Matplotlib 后端笔记本定制
Matplotlib backend notebook customizations
我正在使用 matplotlib 后端 'notebook'，因为我正在制作一些交互式图形，这与笔记本后端配合得很好（特别是，我通过 Jupyter Notebooks 为它们提供服务）。我使用 ipywidgets 来设计 GUI 和交互性。
然而，使用这个后端，有各种各样的按钮可以干扰我的交互图形。特别是，调整大小、缩放、平移或电源按钮，会让我的学生感到困惑...
我想禁用它们。有关我要禁用的内容，请参见此插图。
谁能告诉我相关的 API 页面，或者谁知道如何 disable/remove 这些按钮？我尝试了一些其他后端，但这些后端通常不能很好地用于 Jupyter notebook 中的交互式图形，因此我想尽可能坚持使用 notebook 后端。
这是svm_helper的内容：
from matplotlib import pyplot as plt
from matplotlib.backend_bases import MouseButton as mb
import ipywidgets as widgets
import sklearn.linear_model
import sklearn.metrics
import sklearn.svm
import numpy as np
def plot_decision_boundary_margin(X, y, model):
Xmin = np.min(X[:,:],axis=0)
Xmax = np.max(X[:,:],axis=0)
Xmin = np.array([-3, -3])
Xmax = np.array([3, 3])
x0, x1 = np.meshgrid(
np.linspace(Xmin[0], Xmax[0], 500).reshape(-1, 1),
np.linspace(Xmin[1], Xmax[1], 200).reshape(-1, 1),
)
X_new = np.c_[x0.ravel(), x1.ravel()]
y_new = model.decision_function(X_new)
#plot_dataset(X,y)
zz = y_new.reshape(x0.shape)
C1 = plt.contour(x0, x1, zz, levels=np.array([0]),colors='k')
C2 = plt.contour(x0, x1, zz, levels=np.array([-1,1]),colors='k',linestyles='dashed')
return (C1, C2)
class LineBuilder2:
def __init__(self, lineR, lineB, widgetcolor, widgetC, my_out, need_seperable):
self.lineR = lineR
self.xsR = list(lineR.get_xdata())
self.ysR = list(lineR.get_ydata())
self.lineB = lineB
self.xsB = list(lineB.get_xdata())
self.ysB = list(lineB.get_ydata())
self.mywidgetcolor = widgetcolor
self.cid = lineR.figure.canvas.mpl_connect('button_press_event', self)
self.cid = lineR.figure.canvas.mpl_connect('motion_notify_event', self)
self.widgetC = widgetC
self.my_out = my_out
self.dragging_timer = 0
self.trained = False
self.model = None
self.C1 = None
self.C2 = None
self.need_seperable = need_seperable
def remove_decision_boundary(self):
if (self.C1 == None) or (self.C2 == None):
return
for coll in self.C1.collections:
plt.gca().collections.remove(coll)
for coll in self.C2.collections:
plt.gca().collections.remove(coll)
def __call__(self, event):
#print('click', event)
currently_dragging = False
if event.name == 'motion_notify_event':
currently_dragging = True
self.dragging_timer = self.dragging_timer+1
if self.dragging_timer > 5:
self.dragging_timer = 0
if not (event.button == mb.LEFT or event.button == mb.MIDDLE or event.button == mb.RIGHT):
return
if event.inaxes != self.lineB.axes:
return
#print(widgetcolor.value)
if self.mywidgetcolor.value == 'green':
self.xsR.append(event.xdata)
self.ysR.append(event.ydata)
if (not currently_dragging) or (currently_dragging and self.dragging_timer == 0):
self.lineR.set_data(self.xsR, self.ysR)
#self.lineR.figure.canvas.draw()
if self.mywidgetcolor.value == 'blue':
self.xsB.append(event.xdata)
self.ysB.append(event.ydata)
if (not currently_dragging) or (currently_dragging and self.dragging_timer == 0):
self.lineB.set_data(self.xsB, self.ysB)
#self.lineB.figure.canvas.draw()
#if self.dragging_timer == 0:
# self.lineR.figure.canvas.draw()
def clear(self, button):
if self.trained == False:
with self.my_out:
print('can only reset if trained')
return
with self.my_out:
print('resetted the widget')
self.trained = False
self.remove_decision_boundary()
self.C1 = None
self.C2 = None
self.model = None
self.xsR = []
self.ysR = []
self.xsB = []
self.ysB = []
self.lineR.set_data(self.xsR, self.ysR)
self.lineB.set_data(self.xsB, self.ysB)
self.lineB.figure.canvas.draw()
self.lineR.figure.canvas.draw()
def export(self):
dataR = np.array([self.xsR,self.ysR]).transpose()
dataB = np.array([self.xsB,self.ysB]).transpose()
yR = np.ones((dataR.shape[0], 1))
yB = -np.ones((dataB.shape[0], 1))
