import flush_figures
except ImportError:
return
if mpl.get_backend() == 'module://ipykernel.pylab.backend_inline':
flush_figures()
def show_inline_matplotlib_plots():
"""Show matplotlib plots immediately if using the inline backend.
With ipywidgets 6.0, matplotlib plots don't work well with interact when
using the inline backend that comes with ipykernel. Basically, the inline
backend only shows the plot after the entire cell executes, which does not
play well with drawing plots inside of an interact function. See
https://github.com/jupyter-widgets/ipywidgets/issues/1181/ and
https://github.com/ipython/ipython/issues/10376 for more details. This
function displays any matplotlib plots if the backend is the inline backend.
"""
if 'matplotlib' not in sys.modules:
# matplotlib hasn't been imported, nothing to do.
return
try:
import matplotlib as mpl
from ipykernel.pylab.backend_inline import flush_figures
except ImportError:
return
if mpl.get_backend() == 'module://ipykernel.pylab.backend_inline':
flush_figures()
def colorThreshold(image, t):
processed = (image > t) * 1
flush_figures()
return processed
def thresh(t, photo):
warnings.simplefilter("ignore")
binary = (photo > t) * 255
binary = np.uint8(binary)
flush_figures()
return binary
def drawPlot(image, oryg_image, name="image.png"):
fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(10, 10))
ax.imshow(image, cmap=plt.cm.gray)
ax2.imshow(oryg_image, cmap=plt.cm.gray)
plt.savefig(name)
flush_figures()
def upd(patient_id, n_slice, nods):
img = batch.get(patient_id, 'images')[n_slice]
mask = batch.get(patient_id, 'masks')[n_slice]
nonlocal predict
if predict is None:
rows, cols = 1, len(select)
pred = np.zeros_like(img)
fig, axes = plt.subplots(rows,
cols,
squeeze=False,
figsize=(size1, size2))
else:
rows = np.ceil(len(select) / 3).astype(np.int)
cols = 3 if rows > 1 else len(select)
fig, axes = plt.subplots(rows,
cols,
squeeze=False,
figsize=(size1, size2))
# where fun begins :D
if 4 or 5 or 6 in select:
pred = predict[indices.index(patient_id), n_slice, ...]
if grid:
inv_spacing = 1 / batch.get(patient_id, 'spacing').reshape(-1)[1:]
step_mult = 10
xticks = np.arange(0, img.shape[0], step_mult * inv_spacing[0])
yticks = np.arange(0, img.shape[1], step_mult * inv_spacing[1])
all_plots = {
1: {
'args': (img, plt.cm.bone)
},
2: {
'args': (mask, plt.cm.bone)
},
3: {
'args': (mask * img, plt.cm.bone)
},
4: {
'args': (img + mask * 300, plt.cm.seismic)
},
5: {
'args': (pred, plt.cm.bone)
},
6: {
'args': (pred * img, plt.cm.bone)
},
7: {
'args': (img + pred * 300, plt.cm.seismic)
}
}
i = 0
for r in range(rows):
for c in range(cols):
axes[r][c].imshow(*all_plots[select[i]]['args'])
axes[r][c].set_xticks(xticks, minor=True)
axes[r][c].set_yticks(yticks, minor=True)
axes[r][c].grid(color='r',
linewidth=1.5,
alpha=0.15,
which='minor')
i += 1
if i == len(select):
break
fig.subplots_adjust(left=None,
bottom=0.1,
right=None,
top=0.4,
wspace=0.1,
hspace=0.1)
flush_figures()
