def add_logo_on_map(imagepath, ax, position, zoom, zorder):
logo2plot = read_png(imagepath)
imagebox = OffsetImage(logo2plot, zoom=zoom)
# coordinates to position this image
ab = AnnotationBbox(imagebox, position, xybox=(0.0, 0.0), xycoords="data", pad=0.0, boxcoords="offset points")
ab.zorder = zorder
ax.add_artist(ab)
def draw_mologram(self,position,turned): if turned: imagebox = OffsetImage(self.mologram_turned, zoom=.05) else: imagebox = OffsetImage(self.mologram, zoom=.05) ab = AnnotationBbox( imagebox, position, xybox= None, xycoords='data', boxcoords="offset points", frameon=False ) ab.zorder=1 self.ax.add_artist(ab)
def add_image(self, color):
"""Draw an image
Parameters
----------
color : string. specify the color of the image to draw.
The concatenation of the self.img_path and the color will give the complete path to the image.
(ex: to draw a network in blue, self.img_path should='../ressources/network.png' and color='blue')
"""
if self.artist:
self.artist.remove()
self.color = color
file_name, ext = os.path.splitext(self.img_path)
new_path = file_name + color + ext
image_path = get_sample_data(new_path)
image = plt.imread(image_path)
im = OffsetImage(image, zoom=self.zoom)
ab = AnnotationBbox(im, (self.x, self.y), xycoords='data', frameon=False)
ab.set_picker(5)
self.artist = plt.gca().add_artist(ab)
self.annotation_box = ab
def __plot_points_with_img(self, data, images):
data_t = self.transform(data)
# plot all points
plt.plot(data_t[:, 0], data_t[:, 1], ".r")
# draw images
ax = plt.gcf().gca()
for img, x, y in zip(images, data_t[:, 0], data_t[:, 1]):
# add a first image
imagebox = OffsetImage(img, zoom=.1)
xy = [x, y] # coordinates to position this image
ab = AnnotationBbox(imagebox, xy,
# xybox=(arr_hand.shape[1] * 0.1 / 2, -arr_hand.shape[0] * 0.1 / 2),
xybox=(0, 0),
xycoords='data',
boxcoords="offset points"
)
ab.set_zorder(3)
ax.add_artist(ab)
def background_image(self, file):
"""Set the file image as background image"""
if self.bg_img:
self.bg_img.remove()
datafile = get_sample_data(file)
img = plt.imread(datafile)
im = OffsetImage(img)
im.set_zoom(min(1. * self.canvas.window.get_size()[0] / len(im.properties()['data'][0]),
1. * self.canvas.window.get_size()[1] / len(im.properties()['data'])))
self.bg_img = AnnotationBbox(im, (0.5, 0.5), xycoords='data', frameon=False)
self.bg_img.set_zorder(-1)
plt.gca().add_artist(self.bg_img)
self.do_refresh()
def graph4(self, lo, dday):
if lo == 'All' and dday != 'All':
de = self.ds5.loc[dday]
fig = Figure(figsize=(7, 7))
plt = fig.add_subplot(111)
s = list((de['Trips'] / de['Active_Vehicles']))
dc = (max(s) + min(s)) / 800
s = list(map(lambda x: round(x, 2), s))
t = list(de['Dispatch_Base'])
b = OffsetImage(plti.imread('taxi2.png'), zoom=0.05)
plt.scatter(de['Dispatch_Base'],
(de['Trips'] / de['Active_Vehicles']),
color='white')
for x0, y0 in zip(de['Dispatch_Base'],
(de['Trips'] / de['Active_Vehicles'])):
ab = AnnotationBbox(b, (x0, y0), frameon=False)
plt.add_artist(ab)
plt.set_xlabel('Dispatch Bases')
plt.set_ylabel('Trips per Vehicle')
plt.set_title(
f'Trips per Vehicle on week day {dday} of all locations')
for i in range(len(s)):
plt.annotate(s[i], (t[i], s[i] + dc))
canvas = FigureCanvasTkAgg(fig, master=self.root)
canvas.get_tk_widget().place(x=780, y=100)
canvas.draw()
btn4 = Button(self.root,
command=self.suggestion,
text="Suggestions?",
compound=LEFT,
font=("Industry Inc Detail Fill", 20, "bold"),
bg="white",
fg="dimgray")
btn4.place(x=930, y=620, height=50, width=200)
else:
messagebox.showerror(
"Error",
"Select the Correct Location and Week Day for this graph!")
