column shows the 3 methanol-based pairs. Each plot will look like your plot for Exercise 1 above: it should includ ellipses for fermented and unfermented. Each subplot should have its own axis labels and title. In order to keep your code from exploding, you should create a function whose inputs are two columns of data, v two features. You will then call this function 6 times to produce the 6 subplots. import matplotlib.pyplot as plt xf= df_fer[['TPC-MECH']] |yf= df_fer[['TEAC-MEOH¹]] xnf = df_nf[['TPC-MEOH']] ynf = df_nf[['TEAC-MEOH']]
column shows the 3 methanol-based pairs. Each plot will look like your plot for Exercise 1 above: it should includ ellipses for fermented and unfermented. Each subplot should have its own axis labels and title. In order to keep your code from exploding, you should create a function whose inputs are two columns of data, v two features. You will then call this function 6 times to produce the 6 subplots. import matplotlib.pyplot as plt xf= df_fer[['TPC-MECH']] |yf= df_fer[['TEAC-MEOH¹]] xnf = df_nf[['TPC-MEOH']] ynf = df_nf[['TEAC-MEOH']]
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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How do you fix this error. Or is there another way to plot six confidence ellipse in 3x2 grid of plots using subplot (IN PYTHON)
![Create a 3 x 2 grid of plots (using the subplot command from matplotlib), where the first column of plots shows the 3 water-based pairs and the second
column shows the 3 methanol-based pairs. Each plot will look like your plot for Exercise 1 above: it should include the scattered points as well as confidence
ellipses for fermented and unfermented. Each subplot should have its own axis labels and title.
In order to keep your code from exploding, you should create a function whose inputs are two columns of data, which produces the plot corresponding to those
two features. You will then call this function 6 times to produce the 6 subplots.
import matplotlib.pyplot as plt
xf= df_fer [['TPC-MEOH']]
yf= df_fer[['TEAC-MEOH']]
xnf = df_nf[[ TPC-MEOH']]
ynf = df_nf[['TEAC-MEOH']]
#Answer to Exercise 4 here
import numpy as np # 'np' is the prefix that will identify nump packages
from source.ellipses import confidence_ellipse # for representing the correlation (for def draw_confidence_ellipse2(xf, yf,
xfa = np.array(xf).flatten()
yfa= np.array(yf).flatten()
xnfa = np.array(xnf).flatten()
ynfa = np.array(ynf).flatten()
ax = plt.subplots()
plt.scatter(xfa, yfa, s-6, color = 'blue')
confidence_ellipse (xfa, yfa, ax, n_std=1, label='FR', edgecolor='blue', linestyle='--')
plt.scatter (xnfa, ynfa, s-6, color = 'orange')
confidence_ellipse (xnfa, ynfa, ax, n_std=1, label='NF', edgecolor="orange', linestyle='--')
ax.set_title('title', fontsize =16, fontweight = "bold")
ax.set_xlabel('x1', fontweight ='bold', fontsize =16)
ax.set_ylabel('yl', fontweight ='bold', fontsize =16)
ax.legend (prop={"size":14})
f_tpc_m= df_fer [['TPC-MEOH']]
f_teac_m= df_fer [['TEAC-MEOH']]
f_frap_m= df_fer [['FRAP-MECH']]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F892e817a-9b32-4eeb-b8fc-5dd7ffde6479%2Fe778a127-e9d1-4974-8f4c-9eb8fba79043%2Fcw8ct3rm_processed.png&w=3840&q=75)
Transcribed Image Text:Create a 3 x 2 grid of plots (using the subplot command from matplotlib), where the first column of plots shows the 3 water-based pairs and the second
column shows the 3 methanol-based pairs. Each plot will look like your plot for Exercise 1 above: it should include the scattered points as well as confidence
ellipses for fermented and unfermented. Each subplot should have its own axis labels and title.
