matplotlib: ejes insertados para múltiples diagramas de caja

Question

Tengo algunos diagramas de caja en matplotlib que quiero ampliar en un rango y particular ([0,0.1]) usando inset axes. No está claro para mí desde el example en la documentación, cómo debería hacer esto para múltiples diagramas de caja en la misma figura. Estaba intentando modificar el código proporcionado en este ejemplo, pero había demasiada complejidad innecesaria. Mi código es bastante simple:

# dataToPlot is a list of lists, containing some data. 
plt.figure() 
plt.boxplot(dataToPlot) 
plt.savefig('image.jpeg', bbox_inches=0) 

¿Cómo agrego ejes inserción y el zoom en el primer diagrama de caja de los dos? ¿Cómo puedo hacerlo para ambos?

EDIT: He probado el código de abajo, pero aquí es lo que tengo:

lo que salió mal?

# what's the meaning of these two parameters? 
fig = plt.figure(1, [5,4]) 
# what does 111 mean? 
ax = fig.add_subplot(111) 
ax.boxplot(data) 
# ax.set_xlim(0,21) # done automatically based on the no. of samples, right? 
# ax.set_ylim(0,300) # done automatically based on max value in my samples, right? 
# Create the zoomed axes 
axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6, location = 1 (upper right) 
axins.boxplot(data) 
# sub region of the original image 
#here I am selecting the first boxplot by choosing appropriate values for x1 and x2 
# on the y-axis, I'm selecting the range which I want to zoom in, right? 
x1, x2, y1, y2 = 0.9, 1.1, 0.0, 0.01 
axins.set_xlim(x1, x2) 
axins.set_ylim(y1, y2) 
# even though it's false, I still see all numbers on both axes, how do I remove them? 
plt.xticks(visible=False) 
plt.yticks(visible=False) 
# draw a bbox of the region of the inset axes in the parent axes and 
# connecting lines between the bbox and the inset axes area 
# what are fc and ec here? where do loc1 and loc2 come from? 
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") 
plt.savefig('img.jpeg', bbox_inches=0) 

Answer 1

El loc determina la ubicación del eje ampliada, 1 para upper right, 2 para upper left y así sucesivamente. Modifiqué el código de ejemplo ligeramente para generar múltiples ejes ampliados.

import matplotlib.pyplot as plt 

from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes 
from mpl_toolkits.axes_grid1.inset_locator import mark_inset 

import numpy as np 

def get_demo_image(): 
    from matplotlib.cbook import get_sample_data 
    import numpy as np 
    f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False) 
    z = np.load(f) 
    # z is a numpy array of 15x15 
    return z, (-3,4,-4,3) 


fig = plt.figure(1, [5,4]) 
ax = fig.add_subplot(111) 

# prepare the demo image 
Z, extent = get_demo_image() 
Z2 = np.zeros([150, 150], dtype="d") 
ny, nx = Z.shape 
Z2[30:30+ny, 30:30+nx] = Z 

# extent = [-3, 4, -4, 3] 
ax.imshow(Z2, extent=extent, interpolation="nearest", 
      origin="lower") 

axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6 
axins.imshow(Z2, extent=extent, interpolation="nearest", 
      origin="lower") 

# sub region of the original image 
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 
axins.set_xlim(x1, x2) 
axins.set_ylim(y1, y2) 

axins1 = zoomed_inset_axes(ax, 8, loc=2) # zoom = 6 
axins1.imshow(Z2, extent=extent, interpolation="nearest", 
      origin="lower") 

# sub region of the original image 
x1, x2, y1, y2 = -1.2, -0.9, -2.2, -1.9 
axins1.set_xlim(x1, x2) 
axins1.set_ylim(y1, y2) 

plt.xticks(visible=False) 
plt.yticks(visible=False) 

# draw a bbox of the region of the inset axes in the parent axes and 
# connecting lines between the bbox and the inset axes area 
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") 
mark_inset(ax, axins1, loc1=2, loc2=4, fc="none", ec="0.5") 

plt.draw() 
plt.show() 

Edit1:

Del mismo modo, también se puede añadir ejes ampliada en el diagrama de caja. Este es un ejemplo

from pylab import * 
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes 
from mpl_toolkits.axes_grid1.inset_locator import mark_inset 

# fake up some data 
spread= rand(50) * 100 
center = ones(25) * 50 
flier_high = rand(10) * 100 + 100 
flier_low = rand(10) * -100 
data =concatenate((spread, center, flier_high, flier_low), 0) 

# fake up some more data 
spread= rand(50) * 100 
center = ones(25) * 40 
flier_high = rand(10) * 100 + 100 
flier_low = rand(10) * -100 
d2 = concatenate((spread, center, flier_high, flier_low), 0) 
data.shape = (-1, 1) 
d2.shape = (-1, 1) 
data = [data, d2, d2[::2,0]] 

# multiple box plots on one figure 
fig = plt.figure(1, [5,4]) 
ax = fig.add_subplot(111) 
ax.boxplot(data) 
ax.set_xlim(0.5,5) 
ax.set_ylim(0,300) 

# Create the zoomed axes 
axins = zoomed_inset_axes(ax, 3, loc=1) # zoom = 3, location = 1 (upper right) 
axins.boxplot(data) 

# sub region of the original image 
x1, x2, y1, y2 = 0.9, 1.1, 125, 175 
axins.set_xlim(x1, x2) 
axins.set_ylim(y1, y2) 
plt.xticks(visible=False) 
plt.yticks(visible=False) 

# draw a bbox of the region of the inset axes in the parent axes and 
# connecting lines between the bbox and the inset axes area 
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") 

show() 

Edit2

En caso de que la distribución es heterogénea, es decir, la mayoría de los valores son pequeños, con pocos valores muy grandes, el procedimiento de zoom anterior podría no funciona, ya que ampliará tanto el x como el eje y. En ese caso, es mejor cambiar la escala de y-axis a log.

from pylab import * 

# fake up some data 
spread= rand(50) * 1 
center = ones(25) * .5 
flier_high = rand(10) * 100 + 100 
flier_low = rand(10) * -100 
data =concatenate((spread, center, flier_high, flier_low), 0) 

# fake up some more data 
spread= rand(50) * 1 
center = ones(25) * .4 
flier_high = rand(10) * 100 + 100 
flier_low = rand(10) * -100 
d2 = concatenate((spread, center, flier_high, flier_low), 0) 
data.shape = (-1, 1) 
d2.shape = (-1, 1) 
data = [data, d2, d2[::2,0]] 

# multiple box plots on one figure 
fig = plt.figure(1, [5,4]) # Figure Size 
ax = fig.add_subplot(111) # Only 1 subplot 
ax.boxplot(data) 
ax.set_xlim(0.5,5) 
ax.set_ylim(.1,300) 
ax.set_yscale('log') 

show()