def graph(self):
fig = plt.figure(figsize=(23, 10.7))
ax = fig.add_subplot()
if self.colors is None:
plt.bar(self.x, self.y, edgecolor='black', linewidth=2,)
else:
plt.bar(self.x, self.y, edgecolor='black', linewidth=2, color=self.colors)
plt.margins(0.01, 0.01)
plt.grid(axis='y')
if not self.x_ticks:
plt.xticks([])
if self.labels is not None:
for index in range(len(self.x)):
image = OffsetImage(plt.imread(self.labels[index]), zoom=.13)
ax.autoscale()
if self.y[index] > 0:
ly = -5
else:
ly = 5
ab = AnnotationBbox(image, (self.x[index], ly), frameon=False)
ax.add_artist(ab)
plt.ylabel(self.y_label, fontsize=18)
return plt
def make_image_annotation2(image, axes, x, y, size=1.0, bbox=None):
# mage = Image.open(image)
# Resize if necessary
if size != 1.0:
width, height = image.size
width = width*size
height = height*size
image.thumbnail((width, height))
imagebox = OffsetImage(image)
imagebox.image.axes = axes
ab = AnnotationBbox(imagebox, (x, y),
xycoords='axes fraction',
boxcoords="offset points",
pad=0,
frameon=False,
box_alignment=(0.5, 0)
)
axes.add_artist(ab)
return imagebox
def make_image_annotation(icon, axes, x, y, size=1.0):
icon = Image.open(icon)
icon = icon.convert("RGBA")
# Resize if necessary
if size != 1.0:
width, height = icon.size
width = width*size
height = height*size
icon.thumbnail((width, height))
imagebox = OffsetImage(icon)
imagebox.image.axes = axes
ab = AnnotationBbox(imagebox, (x, y),
xycoords='axes fraction',
boxcoords="offset points",
pad=0,
frameon=False
)
axes.add_artist(ab)
return imagebox
def build_first_plot(df):
def getImage(path):
return OffsetImage(plt.imread(path))
logos = os.listdir(os.getcwd() + '\\logos')
logolist = []
for l in logos:
logolist.append(os.getcwd() + '\\logos\\' + l)
x = df['success']
y = df['epa']
fig, ax = plt.subplots(figsize=(10, 10))
ax.scatter(x, y, s=0.1)
ax.set_title('Success Rate vs. EPA/Play', {"fontsize": 24})
ax.set_xlabel('Success Rate', {"fontsize": 18})
ax.set_ylabel('EPA/Play', {"fontsize": 18})
for x0, y0, path in zip(x, y, logolist):
ab = AnnotationBbox(getImage(path), (x0, y0),
frameon=False,
fontsize=4)
ax.add_artist(ab)
def visualize_scatter_with_images(X_2d_data,
images,
figsize=(12, 12),
image_zoom=1.75,
title='Class Scatter'):
'''
X_2d_data is an embedding such as TSNE
expects images in the following two shapes:
3-D np.array of the form [num_imgs x height x width]
4-D np.array of the form [num_imgs x height x width x 3] for three channel images
'''
fig, ax = plt.subplots(figsize=figsize)
artists = []
for xy, i in zip(X_2d_data, images):
x0, y0 = xy
img = OffsetImage(i, zoom=image_zoom)
ab = AnnotationBbox(img, (x0, y0), xycoords='data', frameon=False)
artists.append(ax.add_artist(ab))
ax.update_datalim(X_2d_data)
ax.autoscale()
plt.title(title, fontsize=20)
plt.show()
def display(car, map, i):
global ax
plt.figure(1)
plt.clf() #Pour ne pas superposer les images qui se succèdent
fig = display_init(map)
car.orientate()
arr_lena1 = mpimg.imread('voiture_' + car.orientation +
'.png') #charge l'image de la voiture
imagebox1 = OffsetImage(arr_lena1,
zoom=2) #config de la taille de la voiture
ab1 = AnnotationBbox(
imagebox1, car.pos, frameon=False,
box_alignment=(0.5, 0.5)) #associer une image à une position
ax.add_artist(
ab1) # ajouter l'image à la fenêtre en utilisant la classe artist
map.trace_map()
'''
fig.savefig('frame_'+str(i)+'.png') #sert à enregister les frames de la simulation
'''
plt.pause(0.0000000000000000000001)
def _visualize_scatter_with_images(x_2d_data,
images,
figsize=(30, 30),
image_zoom=1):
"""
shows a scatter plot of 2d feature vector in a way that each point is marked with its corresponding image
:param x_2d_data: 2d data to be shown in scatter plot
:param images: images associated with each data point
:param figsize: size of the plot
:param image_zoom: zooming of the original image to be used as marker
:return: scatter plot of the data with image markers
"""
fig, ax = plt.subplots(figsize=figsize)
artists = []
for xy, i in zip(x_2d_data, images):
x0, y0 = xy
img = OffsetImage(i, zoom=image_zoom)
ab = AnnotationBbox(img, (x0, y0), xycoords='data', frameon=False)
artists.append(ax.add_artist(ab))
ax.update_datalim(x_2d_data)
ax.autoscale()
plt.show()
"""
def coil_image_scatter():
"""在coil数据的降维结果中展示图片"""
path = "E:\\Project\\DataLab\\imageScatter\\"
small_image(eta=0.15,
in_path=path + "images\\",
out_path=path + "smallImages\\")
Y = np.loadtxt(path + "mds3classk20yita01.csv",
dtype=np.float,
delimiter=",")
(n, m) = Y.shape
fig, ax = plt.subplots()
ax.scatter(Y[:, 0], Y[:, 1])
for i in range(0, n):
obj = i // 72 + 1
img_index = i % 72
ab = AnnotationBbox(get_image(path + "smallImages\\obj" + str(obj) +
"__" + str(img_index) + ".png"),
(Y[i, 0], Y[i, 1]),
frameon=False)
ax.add_artist(ab)
ax = plt.gca()
ax.set_aspect(1)
plt.show()
def plot_image(self,
x_lim,
y_lim,
ax,
path='../Computational_Data/Images/',
i=0,
xycoords='data'):
image = self.dict['Image'][i]
if image != 'No':
vert_off = self.dict['Vertical_Offset'][i]
horiz_off = self.dict['Horizontal_Offset'][i]
img = mpimg.imread(path + image)
imagebox = OffsetImage(img, zoom=self.dict['Zoom'][i])
ab = AnnotationBbox(imagebox,
(x_lim - horiz_off, y_lim + vert_off),
frameon=False,
xycoords=xycoords)
ax.add_artist(ab)
def _save_line_graph(scores: list, urls: list):
images, graph_size = _get_line_graph_images(urls)
size = graph_size[0] / 100
vector_figure = plt.figure(
figsize=[graph_size[0] / 100, graph_size[1] / 100])
axes = plt.axes()
axes.set_title("Fruit Vectors", size=size * 3)
axes.set_ylabel("Score", size=size * 3)
axes.set_xlabel("Day", size=size * 3)
plt.setp(axes.get_xticklabels(), size=size * 2)
plt.setp(axes.get_yticklabels(), size=size * 2)
axes.plot(scores,
color="black",
marker="o",
markerfacecolor="red",
linewidth=5)
count = 0
for score in scores:
ab = AnnotationBbox(images[count], (count, score), frameon=False)
axes.add_artist(ab)
count += 1
vector_figure.add_axes(axes)
vector_figure.savefig("./line.png", bbox_inches="tight")
def main():
fig, ax = plt.subplots(1, 1)
artists = []
x = [0, 1, 2, 3, 4]
y = [0, 1, 2, 3, 4]
A = np.random.random(size=(5, 5))
ax.matshow(A)
xl, yl, xh, yh = np.array(ax.get_position()).ravel()
print(xl, yl, xh, yh)
w = xh - xl
h = yh - yl
xp = xl + w * 0.5 #if replace '0' label, can also be calculated systematically using xlim()
print(xp)
size = 0.01
#for x0, y0 in zip(x, y):
ab = AnnotationBbox(getImage('../lib/todd2.jpg'), (xp - size * 0.5, yh),
frameon=False,
box_alignment=(0., 0.))