In order to keep your code from exploding, you should create a function whose inputs are two columns of data, which produces the plot corresponding to those
two features. You will then call this function 6 times to produce the 6 subplots.
import matplotlib.pyplot as plt
xf= df_fer [['TPC-MEOH']]
yf= df_fer[['TEAC-MEOH']]
xnf = df_nf[[ TPC-MEOH']]
ynf = df_nf[['TEAC-MEOH']]
#Answer to Exercise 4 here
import numpy as np # 'np' is the prefix that will identify nump packages
from source.ellipses import confidence_ellipse # for representing the correlation (for def draw_confidence_ellipse2(xf, yf,
xfa = np.array(xf).flatten()
yfa= np.array(yf).flatten()
xnfa = np.array(xnf).flatten()
ynfa = np.array(ynf).flatten()
ax = plt.subplots()
plt.scatter(xfa, yfa, s-6, color = 'blue')
confidence_ellipse (xfa, yfa, ax, n_std=1, label='FR', edgecolor='blue', linestyle='--')
plt.scatter (xnfa, ynfa, s-6, color = 'orange')
confidence_ellipse (xnfa, ynfa, ax, n_std=1, label='NF', edgecolor="orange', linestyle='--')
ax.set_title('title', fontsize =16, fontweight = "bold")
ax.set_xlabel('x1', fontweight ='bold', fontsize =16)
ax.set_ylabel('yl', fontweight ='bold', fontsize =16)
ax.legend (prop={"size":14})
f_tpc_m= df_fer [['TPC-MEOH']]
f_teac_m= df_fer [['TEAC-MEOH']]
f_frap_m= df_fer [['FRAP-MECH']]
![f_tpc_n = df_fer[['TPC-MECH']]
f_teac_n = df_fer [['TEAC-MEOH"]]
f_frap_m= df_fer [['FRAP-MECH']]
f_tpc_h= df_fer[['TPC-H20']]
f_teac_h= df_fer[['TEAC-H20']]
f_frap_h= df_fer[['FRAP-H20']]
nf_tpc_n = df_nf[['TPC-MECH']]
nf_teac_n = df_nf[['TEAC-MECH']]
nf_frap_m= df_nf[['FRAP-MECH']]
nf_tpc_h= df_nf[['TPC-H20']]
nf_teac_h= df_nf[['TEAC-H20']]
nf_frap_h= df_nf[['FRAP-H20']]
fig, ax = plt.subplots(3, 2, figsize=(24, 24))
draw_confidence_ellipse2(f_tpc_h, f_teac_h, nf_tpc_h, nf_teac_h, 2, ax[0,0], "TPC", "TEAC", "TPC-H20 vs TEAC-H20")
draw_confidence_ellipse2(f_tpc_h, f_frap_h, nf_tpc_h, nf_frap_h, 2, ax[1,0], "TPC", "FRAP", "TPC-H20 vs FRAP-H20")
draw_confidence_ellipse2(f_frap_h, f_teac_h, nf_frap_h, nf_teac_h, 2, ax[2,0], "FRAP", "TEAC", "FRAP-H20 vs TEAC-H20")
draw_confidence_ellipse2(f_tpc_m,
draw_confidence_ellipse2(f_tpc_m,
draw_confidence_ellipse2(f_frap_n,
4
f_teac_m, nf_tpc_m, nf_teac_m, 2, ax[0,1], "TPC", "TEAC", "TPC-MEOH vs TEAC-MEOH")
f_frap_m, nf_tpc_m, nf_frap_m, 2, ax[1,1], "TPC", "FRAP", "TPC-MEOH vs FRAP-MEOH")
f_teac_m, nf_frap_n, nf_teac_m, 2, ax[2,1], "FRAP", "TEAC", "FRAP-MEOH vs TEAC-MEOH"
AttributeError
Input In [26], in <cell line: 13>()
plt.subplots()
Traceback (most recent call last)
10 ax
12 plt.scatter(xfa, yfa, s=6, color = 'blue')
---> 13 confidence_ellipse(xfa, yfa, ax, n_std=1, label='FR", edgecolor='blue', linestyle='--')
