def to_mime_and_metadata(obj) -> (dict, dict): # noqa: C901
if isinstance(obj, bytes):
obj = base64.b64encode(obj).decode("utf-8")
return {"text/html": to_html(obj)}, {}
elif isinstance(obj, str) and obj.startswith("http"):
if re.match(r".*\.(gif|jpg|svg|jpeg||png)$", obj, re.I):
try:
return Image(obj, embed=True)._repr_mimebundle_()
except TypeError:
pass
return {"text/html": to_html(obj)}, {}
elif isinstance(obj, str) and len(obj) < 1024 and os.path.exists(obj):
if re.match(r".*\.(gif|jpg|svg|jpeg||png)$", obj, re.I):
try:
return Image(obj, embed=True)._repr_mimebundle_()
except TypeError:
pass
return {"text/html": to_html(obj)}, {}
elif hasattr(obj, "_repr_mimebundle_"):
obj.embed = True
return obj._repr_mimebundle_()
elif hasattr(obj, "_repr_json_"):
obj.embed = True
return {"application/json": obj._repr_json_()}, {}
elif hasattr(obj, "_repr_html_"):
obj.embed = True
return {"text/html": obj._repr_html_()}, {}
elif hasattr(obj, "_repr_png_"):
return {"image/png": obj._repr_png_()}, {}
elif hasattr(obj, "_repr_jpeg_"):
return {"image/jpeg": obj._repr_jpeg_()}, {}
elif hasattr(obj, "_repr_svg_"):
return {"image/svg": obj._repr_svg_()}, {}
try:
if isinstance(obj, str):
return {
"text/html": f"<pre>{to_html(obj)}</pre>".replace("\\n", "\n")
}, {}
else:
data, metadata = JSON(data=obj, expanded=True)._repr_json_()
return (
{
"application/json": data,
"text/html": f"<pre>{to_html(obj)}</pre>"
},
metadata,
)
except (TypeError, JSONDecodeError):
pass
try:
return {"text/html": to_html(obj)}, {}
except TypeError:
return {}, {}
def get_jupyter(self):
from IPython.core.display import HTML, Image, display
for b, result in self.result.items():
error = self.result[b]['else'] if 'else' in self.result[b] else None
if error:
display(HTML(error))
else:
# Show table
display(HTML(self.result[b]["Importance"]["table"]))
# Show plots
display(*list([Image(filename=d["figure"]) for d in self.result[b]['Marginals'].values()]))
display(*list([Image(filename=d["figure"]) for d in self.result[b]['Pairwise Marginals'].values()]))
def dog_breed_algorithm(img_path):
if dog_detector(img_path):
print('Hello, dog!')
display(Image(img_path, width=200, height=200))
print('Your predicted breed is ...\n %s' %
Xception_predict_breed(img_path))
elif face_detector(img_path):
print('Hello, human!')
display(Image(img_path, width=200, height=200))
print('You looks like a ...\n %s' % Xception_predict_breed(img_path))
else:
display(Image(img_path, width=200, height=200))
print('Could not identify a human or a dog in this image!')
def dog_breed_algorithm(img_path):
if dog_detector(img_path) == 1:
print("hello, dog!")
display(Image(img_path,width=200,height=200))
print("Your predicted breed is ... ")
return print(Xception_predict_breed(img_path))
elif face_detector(img_path) == 1:
print("hello, human!")
display(Image(img_path,width=200,height=200))
print("You look like a ... ")
return print(Xception_predict_breed(img_path))
else:
display(Image(img_path,width=200,height=200))
return print("Could not identify a human or dog in the chosen image. Please try again.")
def mapStations(stationList, zoom=8):
"""Return a google static map showing the location of flow and level stations.
Globals:
STATIONS: Pandas dataframe with HYDAT station data.
Args:
stationList (str): List of station codes.
zoom (int): zoom level of the resulting map.