artists.append(ax.add_artist(ab))
plt.show()
def _plot_dial(fig_handle, ax_handle, val, max_val, maxed_pos, maxed_asset,
icon_asset, dial_colour):
ccw = False
if 0 < val <= max_val:
groups = [val / max_val, 1 - val / max_val]
elif val == 0:
groups = [0.01, 0.99]
elif val >= max_val:
groups = [max_val, 0]
_add_maxed(fig_handle, maxed_pos, maxed_asset)
else: # only valid for temperature where a negative value is possible
groups = [abs(val) / max_val, 1 - abs(val) / max_val]
ccw = True
ax_handle.pie(groups,
colors=[dial_colour, DisplayConfig.SEGMENTS],
startangle=90,
counterclock=ccw)
my_circle = plt.Circle((0, 0), 0.7, color=DisplayConfig.BACKGROUND)
ax_handle.add_artist(my_circle)
im_icon = mpimg.imread(icon_asset)
icon = OffsetImage(im_icon, zoom=0.18)
ab_icon = AnnotationBbox(icon, (0, 0), frameon=False)
ax_handle.add_artist(ab_icon)
def image_placer(self, _, industry):
_.set_title(industries[industry], fontsize = 8)
count = 1
new = file.loc[file['Your industry'] == industries[industry]]
size = 30, 30
for i in new['Logo']:
png = i.split('/', 1)[-1].replace('/', '')
r = requests.get(i, stream = True)
with open('logos/'+png, 'wb') as out_file:
shutil.copyfileobj(r.raw, out_file)
del r
img = Image.open('logos/'+png)
img.thumbnail(size, Image.ANTIALIAS)
'''img.save('logos/'+png)
arr_AS = mpimg.imread('logos/'+png)'''
imagebox = OffsetImage(img)
if count == 1:
x, y = 0.2, 0.2
elif count > 1 and count < 4:
x += 0.3
elif count == 4:
x = 0.2
y = 0.5
elif count > 4 and count < 7:
x += 0.3
elif count == 7:
x = 0.2
y = 0.8
elif count > 8 and count < 10:
x += 0.3
elif count == 10:
x, y = 0.2, 0.2
count = 1
break
AS = AnnotationBbox(imagebox, (x, y), frameon=False, pad=0)
count += 1
_.add_artist(AS)
def draw_token(cls, token, r, c, ax, height, width, token_scale=1.0):
if token == EMPTY:
edge_color = '#888888'
face_color = 'white'
drawing = RegularPolygon((c + 0.5, (height - 1 - r) + 0.5),
numVertices=4,
radius=0.5 * np.sqrt(2),
orientation=np.pi / 4,
ec=edge_color,
fc=face_color)
ax.add_patch(drawing)
return drawing
else:
edge_color = '#888888'
face_color = '#DDDDDD'
drawing = RegularPolygon((c + 0.5, (height - 1 - r) + 0.5),
numVertices=4,
radius=0.5 * np.sqrt(2),
orientation=np.pi / 4,
ec=edge_color,
fc=face_color)
ax.add_patch(drawing)
im = TOKEN_IMAGES[token]
oi = OffsetImage(im,
zoom=cls.fig_scale *
(token_scale / max(height, width)**0.5))
box = AnnotationBbox(oi, (c + 0.5, (height - 1 - r) + 0.5),
frameon=False)
ax.add_artist(box)
return box
def receive_map(self) -> None:
"""
Updates the UI when a new map is available for display
"""
children = self.plotWidget.canvas.ax.get_children()
for c in children:
if isinstance(c, AnnotationBbox):
c.remove()
zoom, radius, map_image, mark = self.map_data.get_map_value()
# plotMap UI modification
self.plotWidget.canvas.ax.set_axis_off()
self.plotWidget.canvas.ax.set_ylim(map_image.shape[0], 0)
self.plotWidget.canvas.ax.set_xlim(0, map_image.shape[1])
# Removes pesky white boarder
self.plotWidget.canvas.fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
if self.im:
# Required because plotting images over old ones creates memory leak
# NOTE: im.set_data() can also be used
self.im.remove()
self.im = self.plotWidget.canvas.ax.imshow(map_image)
# updateMark UI modification
for i in range(len(mark)):
annotation_box = AnnotationBbox(OffsetImage(MAP_MARKER), mark[i], frameon=False)
self.plotWidget.canvas.ax.add_artist(annotation_box)
# For debugging marker position
#self.plotWidget.canvas.ax.plot(mark[1][0], mark[1][1], marker='o', markersize=3, color="red")
self.plotWidget.canvas.draw()
MAP_UPDATED_EVENT.increment()
def plot_digits(X, y, min_distance=0.05, images=None, figsize=(13, 10)):
# Let's scale the input features so that they range from 0 to 1
X_normalized = MinMaxScaler().fit_transform(X)
# Now we create the list of coordinates of the digits plotted so far.
# We pretend that one is already plotted far away at the start, to
# avoid `if` statements in the loop below
neighbors = np.array([[10., 10.]])