X = np.concatenate((dataR,dataB))
y = np.concatenate((yR,yB))
y = np.reshape(y,y.shape[0])
return (X,y)
def train(self, button):
self.my_out.clear_output()
if len(self.xsR) < 1 or len(self.xsB) < 1:
with self.my_out:
print('need at least one object in both classes to train')
return
(X,y) = self.export()
if self.need_seperable:
C = float('inf')
else:
C = self.widgetC.value
model = sklearn.svm.LinearSVC(loss='hinge',C=C)
model.fit(X,y)
if self.need_seperable:
acc = model.score(X,y)
if acc < 0.99999:
with self.my_out:
print('this dataset is not seperable')
return
self.remove_decision_boundary()
train_error = model.score(X,y)
(C1, C2) = plot_decision_boundary_margin(X,y,model)
self.C1 = C1
self.C2 = C2
self.model = model
self.trained = True
with self.my_out:
if self.need_seperable:
print('trained hard margin SVM')
else:
print('trained soft margin SVM with C %f' % C)
def init(need_seperable = True):
# Turn off interactivity, for now
plt.ioff()
fig = plt.figure(figsize = (4,4))
ax = fig.add_subplot(111)
# Make some nice axes
ax.set_xlim(-3, 3)
ax.set_ylim(-3, 3)
ax.set_title('click to add points')
ax.set_xlabel('Feature 1')
ax.set_ylabel('Feature 2')
# Remove some stuff from the backend
#fig.canvas.toolbar_visible = False # Hide toolbar
#fig.canvas.header_visible = False # Hide the Figure name at the top of the figure
#fig.canvas.footer_visible = False
#fig.canvas.resizable = False
# These items will contain the objects
lineR, = ax.plot([], [], linestyle="none", marker="s", color="g", markersize=10)
lineB, = ax.plot([], [], linestyle="none", marker="^", color="b", markersize=10)
# Make the GUI
w_clear = widgets.Button(
description='Clear all',
disabled=False,
button_style='danger', # 'success', 'info', 'warning', 'danger' or ''
tooltip='Remove all data and start from scratch',
icon='check' # (FontAwesome names without the `fa-` prefix)
)
w_color = widgets.ToggleButtons(
options=['green', 'blue'],
description='Class:',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltips=['Description of slow', 'Description of regular'],
# icons=['check'] * 3
)
if not need_seperable:
w_C = widgets.FloatLogSlider(
value=1,
base=10,
min=-10, # max exponent of base
max=10, # min exponent of base
step=0.2, # exponent step
#description='Log Slider',
description='C:',
continuous_update=False,
orientation='horizontal',
readout=True,
#readout_format='.2f',
)
else:
w_C = None
w_train = widgets.Button(
description='Train SVM',
disabled=False,
button_style='warning', # 'success', 'info', 'warning', 'danger' or ''
tooltip='...',
icon='check' # (FontAwesome names without the `fa-` prefix)
)
out = widgets.Output(layout={'border': '1px solid black'})
out.layout.height = '40px'
out.layout.width = '600px'
if need_seperable:
b1 = widgets.HBox([w_color,w_train])
bar = widgets.VBox([b1, out])
else:
b1 = widgets.HBox([w_color,w_C,w_train])
#b2 = widgets.HBox([w_train,w_C])
bar = widgets.VBox([b1, out])
linebuilder = LineBuilder2(lineR, lineB, w_color, w_C, out, need_seperable)
w_clear.on_click(linebuilder.clear)
w_train.on_click(linebuilder.train)
# Turn interactivity back on
plt.ion()
out = fig
ui = bar
return display(ui, out)
为了开始交互，我在 Jupyter notebook 中使用了以下内容：
%matplotlib notebook
from svm_helper import init
init()
到目前为止，我发现在以 %matplotlib notebook 开头的单元格上方的单元格中添加以下代码（来自 here）有效：
%%html
<style>
.output_wrapper button.btn.btn-default,
.output_wrapper .ui-dialog-titlebar {
display: none;
}
</style>
可能不理想，因为你必须解释为什么他们必须 运行 这样做，而不是向你的学生解释忽略按钮，但这很重要。
我正在使用 matplotlib 后端 'notebook'，因为我正在制作一些交互式图形，这与笔记本后端配合得很好（特别是，我通过 Jupyter Notebooks 为它们提供服务）。我使用 ipywidgets 来设计 GUI 和交互性。
然而，使用这个后端，有各种各样的按钮可以干扰我的交互图形。特别是，调整大小、缩放、平移或电源按钮，会让我的学生感到困惑...