Returns:
Image object containing the static map. Stations are displayed as:
red: flow station
green: level station
yellow: flow and level
"""
locs = ["{0},{1}".format(STATIONS.loc[s,'LATITUDE'], STATIONS.loc[s,'LONGITUDE']) \
for s in stationList]
flows = [s for s in stationList if STATIONS.loc[s, 'Flow'] == True]
levels = [s for s in stationList if STATIONS.loc[s, 'Level'] == True]
bSet = set(levels).intersection(set(flows))
google_maps_url = \
"https://maps.googleapis.com/maps/api/staticmap?" + \
f"key={google_api_key}" + \
"&size=640x400" + \
f"&zoom={zoom}" + \
"&maptype=terrain" + \
"&markers=color:red%7Csize:mid%7Clabel:F%7C" + "|".join(["{0},{1}".format(
STATIONS.loc[s,'LATITUDE'], STATIONS.loc[s,'LONGITUDE']) for s in set(flows).difference(bSet)]) + \
"&markers=color:green%7Csize:mid%7Clabel:L%7C" +"|".join(["{0},{1}".format(
STATIONS.loc[s,'LATITUDE'], STATIONS.loc[s,'LONGITUDE']) for s in set(levels).difference(bSet)]) + \
"&markers=color:yellow%7Csize:mid%7Clabel:B%7C" + "|".join(["{0},{1}".format(
STATIONS.loc[s,'LATITUDE'], STATIONS.loc[s,'LONGITUDE']) for s in bSet])
return Image(requests.get(google_maps_url).content)
def query_content(Limit, Medium, search_param):
all_objects = [content for content in Content.query.all() if content.medium.name == Medium if content.search_param == search_param]
if len(all_objects) < 1:
print('No Content')
else:
if Medium == 'text':
for i in range (0, Limit):
title = all_objects[i].title
link = all_objects[i].content_url
image = all_objects[i].image_url
source_name = all_objects[i].provider.provider_name
display(HTML("<a href="+link+">"+source_name+': '+title+"</a>"))
display(Image(url = image))
elif Medium == 'video':
for i in range (0, Limit):
title = all_objects[i].title
link = all_objects[i].content_url
link = link[-11:]
source_name = all_objects[i].provider.provider_name
display(HTML("<a href="+link+">"+source_name+': '+title+"</a>"))
display(HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/'+link+'?rel=0&controls=0&showinfo=0" frameborder="0" allowfullscreen></iframe>'))
else:
for i in range (0, Limit):
link = all_objects[i].content_url
display(HTML("<iframe src="+"'"+link+"'"+ "style='width:100%; height:100px;' scrolling='no' frameborder='no'></iframe>"))
def view_image(i):
image = images[i]
try:
display(Image(filename=image))
print(image)
except:
print('No image found!')
def test2():
graph = {
# 1. Layer
'X': ['A', 'B'],
# 2. Layer
'A': ['AA', 'AB'],
'B': ['BA', 'BB'],
# 3. Layer
'AA': ['AAA', 'AAB'],
'AB': ['ABA', 'ABB'],
'BA': ['BAA', 'BAB'],
'BB': ['BBA', 'BBB'],
# 4. Layer
'AAA': 10,
'AAB': 8,
'ABA': 7,
'ABB': 12,
'BAA': 9,
'BAB': 6,
'BBA': 4,
'BBB': 17,
}
desc = alpha_beta(graph)
print_pretty(desc)
digraph = build_viz(graph.keys(), graph, desc, branching=2)
digraph.render(filename='img/graph')
display(Image(filename='img/graph.png'))
def image(self, filename="snapshot.png", group="all", color="type", diameter="type",
size=None, view=None, center=None, up=None, zoom=1.0):
cmd_args = [group, "image", filename, color, diameter]
if size:
width = size[0]
height = size[1]
cmd_args += ["size", width, height]
if view:
theta = view[0]
phi = view[1]
cmd_args += ["view", theta, phi]
if center:
flag = center[0]
