我有一个大型数据集，我希望能够＆＃34;缩放＆＃34;在。
我真正想要的是根据选择重新绑定数据，然后更新图表中的数据。因此图表将显示不同的限制但保持相同的分辨率。目前，imshow尚未更新。必须有一些我不知道的东西，或者不理解任何洞察力都会受到赞赏。
随机数据工作示例：
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
class Annotate(object):
def __init__(self,x,y):
self.ax = plt.gca()
self.x = x
self.y = y
self.rect = Rectangle((0,0), 0, 0, facecolor='None', edgecolor='red')
self.im = plt.imshow(np.zeros((150,150)))
self.x0 = None
self.y0 = None
self.x1 = None
self.y1 = None
self.ax.add_patch(self.rect)
self.ax.figure.canvas.mpl_connect('button_press_event', self.on_press)
self.ax.figure.canvas.mpl_connect('button_release_event', self.on_release)
self.ax.figure.canvas.mpl_connect('motion_notify_event', self.on_motion)
def on_press(self, event):
print ('press')
self.is_pressed = True
self.x0 = event.xdata
self.y0 = event.ydata
self.x1 = event.xdata
self.y1 = event.ydata
self.rect.set_width(self.x1 - self.x0)
self.rect.set_height(self.y1 - self.y0)
self.rect.set_xy((self.x0, self.y0))
#self.rect.set_linestyle('dashed')
self.ax.figure.canvas.draw()
def on_motion(self,event):
if self.on_press is True:
return
self.x1 = event.xdata
self.y1 = event.ydata
self.rect.set_width(self.x1 - self.x0)
self.rect.set_height(self.y1 - self.y0)
self.rect.set_xy((self.x0, self.y0))
#self.rect.set_linestyle('dashed')
if self.is_pressed == True:
self.ax.figure.canvas.draw()
def on_release(self, event):
print ('release')
self.is_pressed= False
self.x1 = event.xdata
self.y1 = event.ydata
self.rect.set_width(self.x1 - self.x0)
self.rect.set_height(self.y1 - self.y0)
self.rect.set_xy((self.x0, self.y0))
self.rect.set_linestyle('solid')
update_data(self)
#self.ax.set_ylim( min(self.y0, self.y1), max(self.y0, self.y1))
#self.ax.set_xlim( min(self.x0, self.x1), max(self.x0, self.x1))
self.ax.figure.canvas.draw()
print (self.x0,self.x1,self.y0,self.y1)
return [self.x0,self.x1,self.y0,self.y1]
#x = np.random.randint(0,500, size=10000)
x= np.arange(10000)
y = np.random.randint(0,500, size=10000)
Z, xedges, yedges = np.histogram2d(x,y, bins=150)
def update_data(selection):
ymin= min(selection.y0, selection.y1)
ymax= max(selection.y0, selection.y1)
xmin= min(selection.x0, selection.x1)
xmax= max(selection.x0, selection.x1)
low_index = np.argmax(selection.x>xmin)
high_index = np.argmax(selection.y>xmax)
Z, xedges, yedges = np.histogram2d(x[low_index:high_index],y, bins=150)
selection.im.set_data(Z)
selection.im.set_extent([xmin,xmax,ymin,ymax])
plt.draw()
selection = Annotate(x,y)
selection.im=plt.imshow(Z, interpolation='none', extent=[0,500,0,500])
plt.colorbar()
plt.show()
答案 0 :(得分：1)
您似乎错过了y中直方图重绘中update_data值的限制。高指数和低指数也是错误的方法。以下看起来更有希望，
Z, xedges, yedges = np.histogram2d(x[high_index:low_index],y[high_index:low_index], bins=150)
（虽然我不确定它到底是你想要的）
编辑：以下内容似乎可以缩放和重绘。
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
class Annotate(object):
def __init__(self,x,y):
self.figure = plt.gcf()
self.ax = plt.gca()
self.x = x
self.y = y
self.rect = Rectangle((0,0), 0, 0, facecolor='None', edgecolor='red')
self.im = plt.imshow(np.zeros((150,150)))
self.x0 = None
self.y0 = None
self.x1 = None
self.y1 = None
self.is_pressed = False
self.ax.add_patch(self.rect)
self.ax.figure.canvas.mpl_connect('button_press_event', self.on_press)
self.ax.figure.canvas.mpl_connect('button_release_event', self.on_release)
self.ax.figure.canvas.mpl_connect('motion_notify_event', self.on_motion)
def on_press(self, event):
print ('press')
self.is_pressed = True
self.x0 = event.xdata
self.y0 = event.ydata
self.x1 = event.xdata
self.y1 = event.ydata
self.rect.set_width(self.x1 - self.x0)
self.rect.set_height(self.y1 - self.y0)
self.rect.set_xy((self.x0, self.y0))
#self.rect.set_linestyle('dashed')
self.ax.figure.canvas.draw()
def on_motion(self,event):
if self.is_pressed is False:
return
self.x1 = event.xdata
self.y1 = event.ydata
#if type(self.x1) is float and type(self.y1) is float:
self.rect.set_width(self.x1 - self.x0)
self.rect.set_height(self.y1 - self.y0)
self.rect.set_xy((self.x0, self.y0))
#self.rect.set_linestyle('dashed')
if self.is_pressed == True:
self.ax.figure.canvas.draw()
def on_release(self, event):
print ('release')
self.is_pressed= False
self.x1 = event.xdata
self.y1 = event.ydata
self.rect.set_width(self.x1 - self.x0)
self.rect.set_height(self.y1 - self.y0)
self.rect.set_xy((self.x0, self.y0))
self.rect.set_linestyle('solid')
update_data(self)
#self.ax.set_ylim( min(self.y0, self.y1), max(self.y0, self.y1))
#self.ax.set_xlim( min(self.x0, self.x1), max(self.x0, self.x1))
self.ax.figure.canvas.draw()
print (self.x0,self.x1,self.y0,self.y1)
return [self.x0,self.x1,self.y0,self.y1]
#x = np.random.randint(0,500, size=10000)
x = np.arange(5000)
y = np.random.randint(0,500, size=5000)
Z, xedges, yedges = np.histogram2d(x, y, bins=150, normed=True)
def update_data(selection):
ymin= min(selection.y0, selection.y1)
ymax= max(selection.y0, selection.y1)
xmin= min(selection.x0, selection.x1)
xmax= max(selection.x0, selection.x1)
low_index = np.argmax(selection.x>xmin)
high_index = np.argmax(selection.y>xmax)
Z, xedges, yedges = np.histogram2d(x[high_index:low_index],y[high_index:low_index], bins=150, normed=True)
selection.ax.figure.clf(keep_observers=True)
selection.ax = selection.ax.figure.add_subplot(111)
selection.im=plt.imshow(Z, interpolation='none', extent=[xmin, xmax, ymin, ymax])
plt.colorbar(selection.im)
selection = Annotate(x,y)
selection.im=plt.imshow(Z, interpolation='none', extent=[y.min(), y.max(), y.min(), y.max()])
plt.colorbar(selection.im)
plt.show()