我想禁用它们。有关我要禁用的内容，请参见此插图。
谁能告诉我相关的 API 页面，或者谁知道如何 disable/remove 这些按钮？我尝试了一些其他后端，但这些后端通常不能很好地用于 Jupyter notebook 中的交互式图形，因此我想尽可能坚持使用 notebook 后端。
这是svm_helper的内容：
from matplotlib import pyplot as plt
from matplotlib.backend_bases import MouseButton as mb
import ipywidgets as widgets
import sklearn.linear_model
import sklearn.metrics
import sklearn.svm
import numpy as np
def plot_decision_boundary_margin(X, y, model):
Xmin = np.min(X[:,:],axis=0)
Xmax = np.max(X[:,:],axis=0)
Xmin = np.array([-3, -3])
Xmax = np.array([3, 3])
x0, x1 = np.meshgrid(
np.linspace(Xmin[0], Xmax[0], 500).reshape(-1, 1),
np.linspace(Xmin[1], Xmax[1], 200).reshape(-1, 1),
)
X_new = np.c_[x0.ravel(), x1.ravel()]
y_new = model.decision_function(X_new)
#plot_dataset(X,y)
zz = y_new.reshape(x0.shape)
C1 = plt.contour(x0, x1, zz, levels=np.array([0]),colors='k')
C2 = plt.contour(x0, x1, zz, levels=np.array([-1,1]),colors='k',linestyles='dashed')
return (C1, C2)
class LineBuilder2:
def __init__(self, lineR, lineB, widgetcolor, widgetC, my_out, need_seperable):
self.lineR = lineR
self.xsR = list(lineR.get_xdata())
self.ysR = list(lineR.get_ydata())
self.lineB = lineB
self.xsB = list(lineB.get_xdata())
self.ysB = list(lineB.get_ydata())
self.mywidgetcolor = widgetcolor
self.cid = lineR.figure.canvas.mpl_connect('button_press_event', self)
self.cid = lineR.figure.canvas.mpl_connect('motion_notify_event', self)
self.widgetC = widgetC
self.my_out = my_out
self.dragging_timer = 0
self.trained = False
self.model = None
self.C1 = None
self.C2 = None
self.need_seperable = need_seperable
def remove_decision_boundary(self):
if (self.C1 == None) or (self.C2 == None):
return
for coll in self.C1.collections:
plt.gca().collections.remove(coll)
for coll in self.C2.collections:
plt.gca().collections.remove(coll)
def __call__(self, event):
#print('click', event)
currently_dragging = False
if event.name == 'motion_notify_event':
currently_dragging = True
self.dragging_timer = self.dragging_timer+1
if self.dragging_timer > 5:
self.dragging_timer = 0
if not (event.button == mb.LEFT or event.button == mb.MIDDLE or event.button == mb.RIGHT):
return
if event.inaxes != self.lineB.axes:
return
#print(widgetcolor.value)
if self.mywidgetcolor.value == 'green':
self.xsR.append(event.xdata)
self.ysR.append(event.ydata)
if (not currently_dragging) or (currently_dragging and self.dragging_timer == 0):
self.lineR.set_data(self.xsR, self.ysR)
#self.lineR.figure.canvas.draw()
if self.mywidgetcolor.value == 'blue':
self.xsB.append(event.xdata)
self.ysB.append(event.ydata)
if (not currently_dragging) or (currently_dragging and self.dragging_timer == 0):
self.lineB.set_data(self.xsB, self.ysB)
#self.lineB.figure.canvas.draw()
#if self.dragging_timer == 0:
# self.lineR.figure.canvas.draw()
def clear(self, button):
if self.trained == False:
with self.my_out:
print('can only reset if trained')
return
with self.my_out:
print('resetted the widget')
self.trained = False
self.remove_decision_boundary()
self.C1 = None
self.C2 = None
self.model = None
self.xsR = []
self.ysR = []
self.xsB = []
self.ysB = []
self.lineR.set_data(self.xsR, self.ysR)
self.lineB.set_data(self.xsB, self.ysB)
self.lineB.figure.canvas.draw()
self.lineR.figure.canvas.draw()
def export(self):
dataR = np.array([self.xsR,self.ysR]).transpose()
dataB = np.array([self.xsB,self.ysB]).transpose()
yR = np.ones((dataR.shape[0], 1))
yB = -np.ones((dataB.shape[0], 1))
X = np.concatenate((dataR,dataB))