# The rest should be self-explanatory
plt.figure(figsize=figsize)
cmap = matplotlib.cm.get_cmap("jet")
digits = np.unique(y)
for digit in digits:
plt.scatter(X_normalized[y == digit, 0],
X_normalized[y == digit, 1],
c=cmap(digit / 9),
s=2)
plt.axis("off")
ax = plt.gcf().gca() # get current axes in current figure
for index, image_coord in enumerate(X_normalized):
closest_distance = np.linalg.norm(np.array(neighbors) - image_coord,
axis=1).min()
if closest_distance > min_distance:
neighbors = np.r_[neighbors, [image_coord]]
if images is None:
plt.text(image_coord[0],
image_coord[1],
str(int(y[index])),
color=cmap(y[index] / 9),
fontdict={
"weight": "bold",
"size": 16
})
else:
image = images[index].reshape(28, 28)
imagebox = AnnotationBbox(OffsetImage(image, cmap="binary"),
image_coord)
ax.add_artist(imagebox)
def tipsquares(plot, p, colors='r', size=15, pad=2, edgepad=10):
"""
RR: Bug with this function. If you attempt to call it with a list as an
argument for p, it will not only not work (expected) but it will also
make it so that you can't interact with the tree figure (gives errors when
you try to add symbols, select nodes, etc.) -CZ
Add square after tip label, anchored to the side of the plot
Args:
plot (Tree): A Tree plot instance.
p (Node): A Node object (Should be a leaf node).
colors (str): color of drawn square. Optional, defaults to 'r' (red)
size (float): Size of square. Optional, defaults to 15
pad: RR: I am unsure what this does. Does not seem to have visible
effect when I change it. -CZ
edgepad (float): Padding from square to edge of plot. Optional,
defaults to 10.
"""
x, y = _xy(plot, p) # p is a single node or point in data coordinates
n = len(colors)
da = DrawingArea(size * n + pad * (n - 1), size, 0, 0)
sx = 0
for c in colors:
sq = Rectangle((sx, 0), size, size, color=c)
da.add_artist(sq)
sx += size + pad
box = AnnotationBbox(da, (x, y),
xybox=(-edgepad, y),
frameon=False,
pad=0.0,
xycoords='data',
box_alignment=(1, 0.5),
boxcoords=('axes points', 'data'))
plot.add_artist(box)
plot.figure.canvas.draw_idle()
def _add_image(ax,fname = None,loc = (.5,.5),**kwargs):
fname = fname.replace(' ','_')
fname = fname.replace("'",'_')
if not os.path.isfile(fname):
print '{} not found!'.format(fname)
return
print 'Adding image: {}'.format(fname)
arr_img = plt.imread(fname, format='png')
imagebox = OffsetImage(arr_img, zoom=0.175,resample=True, dpi_cor=False)
imagebox.image.axes = ax
ab = AnnotationBbox(imagebox, loc,
xycoords='data',
boxcoords="offset points",
frameon=False,
arrowprops=dict(
arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=3")
)
ax.add_artist(ab)
def plot_dog_atlas(embed, meta_df, title, ax):
# fitting kmeans to get evenly spaced points on MAP
km = KMeans(n_clusters=100)
km.fit(embed)
# getting these centroids
centroids = km.cluster_centers_
medoids = (pd.DataFrame(embed).apply(
lambda x: km.score(x.values.reshape(1, -1)),
axis=1).groupby(km.predict(embed)).idxmax())
# images to plot
img_to_plot = meta_df.index.values[medoids]
# plotting a light scatter plot
#fig, ax = plt.subplots(figsize=(12,6), dpi=120)
ax.scatter(embed[:, 0], embed[:, 1], s=2, alpha=0.1, color='black')
# loop adding pictures to plot
for i, img in enumerate(img_to_plot):
img = plt.imread(img)
imagebox = OffsetImage(img, zoom=0.1)
imagebox.image.axes = ax
ab = AnnotationBbox(imagebox, embed[medoids[i]], pad=0)
ax.add_artist(ab)
# title and other info
ax.set_title(title)
ax.set_xlabel('first UMAP dimension')
ax.set_ylabel('second UMAP dimension')
plt.grid(b=None)
ax.set_xticks([])
ax.set_yticks([])
def scatter(x, colors):
# We choose a color palette with seaborn.
palette = np.array(sns.color_palette("hls", 10))
# We create a scatter plot.
f = plt.figure()
ax = plt.subplot()
sc = ax.scatter(x[:, 0], x[:, 1], lw=0, s=40, c=palette[1])
plt.xlim(-400, 400)
plt.ylim(-400, 400)
ax.axis('off')
ax.axis('tight')
img_boxs = []
for ind, point in zip(range(len(x)), x):
oImg = OffsetImage(plt.imread(image_paths[ind]), zoom=.2)
ab = AnnotationBbox(oImg,
xy=(point[0], point[1]),
xycoords='data',
boxcoords="offset points")
img_boxs.append(ax.add_artist(oImg))
print('ImgBox[%d]' % ind)
return f, ax, sc, img_boxs
def plot_digits(X, y, min_distance=0.05, images=None, figsize=(13, 10)):
# Scale the input features for range 0 to 1
X_normalized = MinMaxScaler().fit_transform(X)
# Create the list of coordinates of the digits plotted
# Create one initially
neighbors = np.array([[10., 10.]])
plt.figure(figsize=figsize)
cmap = matplotlib.cm.get_cmap("jet")
digits = np.unique(y)
for digit in digits:
plt.scatter(
X_normalized[y == digit, 0],
X_normalized[y == digit, 1],
c=cmap(digit / 9))
plt.axis("off")
ax = plt.gcf().gca()
for index, image_coord in enumerate(X_normalized):
closest_distance = np.linalg.norm(
np.array(neighbors) - image_coord, axis=1).min()
if closest_distance > min_distance:
neighbors = np.r_[neighbors, [image_coord]]
if images is None:
plt.text(
image_coord[0],
image_coord[1],
str(int(y[index])),
color=cmap(y[index] / 9),
fontdict={
"weight": "bold",
"size": 16
})
else:
image = images[index].reshape(28, 28)
imagebox = AnnotationBbox(
OffsetImage(image, cmap="binary"), image_coord)
ax.add_artist(imagebox)
def add_image(name: str, x: float or int, y: float or int, offset: float, zoom: float = 0.20) -> AnnotationBbox:
"""
Adds the given image to the bar graph, with the given specifications.