Cx = center[1]
Cy = center[2]
Cz = center[3]
cmd_args += ["center", flag, Cx, Cy, Cz]
if up:
Ux = up[0]
Uy = up[1]
Uz = up[2]
cmd_args += ["up", Ux, Uy, Uz]
if zoom:
cmd_args += ["zoom", zoom]
cmd_args.append("modify backcolor white")
self.write_dump(*cmd_args)
from IPython.core.display import Image
return Image('snapshot.png')
def jupyter_draw_nltk_tree(tree: Tree = None,
directory: str = '/tmp',
f_name: str = 'tmp',
show_tree: bool = False):
f_name = Path(directory) / f_name
f_name.parent.mkdir(exist_ok=True, parents=True)
cf = CanvasFrame()
tc = TreeWidget(cf.canvas(), tree)
tc['node_font'] = 'arial 13 bold'
tc['leaf_font'] = 'arial 14'
tc['node_color'] = '#005990'
tc['leaf_color'] = '#3F8F57'
tc['line_color'] = '#175252'
cf.add_widget(tc, 20, 20)
ps_file_name = f_name.with_suffix('.ps').as_posix()
cf.print_to_file(ps_file_name)
cf.destroy()
png_file_name = f_name.with_suffix(".png").as_posix()
system(f'convert {ps_file_name} {png_file_name}')
if show_tree:
display((Image(filename=png_file_name), ))
system(f'rm {f_name}.ps')
return png_file_name
def drawbubble(self, fn='bubble.png', width=1000, height=1000):
self.grdevices.png(fn, width=width, height=height)
robjects.r(
'''plot(ruleset, method="grouped",control=list(col = rev(brewer.pal(9, "Greens")[4:9])),shading ="lift")'''
)
self.grdevices.dev_off()
return Image(filename=fn)
def drawnetwork(self, fn='network.png', width=800, height=800):
self.grdevices.png(fn, width=width, height=height)
robjects.r(
'''plot(ruleset,measure="confidence",method="graph",control=list(type="items"),shading ="lift")'''
)
self.grdevices.dev_off()
return Image(filename=fn)
def latex2image(latex, density, export_file=None):
"""Compile LaTeX to PDF, and convert to PNG."""
temp_dir = tempfile.mkdtemp()
try:
# make a temp directory, and name temp files
temp_tex = os.path.join(temp_dir, 'tikzfile.tex')
temp_pdf = os.path.join(temp_dir, 'tikzfile.pdf')
temp_png = os.path.join(temp_dir, 'tikzfile.png')
with open(temp_tex, 'w') as out:
out.write(latex)
# run LaTeX to generate a PDF
sh_latex(in_file=temp_tex, out_dir=temp_dir)
if not os.path.isfile(temp_pdf):
raise Exception('pdflatex did not produce a PDF file.')
if export_file:
shutil.copyfile(temp_pdf, export_file)
# convert PDF to PNG
sh_convert(in_file=temp_pdf, out_file=temp_png, density=density)
# The Image(data=png) thing doesn't work..?
# png = b64encode(open(temp_png, "rb").read())
# return Image(data=png)
return Image(filename=temp_png)
finally:
# remove temp directory
shutil.rmtree(temp_dir)
def drawscatter(self, fn='scatter.png', width=1000, height=600):
self.grdevices.png(fn, width=width, height=height)
robjects.r(
'''plot(ruleset, control=list(jitter=2,col=rev(brewer.pal(9,"Greens")[4:9])), shading = "lift")'''
)
self.grdevices.dev_off()
return Image(filename=fn)
def draw_tree(classifier, feature_names):
dot_buf = StringIO.StringIO()
export_graphviz(classifier, out_file=dot_buf, feature_names=feature_names
) # classifier: clf_model, feature_names: df.columns
graph = pydot.graph_from_dot_data(dot_buf.getvalue())[0]
image = graph.create_png()
return Image(image)
def latex2image(latex, density, export_file=None, debug=False):
'''Compile LaTeX to PDF, and convert to PNG.'''