y = np.concatenate((yR,yB))
y = np.reshape(y,y.shape[0])
return (X,y)
def train(self, button):
self.my_out.clear_output()
if len(self.xsR) < 1 or len(self.xsB) < 1:
with self.my_out:
print('need at least one object in both classes to train')
return
(X,y) = self.export()
if self.need_seperable:
C = float('inf')
else:
C = self.widgetC.value
model = sklearn.svm.LinearSVC(loss='hinge',C=C)
model.fit(X,y)
if self.need_seperable:
acc = model.score(X,y)
if acc < 0.99999:
with self.my_out:
print('this dataset is not seperable')
return
self.remove_decision_boundary()
train_error = model.score(X,y)
(C1, C2) = plot_decision_boundary_margin(X,y,model)
self.C1 = C1
self.C2 = C2
self.model = model
self.trained = True
with self.my_out:
if self.need_seperable:
print('trained hard margin SVM')
else:
print('trained soft margin SVM with C %f' % C)
def init(need_seperable = True):
# Turn off interactivity, for now
plt.ioff()
fig = plt.figure(figsize = (4,4))
ax = fig.add_subplot(111)
# Make some nice axes
ax.set_xlim(-3, 3)
ax.set_ylim(-3, 3)
ax.set_title('click to add points')
ax.set_xlabel('Feature 1')
ax.set_ylabel('Feature 2')
# Remove some stuff from the backend
#fig.canvas.toolbar_visible = False # Hide toolbar
#fig.canvas.header_visible = False # Hide the Figure name at the top of the figure
#fig.canvas.footer_visible = False
#fig.canvas.resizable = False
# These items will contain the objects
lineR, = ax.plot([], [], linestyle="none", marker="s", color="g", markersize=10)
lineB, = ax.plot([], [], linestyle="none", marker="^", color="b", markersize=10)
# Make the GUI
w_clear = widgets.Button(
description='Clear all',
disabled=False,
button_style='danger', # 'success', 'info', 'warning', 'danger' or ''
tooltip='Remove all data and start from scratch',
icon='check' # (FontAwesome names without the `fa-` prefix)
)
w_color = widgets.ToggleButtons(
options=['green', 'blue'],
description='Class:',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltips=['Description of slow', 'Description of regular'],
# icons=['check'] * 3
)
if not need_seperable:
w_C = widgets.FloatLogSlider(
value=1,
base=10,
min=-10, # max exponent of base
max=10, # min exponent of base
step=0.2, # exponent step
#description='Log Slider',
description='C:',
continuous_update=False,
orientation='horizontal',
readout=True,
#readout_format='.2f',
)
else:
w_C = None
w_train = widgets.Button(
description='Train SVM',
disabled=False,
button_style='warning', # 'success', 'info', 'warning', 'danger' or ''
tooltip='...',
icon='check' # (FontAwesome names without the `fa-` prefix)
)
out = widgets.Output(layout={'border': '1px solid black'})
out.layout.height = '40px'
out.layout.width = '600px'
if need_seperable:
b1 = widgets.HBox([w_color,w_train])
bar = widgets.VBox([b1, out])
else:
b1 = widgets.HBox([w_color,w_C,w_train])
#b2 = widgets.HBox([w_train,w_C])
bar = widgets.VBox([b1, out])
linebuilder = LineBuilder2(lineR, lineB, w_color, w_C, out, need_seperable)
w_clear.on_click(linebuilder.clear)
w_train.on_click(linebuilder.train)
# Turn interactivity back on
plt.ion()
out = fig
ui = bar
return display(ui, out)
为了开始交互，我在 Jupyter notebook 中使用了以下内容：
%matplotlib notebook
from svm_helper import init
init()
到目前为止，我发现在以 %matplotlib notebook 开头的单元格上方的单元格中添加以下代码（来自 here）有效：
%%html
<style>
.output_wrapper button.btn.btn-default,
.output_wrapper .ui-dialog-titlebar {
display: none;
}
</style>
可能不理想，因为你必须解释为什么他们必须 运行 这样做，而不是向你的学生解释忽略按钮，但这很重要。