Args:
name - Should be a string representing name of the file to open (without file extension)
x - x-coordinate
y - y-coordinate
offset - By how much to the left or right should the image be placed. Is applied only to x coordinate.
zoom - Controls how big the image is.
"""
# Open image as numpy array-
try:
pic_file = get_sample_data(f"{cwd}/profile_pics/{name}.png")
except FileNotFoundError: # When user has no profile pic, or changed their dp privacy settings
pic_file = get_sample_data(f"{cwd}/profile_pics/nobody.png")
with pic_file as file:
arr_img = plt.imread(file, format='jpg')
image_box = OffsetImage(arr_img, zoom=zoom) # zoom changes the size of the image
# Adds image to the provided coordinates-
return AnnotationBbox(image_box, (x + offset, y), frameon=False, annotation_clip=False)
def plot_pixel_level_results_as_image_graph():
Y, files = torch.load('../tsne_output_pix.pth')
fig, ax = pyplot.subplots()
fig.set_size_inches(200,200,forward=True)
ax.update_datalim(np.column_stack([Y[:, 0], Y[:, 1]]))
ax.autoscale()
expansion = 8 # Should be latent_compression(=8) * image_compression_at_inference(=1)
margins = 4 # Keep in mind this will be multiplied by <expansion>
for b in tqdm(range(Y.shape[0])):
if b % 4 == 0:
id = b // 4
imgfile = files[id]
baseim = pyplot.imread(imgfile)
ct, cl = random_coords[b%4]
im = baseim[expansion*(ct-margins):expansion*(ct+margins),
expansion*(cl-margins):expansion*(cl+margins),:]
im = OffsetImage(im, zoom=1)
ab = AnnotationBbox(im, (Y[b, 0], Y[b, 1]), xycoords='data', frameon=False)
ax.add_artist(ab)
ax.scatter(Y[:, 0], Y[:, 1])
pyplot.savefig('tsne_pix.pdf')
def plot_progress(self, error_series, plt_imgs, plt_imgs_t):
"""
This method generates an informative plot, displaying the loss over time along logged images.
:param error_series: loss values over time (/ steps)
:param plt_imgs: logged images
:param plt_imgs_t: time (/ step) at which respective images have been logged
:return:
"""
fig, ax = plt.subplots(figsize=(160, 40))
ax.plot(np.arange(1, 1 + len(error_series)), error_series, alpha=0.9)
for i, p_I in enumerate(plt_imgs):
imagebox = OffsetImage(p_I, zoom=1.)
x = plt_imgs_t[i]
ab = AnnotationBbox(imagebox, (x, error_series[x - 1]),
xybox=(0., 256.),
xycoords='data',
boxcoords="offset points",
arrowprops={'arrowstyle': '->'})
ax.add_artist(ab)
plt.savefig(join(f'logs/{self.model_name}/progress.png'),
bbox_inches='tight',
dpi=50)
plt.close(fig)
def average_age_visualize(save):
data = pd.read_csv('Data/average_age.csv')
df = pd.DataFrame(data)
fig, ax = plt.subplots(figsize=(20, 15))
ax.title.set_text('Average age of each NFL team')
for index in df.index:
row = str(df.iloc[index]).split(' ')
row = remove_spaces(row)
team = row[1].replace('age', '').replace(' ', '')
age = row[2].replace('Name:', '')
team = team.replace('\r\n', '\n').replace('\n', '')
image = team + '.png'
ax.scatter(team, float(age), c='w')
ab = AnnotationBbox(OffsetImage(plt.imread(image, 0)),
(team, float(age)),
frameon=False)
ax.add_artist(ab)
ax.axhline(y=avg_list(df['age']), color='black')
if save:
plt.savefig('avgage.png')
else:
plt.show()
def image_scatter():
path = "E:\\Project\\DataLab\\duck\\"
path1 = "E:\\Project\\DataLab\\duck\\images\\"
path2 = "E:\\Project\\DataLab\\duck\\smallimages\\"
small_image(eta=0.3, in_path=path1, out_path=path2)
image_path = path + "smallimages\\obj1__"
X = np.loadtxt(path + "data.csv", dtype=np.float, delimiter=",")
X = preprocessing.minmax_scale(X)
(n, m) = X.shape
print((n, m))
pca = PCA(n_components=2)
Y = pca.fit_transform(X)
fig, ax = plt.subplots()
ax.scatter(Y[:, 0], Y[:, 1])
for i in range(0, n):
ab = AnnotationBbox(get_image(image_path + str(i) + ".png"),
(Y[i, 0], Y[i, 1]),
frameon=False)
ax.add_artist(ab)
ax = plt.gca()
ax.set_aspect(1)
plt.show()
def imscatter(x, y, images, ax=None, zoom=1, lw=0, color='black'):
if ax is None:
ax = plt.gca()
x, y = np.atleast_1d(x, y)
artists = []
for x0, y0, image in zip(x, y, images):
image = plt.imread(image)
im = OffsetImage(image, zoom=zoom)
ab = AnnotationBbox(im, (x0, y0),
xycoords='data',
frameon=True,
bboxprops=dict(edgecolor=color,
boxstyle="square,pad=0",
lw=lw))
artists.append(ax.add_artist(ab))
ax.update_datalim(np.column_stack([x, y]))
ax.autoscale()
return artists
def generate_map(self, lat, lon):
lat = float(lat)
lon = float(lon)
icon = join(dirname(__file__), self.settings["iss_icon"])
output = join(tempfile.gettempdir(), "iss.jpg")
lat_0 = None
lon_0 = None
if self.settings["center_iss"]:
lat_0 = lat
lon_0 = lon
elif self.settings["center_location"]:
lat_0 = self.location["coordinate"]["latitude"]
lon_0 = self.location["coordinate"]["longitude"]
if self.settings["map_style"] == "cyl":
lat_0 = None
lon_0 = None
m = Basemap(projection=self.settings["map_style"],
resolution=None,
lat_0=lat_0,
lon_0=lon_0)
m.bluemarble()
x, y = m(lon, lat)
iss = plt.imread(icon)
im = OffsetImage(iss, zoom=self.settings["iss_size"])
ab = AnnotationBbox(im, (x, y), xycoords='data', frameon=False)
# Get the axes object from the basemap and add the AnnotationBbox artist
m._check_ax().add_artist(ab)
plt.savefig(output,
dpi=self.settings["dpi"],
bbox_inches='tight',
facecolor="black")
plt.close()
return output
def imscatter(
x: np.ndarray,
y: np.ndarray,
ax: Axes,
imageData: torch.Tensor,
unnormalize_fn: Optional[Callable] = None,
zoom: int = 1,
) -> NoReturn:
"""Scatter plot with images instead of points on ax."""