try:
# make a temp directory, and name temp files
temp_dir = tempfile.mkdtemp()
temp_tex = join(temp_dir, 'tikzfile.tex')
temp_pdf = join(temp_dir, 'tikzfile.pdf')
temp_png = join(temp_dir, 'tikzfile.png')
with open(temp_tex, 'w') as t:
t.write(latex)
# run LaTeX to generate a PDF
sh_latex(in_file=temp_tex, out_dir=temp_dir, debug=debug)
if not isfile(temp_pdf):
raise Exception("xelatex didn't produce a PDF file.")
if export_file:
shutil.copyfile(temp_pdf, export_file)
# convert PDF to PNG
sh_convert(in_file=temp_pdf,
out_file=temp_png,
density=density,
debug=debug)
return Image(data=open(temp_png, "rb").read())
finally:
# remove temp directory
shutil.rmtree(temp_dir)
def imshow(img):
"""Displays the image in a Jupyter Notebook Notebook.
You can only call this if you're already inside a Jupyter Notebook.
"""
if "JPY_PARENT_PID" not in os.environ:
raise RuntimeError(
"_stbt.logging.imshow can only be run inside a Jupyter Notebook")
from IPython.core.display import Image, display # pylint:disable=import-error
if isinstance(img, basestring):
display(Image(img))
else:
import cv2
_, data = cv2.imencode(".png", img)
display(Image(data=bytes(data.data), format="png"))
def latex2image(latex, density, export_file=None):
'''Compile LaTeX to PDF, and convert to PNG.'''
try:
# make a temp directory, and name temp files
temp_dir = tempfile.mkdtemp()
temp_tex = temp_dir + '/tikzfile.tex'
temp_pdf = temp_dir + '/tikzfile.pdf'
temp_png = temp_dir + '/tikzfile.png'
open(temp_tex, 'w').write(latex)
# run LaTeX to generate a PDF
sh_latex(in_file=temp_tex, out_dir=temp_dir)
if not isfile(temp_pdf):
raise Exception('pdflatex did not produce a PDF file.')
if export_file:
shutil.copyfile(temp_pdf, export_file)
# convert PDF to PNG
sh_convert(in_file=temp_pdf, out_file=temp_png, density=density)
return Image(temp_png)
finally:
# remove temp directory
shutil.rmtree(temp_dir)
def showdocs(uid, title=""):
if uid not in DOCS:
display(Markdown("No documentation found for `%s`"%uid))
return
d = DOCS[uid]
if len(title):
title += " "
display(Markdown("# %s%s" % (title, d['name'])))
display(Markdown(d['desc']))
if 'fn' in d:
# display the figures.
files = Path(BASEDIR, 'figures').glob("%s.png" % d['fn'])
for f in sorted(files,key=lambda x:x.name):
#print(f.name)
display(Image(filename=f))
# citations
md = []
if 'refs' in d and len(d['refs']):
md += ["# References"]
md += ["\n".join("+ %s" % z for z in d['refs'])]
display(Markdown("\n\n\n\n".join(md)))
def ggplot_notebook(gg, width=800, height=600):
# adapted from: http://stackoverflow.com/a/15343186
fn = os.path.join(tempfile.gettempdir(),
'{uuid}.png'.format(uuid=uuid.uuid4()))
grdevices.png(fn, width=width, height=height)
gg.plot()
grdevices.dev_off()
return Image(filename=fn)
def _(G: AnalysisGraph, *args, **kwargs):
""" Visualize the analysis graph in a Jupyter notebook cell. """
return Image(
to_agraph(G, *args, **kwargs).draw(format="png",
prog=kwargs.get("prog", "dot")),
retina=True,
)
def draw_decision_tree(self, classifier):
dot_buf = StringIO()
export_graphviz(classifier,
out_file=dot_buf,
feature_names=self.train_X.columns)
graph = pydot.graph_from_dot_data(dot_buf.getvalue())[0]
image = graph.create_png()
return Image(image)
def to_mime_and_metadata(obj) -> (dict, dict): # noqa: C901
if isinstance(obj, bytes):
obj = base64.b64encode(obj).decode('utf-8')
return {'text/html': to_html(obj)}, {}
elif isinstance(obj, str) and obj.startswith('http'):
if re.match(r'.*\.(gif|jpg|svg|jpeg||png)$', obj, re.I):
try:
return Image(obj, embed=True)._repr_mimebundle_()