15 plt.scatter(xnfa, ynfa, s=6, color="orange")
16 confidence_ellipse(xnfa, ynfa, ax, n_std=1, label='NF", edgecolor='orange', linestyle='--')
File -\source\ellipses.py:58, in confidence_ellipse(x, y, ax, n_std, facecolor, **kwargs)
51 mean y = np.mean(y)
53 transf = transforms.Affine2D() \
54
.rotate_deg (45) \
55
.scale(scale_x, scale_y) \
.translate(mean_x, nean_y)
56
--> 58 ellipse.set_transform(transf + ax. transData)
59 return ax.add_patch(ellipse)
AttributeError: 'tuple' object has no attribute 'transData'](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F892e817a-9b32-4eeb-b8fc-5dd7ffde6479%2Fe778a127-e9d1-4974-8f4c-9eb8fba79043%2F4lw5ptg_processed.png&w=3840&q=75)
Transcribed Image Text:f_tpc_n = df_fer[['TPC-MECH']]
f_teac_n = df_fer [['TEAC-MEOH"]]
f_frap_m= df_fer [['FRAP-MECH']]
f_tpc_h= df_fer[['TPC-H20']]
f_teac_h= df_fer[['TEAC-H20']]
f_frap_h= df_fer[['FRAP-H20']]
nf_tpc_n = df_nf[['TPC-MECH']]
nf_teac_n = df_nf[['TEAC-MECH']]
nf_frap_m= df_nf[['FRAP-MECH']]
nf_tpc_h= df_nf[['TPC-H20']]
nf_teac_h= df_nf[['TEAC-H20']]
nf_frap_h= df_nf[['FRAP-H20']]
fig, ax = plt.subplots(3, 2, figsize=(24, 24))
draw_confidence_ellipse2(f_tpc_h, f_teac_h, nf_tpc_h, nf_teac_h, 2, ax[0,0], "TPC", "TEAC", "TPC-H20 vs TEAC-H20")
draw_confidence_ellipse2(f_tpc_h, f_frap_h, nf_tpc_h, nf_frap_h, 2, ax[1,0], "TPC", "FRAP", "TPC-H20 vs FRAP-H20")
draw_confidence_ellipse2(f_frap_h, f_teac_h, nf_frap_h, nf_teac_h, 2, ax[2,0], "FRAP", "TEAC", "FRAP-H20 vs TEAC-H20")
draw_confidence_ellipse2(f_tpc_m,
draw_confidence_ellipse2(f_tpc_m,
draw_confidence_ellipse2(f_frap_n,
4
f_teac_m, nf_tpc_m, nf_teac_m, 2, ax[0,1], "TPC", "TEAC", "TPC-MEOH vs TEAC-MEOH")
f_frap_m, nf_tpc_m, nf_frap_m, 2, ax[1,1], "TPC", "FRAP", "TPC-MEOH vs FRAP-MEOH")
f_teac_m, nf_frap_n, nf_teac_m, 2, ax[2,1], "FRAP", "TEAC", "FRAP-MEOH vs TEAC-MEOH"
AttributeError
Input In [26], in <cell line: 13>()
plt.subplots()
Traceback (most recent call last)
10 ax
12 plt.scatter(xfa, yfa, s=6, color = 'blue')
---> 13 confidence_ellipse(xfa, yfa, ax, n_std=1, label='FR", edgecolor='blue', linestyle='--')
15 plt.scatter(xnfa, ynfa, s=6, color="orange")
16 confidence_ellipse(xnfa, ynfa, ax, n_std=1, label='NF", edgecolor='orange', linestyle='--')
File -\source\ellipses.py:58, in confidence_ellipse(x, y, ax, n_std, facecolor, **kwargs)
51 mean y = np.mean(y)
53 transf = transforms.Affine2D() \
54
.rotate_deg (45) \
55
.scale(scale_x, scale_y) \
.translate(mean_x, nean_y)
56
--> 58 ellipse.set_transform(transf + ax. transData)
59 return ax.add_patch(ellipse)
AttributeError: 'tuple' object has no attribute 'transData'
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