images = []
for i in range(len(x)):
x0, y0 = x[i], y[i]
# Convert to image
img = imageData[i]
if unnormalize_fn is not None:
img = unnormalize_fn(img)
img *= 255.0
img = img.permute([1, 2, 0]).numpy().astype(np.uint8)
image = OffsetImage(img, zoom=zoom)
ab = AnnotationBbox(image, (x0, y0), xycoords="data", frameon=False)
images.append(ax.add_artist(ab))
ax.update_datalim(np.column_stack([x, y]))
ax.autoscale()
import matplotlib.pyplot as plt
ax = plt.subplot(111)
ax.plot([1,2,3], label="test")
l = ax.legend()
d1 = l.draggable()
xy = 1, 2
txt = ax.annotate("Test", xy, xytext=(-30, 30),
textcoords="offset points",
bbox=dict(boxstyle="round",fc=(0.2, 1, 1)),
arrowprops=dict(arrowstyle="->"))
d2 = txt.draggable()
from matplotlib._png import read_png
from matplotlib.cbook import get_sample_data
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
fn = get_sample_data("lena.png", asfileobj=False)
arr_lena = read_png(fn)
imagebox = OffsetImage(arr_lena, zoom=0.2)
ab = AnnotationBbox(imagebox, xy,
xybox=(120., -80.),
xycoords='data',
boxcoords="offset points",
pad=0.5,
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=3")
)
ax.add_artist(ab)
d3 = ab.draggable(use_blit=True)
plt.show()
ax.yaxis.pan(+1)
elif event.key == 'down':
ax.yaxis.pan(-1)
draw()
fig = figure()
fig.canvas.mpl_connect('key_press_event', onpress)
ax = fig.gca()
ann = AnnotationBbox(OffsetImage(np.arange(256).reshape(16,16)/256.0,
zoom=2,
norm = None,
origin=None),
(0.5, 0.5),
xybox=(30, 30),
xycoords='data',
boxcoords="offset points",
frameon=True, pad=0.4, # BboxPatch
bboxprops=dict(boxstyle="round", fc="y"),
fontsize=None,
arrowprops=dict(arrowstyle="->"))
ax.add_artist(ann)
ann.draggable()
show()
#draggable = DraggableAnnotation(a, use_blit=True)
class Gtk_NetworkCanvas:
"""Gtk_NetworkCanvas class.
This class contains the canvas to draw the topology. It implements event listener and zoom.
Parameters
----------
canvas : the gtk canvas to draw
adjustement : used for zoom scroll bar
zoom_scale : a scroll bar to zoom
redraw : a button to redraw the graph
popup : a popup for interaction on right click
bRefresh : bool. if True enable refresh with do_refresh function
corners : the limit of the canvas drawing area
press : bool. True if mouse click on canvas (used for zoom)
x_old, y_old : position for zoom
"""
def __init__(self):
fig = plt.figure(num=None, facecolor='w', edgecolor='k')
plt.axis('off')
plt.subplots_adjust(left=0., right=1., bottom=0., top=1., wspace=0.2, hspace=0.2)
self.canvas = FigureCanvas(fig)
self.canvas.add_events(
gtk.gdk.EXPOSURE_MASK | gtk.gdk.LEAVE_NOTIFY_MASK | gtk.gdk.BUTTON_PRESS_MASK | gtk.gdk.POINTER_MOTION_MASK | gtk.gdk.POINTER_MOTION_HINT_MASK | gtk.gdk.BUTTON_RELEASE_MASK)
self.canvas.connect("motion_notify_event", self.on_motion)
self.canvas.connect("button-press-event", self.on_click)
self.canvas.connect("button-release-event", self.on_release)
self.canvas.connect("scroll-event", self.on_scroll)
self.canvas.connect("leave-notify-event", self.on_lose_focus)
self.adjustement = gtk.Adjustment(0.0, 0.0, 100.0, 1.0, 1.0, 1.0)
self.adjustement_signal = self.adjustement.connect("value-changed", self.on_zoom_changed)
self.zoom_scale = gtk.HScale(self.adjustement)
self.zoom_scale.set_draw_value(False)
self.zoom_value = 0.0
self.redraw = gtk.Button("Redraw")
self.redraw.connect("clicked", self.on_redraw)
self.hbox = gtk.HBox()
self.hbox.pack_start(self.zoom_scale, True, True, 0)
self.hbox.pack_end(self.redraw, False, False, 0)
self.vbox = gtk.VBox()
self.vbox.pack_start(self.canvas, True, True, 0)
self.vbox.pack_end(self.hbox, False, False, 0)
self.popup = Gtk_NewtworkPopupMenu()
self.bRefresh = True
self.corners = None
self.press = False
self.x_old = 0
self.y_old = 0
self.bg_img = None
def on_click(self, widget, event):
"""Event listener : click
If double left click :
- on edge, show edges rules
- on firewall, show firewall conf
- on interface, add note
If left click :
- on edge, show message
- on firewall, show message interaction firewall
- on interface, show message interaction interface
- else move x/y limit drawing area
If right click :
- on edge, show edge menu
- on firewall, show firewall menu
- on interface, show interface menu
- else show canvas menu
"""
if event.button == 1 and event.type == gtk.gdk._2BUTTON_PRESS:
dbl_click = False
dbl_click |= self.on_dblclick_edge()
dbl_click |= self.on_dblclick_node()
if not dbl_click:
Gtk_Main.Gtk_Main().lateral_pane.help_message.change_message(Gtk_Message.TOPOLOGY_MESSAGE)
if event.button == 3 and event.type == gtk.gdk.BUTTON_PRESS:
right_click = False
right_click |= self.on_right_click_edge(event)
right_click |= self.on_right_click_node(event)
if not right_click:
Gtk_Main.Gtk_Main().lateral_pane.help_message.change_message(Gtk_Message.ON_BACKGROUND_CLICK)
self.popup.popup_clear(event)
if event.button == 1 and event.type == gtk.gdk.BUTTON_PRESS:
left_click = False
left_click |= self.on_left_click_edge()
left_click |= self.on_left_click_node()
if not left_click:
self.press = True
self.x_old = event.x
self.y_old = event.y
Gtk_Main.Gtk_Main().lateral_pane.help_message.change_message(Gtk_Message.TOPOLOGY_MESSAGE)
return True
def on_dblclick_edge(self):
"""Show interface rules"""
def get_firewall(x, y):
if isinstance(x, Firewall):
return x
elif isinstance(y, Firewall):
return y
return None
def get_ip(x, y):
if isinstance(x, Ip):
return x
elif isinstance(y, Ip):
return y
return None
g = NetworkGraph.NetworkGraph()
for elem in g.graph.edges(data=True):
edge = elem[2]['object']
if edge.gtk_press:
fw = get_firewall(elem[0], elem[1])
ip = get_ip(elem[0], elem[1])
result = []
[result.append(acl) for acl in g.get_acl_list(src=ip, dst=None, firewall=fw)]
[result.append(acl) for acl in g.get_acl_list(src=None, dst=ip, firewall=fw)]
if not result:
Gtk_DialogBox("No rules found for this interface !")
[Gtk_Main.Gtk_Main().notebook.add_interface_tab(acl) for acl in result]
return True
return False
def on_dblclick_node(self):
"""Event listener, on double click node, if firewall show conf file else add note"""
g = NetworkGraph.NetworkGraph()
for k, v in g.graph.node.items():
if v['object'].gtk_press and isinstance(v['object'].object, Firewall):
Gtk_Main.Gtk_Main().notebook.add_conf_tab(v['object'].object.name, v['object'].object.hostname)
return True
if v['object'].gtk_press and isinstance(v['object'].object, Ip):
self.popup.node = v['object']
self.popup.on_add_note(None)
return True
return False
def on_right_click_edge(self, event):
"""Event listener, on right click edge, popup menu showing acl list of related to this interface"""
g = NetworkGraph.NetworkGraph()
for elem in g.graph.edges(data=True):
edge = elem[2]['object']
if edge.gtk_press:
self.popup.popup(elem, event, edge)
edge.gtk_press = False
return True
return False
def on_right_click_node(self, event):
"""Event listener, on right click node, show popup menu for node"""
g = NetworkGraph.NetworkGraph()
for k, v in g.graph.node.items():
if v['object'].gtk_press:
self.popup.popup(None, event, v['object'])
v['object'].gtk_press = False
return True
return False
def on_left_click_node(self):
"""Show node details"""
g = NetworkGraph.NetworkGraph()
for k, v in g.graph.node.items():
if v['object'].gtk_press:
Gtk_Main.Gtk_Main().lateral_pane.help_message.change_message(Gtk_Message.ON_CLICK_NODE)
Gtk_Main.Gtk_Main().lateral_pane.details.clear()
tmp_intf = [e[2]['object'].object for e in g.graph.edges(k, data=True)]
for e in sorted(tmp_intf, key=lambda tmp_intf: tmp_intf.nameif):
message = "%s:\n- %s\n- %s" % (e.nameif, e.name, e.network.to_string())
for key, value in e.attributes.items():
message += "\n- %s : %s" % (key, value)
Gtk_Main.Gtk_Main().lateral_pane.details.add_row(message)
Gtk_Main.Gtk_Main().lateral_pane.focus_details()
return True
return False
def on_left_click_edge(self):
"""If left click edge, show help message"""
g = NetworkGraph.NetworkGraph()
for edge in g.graph.edges():
if g.graph[edge[0]][edge[1]]['object'].gtk_press:
Gtk_Main.Gtk_Main().lateral_pane.help_message.change_message(Gtk_HelpMessage.Gtk_Message.ON_CLICK_EDGE)
return True
return False
def on_motion(self, widget, event):
"""If not click node, then move axis"""
if self.press and self.corners:
xlim = list(plt.gca().get_xlim())
ylim = list(plt.gca().get_ylim())
x = (self.x_old - event.x) / (1. * self.canvas.window.get_size()[0] / (xlim[1] - xlim[0]))
y = (event.y - self.y_old) / (1. * self.canvas.window.get_size()[1] / (ylim[1] - ylim[0]))
self.x_old = event.x
self.y_old = event.y
self.axes_move(x, y, x, y)
self.do_refresh()
def refresh(self):
"""refresh function. This function is call periodically by do_refresh"""