except TypeError:
pass
return {'text/html': to_html(obj)}, {}
elif isinstance(obj, str) and len(obj) < 1024 and os.path.exists(obj):
if re.match(r'.*\.(gif|jpg|svg|jpeg||png)$', obj, re.I):
try:
return Image(obj, embed=True)._repr_mimebundle_()
except TypeError:
pass
return {'text/html': to_html(obj)}, {}
elif hasattr(obj, '_repr_mimebundle_'):
obj.embed = True
return obj._repr_mimebundle_()
elif hasattr(obj, '_repr_json_'):
obj.embed = True
return {'application/json': obj._repr_json_()}, {}
elif hasattr(obj, '_repr_html_'):
obj.embed = True
return {'text/html': obj._repr_html_()}, {}
elif hasattr(obj, '_repr_png_'):
return {'image/png': obj._repr_png_()}, {}
elif hasattr(obj, '_repr_jpeg_'):
return {'image/jpeg': obj._repr_jpeg_()}, {}
elif hasattr(obj, '_repr_svg_'):
return {'image/svg': obj._repr_svg_()}, {}
try:
data, metadata = JSON(data=obj, expanded=True)._repr_json_()
return {
'application/json': data,
'text/html': f'<pre>{to_html(obj)}</pre>',
}, metadata
except (TypeError, JSONDecodeError):
pass
try:
return {'text/html': to_html(obj)}, {}
except TypeError:
return {}, {}
def predict_label(img_path):
from IPython.core.display import Image, display
if dog_detector(img_path):
print('\nhello, dog')
display(Image(img_path,width=200,height=200))
print("Your predicted breed is ... ")
dog_name = Xception_predict_breed(img_path)
return print(dog_name)
elif face_detector(img_path):
print('\nhello, human')
display(Image(img_path,width=200,height=200))
dog_name = Xception_predict_breed(img_path)
print("You look like a ... ")
return print(dog_name)
else:
print('\n')
display(Image(img_path,width=200,height=200))
return print("Sorry! Could not identify a human or dog in the chosen image. Please try again.")
def svg(self, width=6, height=4):
""" Build an Ipython "Image" (requires iPython). """
with grdevices.render_to_bytesio(grdevices.svg,
width=width,
height=height) as b:
robjects.r("print")(self)
data = b.getvalue()
ip_img = Image(data=data, format='svg', embed=False)
return ip_img
def show_images(images, path='result.png', rows=8):
save_image(images,
path,
nrow=rows,
padding=1,
pad_value=1.0,
scale_each=False,
normalize=False)
display(Image(path))
def get_plot_as_image(self):
import matplotlib.pyplot as plt
from IPython.core.display import Image
with io.BytesIO() as fobj:
self.call_plotting_func()
plt.savefig(fobj, format="png")
plt.close()
return Image(data=fobj.getvalue(), format="png")
def show_example(fpath="data/static/DraughtPlot_example.gif"):
"""
Displays a .gif demonstrating how to use the tool.
Parameters:
-----------
fpath : str, default="data/static/DraughtPlot_example.gif"
Location of example .gif to show.
"""
return Image(filename=fpath)
def image_png(gg, width=800, height=400):
with grdevices.render_to_bytesio(grdevices.png,
type="cairo-png",
width=width,
height=height,
antialias="subpixel") as b:
robjects.r("print")(gg)
data = b.getvalue()
ip_img = Image(data=data, format='png', embed=True)
return ip_img
def show_flowers():
print('Iris virginica')
display(
Image(
url=
'https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/Iris_virginica.jpg/1024px-Iris_virginica.jpg'
))
print('Iris setosa')
display(
Image(
url=
'https://upload.wikimedia.org/wikipedia/commons/thumb/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg/450px-Kosaciec_szczecinkowaty_Iris_setosa.jpg'
))
print('Iris versicolor')
display(
Image(
url=
'https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Iris_versicolor_3.jpg/800px-Iris_versicolor_3.jpg'
))