self.bRefresh = True
def do_refresh(self):
"""Update the graph if bRefresh is True"""
if self.bRefresh:
self.bRefresh = False
self.canvas.draw()
gtk.timeout_add(30, self.refresh)
def on_release(self, widget, event):
"""Event listener : release"""
self.press = False
def on_scroll(self, widget, event):
"""Event listener : scroll. Update zoom"""
if event.direction == 0 and self.adjustement.get_value() < 99:
self.adjustement.set_value(self.adjustement.get_value() + 1)
elif event.direction == 1 and self.adjustement.get_value() > 0:
self.adjustement.set_value(self.adjustement.get_value() - 1)
def on_zoom_changed(self, widget):
"""Event listerner : HScale change. Update zoom"""
if self.corners:
im = None
if self.bg_img:
im = self.bg_img.get_children()[0]
if widget.value != 0:
zoom = (self.zoom_value - widget.value) * (self.corners[1][0] - self.corners[0][0]) / 200
self.axes_zoom(-zoom, -zoom, zoom, zoom)
if im:
dim = min(1. * self.canvas.window.get_size()[0] / len(im.properties()['data'][0]),
1. * self.canvas.window.get_size()[1] / len(im.properties()['data']))
corners = min(self.corners[1][0] - self.corners[0][0], self.corners[1][1] - self.corners[0][1])
im.set_zoom(im.get_zoom() - zoom * 2 * dim / corners)
else:
plt.gca().set_xlim((self.corners[0][0], self.corners[1][0]))
plt.gca().set_ylim((self.corners[0][1], self.corners[1][1]))
if im:
im.set_zoom(min(1. * self.canvas.window.get_size()[0] / len(im.properties()['data'][0]),
1. * self.canvas.window.get_size()[1] / len(im.properties()['data'])))
self.zoom_value = widget.value
self.do_refresh()
def on_lose_focus(self, widget, event):
"""Event listener : focus"""
self.press = False
NetworkGraph.NetworkGraph().node_click = False
for node in NetworkGraph.NetworkGraph().graph.nodes(data=True):
node[1]['object'].press = False
for edge in NetworkGraph.NetworkGraph().graph.edges(data=True):
edge[2]['object'].press = False
self.do_refresh()
def on_redraw(self, widget):
"""Event listener : button redraw"""
self.draw()
def axes_move(self, x0, y0, x1, y1):
"""Change axis position according to the drawing area limit.
Parameters
----------
x0 : float. x minimal value
x1 : float. x maximal value
y0 : float. y minimal value
y1 : float. y maximal value
"""
if self.corners:
x = list(plt.gca().get_xlim())
y = list(plt.gca().get_ylim())
if ((x0 < 0 and x[0] + x0 >= self.corners[0][0]) or (x0 > 0 and x[0] + x0 < x[1])) and \
((x1 > 0 and x[1] + x1 <= self.corners[1][0]) or (x1 < 0 and x[1] + x1 > x[0])):
x[0] += x0
x[1] += x1
if ((y0 < 0 and y[0] + y0 >= self.corners[0][1]) or (y0 > 0 and y[0] + y0 < y[1])) and \
((y1 > 0 and y[1] + y1 <= self.corners[1][1]) or (y1 < 0 and y[1] + y1 > y[0])):
y[0] += y0
y[1] += y1
plt.gca().set_xlim((x[0], x[1]))
plt.gca().set_ylim((y[0], y[1]))
def axes_zoom(self, x0, y0, x1, y1):
"""Zoom axis position according to the drawing area limit.
Parameters
----------
x0 : float. x minimal value
x1 : float. x maximal value
y0 : float. y minimal value
y1 : float. y maximal value
"""
if self.corners:
x = list(plt.gca().get_xlim())
y = list(plt.gca().get_ylim())
if (x0 < 0 and x[0] >= self.corners[0][0]) or (x0 > 0 and x[0] + x0 < x[1]):
x[0] += x0
if (x1 > 0 and x[1] <= self.corners[1][0]) or (x1 < 0 and x[1] - x1 > x[0]):
x[1] += x1
if (y0 < 0 and y[0] >= self.corners[0][1]) or (y0 > 0 and y[0] + y0 < y[1]):
y[0] += y0
if (y1 > 0 and y[1] <= self.corners[1][1]) or (y1 < 0 and y[1] - y1 > y[0]):
y[1] += y1
plt.gca().set_xlim((x[0], x[1]))
plt.gca().set_ylim((y[0], y[1]))
def background_image(self, file):
"""Set the file image as background image"""
if self.bg_img:
self.bg_img.remove()
datafile = get_sample_data(file)
img = plt.imread(datafile)
im = OffsetImage(img)
im.set_zoom(min(1. * self.canvas.window.get_size()[0] / len(im.properties()['data'][0]),
1. * self.canvas.window.get_size()[1] / len(im.properties()['data'])))
self.bg_img = AnnotationBbox(im, (0.5, 0.5), xycoords='data', frameon=False)
self.bg_img.set_zorder(-1)
plt.gca().add_artist(self.bg_img)
self.do_refresh()
def draw(self):
"""Draw the netowrk graph and set limit corners"""
g = NetworkGraph.NetworkGraph()
g.layout_graph()
g.draw(self.canvas)
self.corners = (-0.05, -0.05), (1.05, 1.05)
plt.gca().set_xlim((-0.05, 1.05))
plt.gca().set_ylim((-0.05, 1.05))
self.do_refresh()
