Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN: 9780133594140
Author: James Kurose, Keith Ross
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Related questions
Question
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)
![The text describes a visualization task using Python's Matplotlib to create a 3x2 grid of plots, organizing data into two main columns comparing water-based and methanol-based pairs. Each subplot will display scattered points and confidence ellipses to differentiate fermented and unfermented samples with distinct axis labels and titles. Here's the transcription and explanation:
### Code Snippet Explanation
#### Initial Setup
- Import required libraries:
```python
import matplotlib.pyplot as plt
import numpy as np
from source.ellipses import confidence_ellipse
```
- Load data for plotting:
```python
xf = df_fer[['TPC-MEOH']]
yf = df_fer[['TEAC-MEOH']]
xfna = df_nf[['TPC-MEOH']]
ynfna = df_nf[['TEAC-MEOH']]
```
#### Plotting Function
1. **Prepare Data:**
- Flatten arrays for plotting:
```python
xfa = np.array(xf).flatten()
yfa = np.array(yf).flatten()
xfna = np.array(xfna).flatten()
ynfna = np.array(ynfna).flatten()
```
2. **Create Subplots:**
- Initialize subplots:
```python
ax = plt.subplots()
```
3. **Plot Data Points and Confidence Ellipses:**
- Plot fermented data:
```python
plt.scatter(xfa, yfa, s=6, color='blue')
confidence_ellipse(xfa, yfa, ax, n_std=1, label='F', edgecolor='blue', linestyle='--')
```
- Plot unfermented data:
```python
plt.scatter(xfna, ynfna, s=6, color='orange')
confidence_ellipse(xfna, ynfna, ax, n_std=1, label='NF', edgecolor='orange', linestyle='--')
```
4. **Format Plot:**
- Set the title and labels:
```python
ax.set_title('Title', fontsize=16, fontweight='bold')
ax.set_xlabel('x1', fontweight='bold', fontsize=16)
ax.set_ylabel('y1', fontweight='bold', fontsize=16)
```
- Legend and label size:
```python](https://content.bartleby.com/qna-images/question/892e817a-9b32-4eeb-b8fc-5dd7ffde6479/e778a127-e9d1-4974-8f4c-9eb8fba79043/cw8ct3rm_processed.png)
Transcribed Image Text:The text describes a visualization task using Python's Matplotlib to create a 3x2 grid of plots, organizing data into two main columns comparing water-based and methanol-based pairs. Each subplot will display scattered points and confidence ellipses to differentiate fermented and unfermented samples with distinct axis labels and titles. Here's the transcription and explanation:
### Code Snippet Explanation
#### Initial Setup
- Import required libraries:
```python
import matplotlib.pyplot as plt
import numpy as np
from source.ellipses import confidence_ellipse
```
- Load data for plotting:
```python
xf = df_fer[['TPC-MEOH']]
yf = df_fer[['TEAC-MEOH']]
xfna = df_nf[['TPC-MEOH']]
ynfna = df_nf[['TEAC-MEOH']]
```
#### Plotting Function
1. **Prepare Data:**
- Flatten arrays for plotting:
```python
xfa = np.array(xf).flatten()
yfa = np.array(yf).flatten()
xfna = np.array(xfna).flatten()
ynfna = np.array(ynfna).flatten()
```
2. **Create Subplots:**
- Initialize subplots:
```python
ax = plt.subplots()
```
3. **Plot Data Points and Confidence Ellipses:**
- Plot fermented data:
```python
plt.scatter(xfa, yfa, s=6, color='blue')
confidence_ellipse(xfa, yfa, ax, n_std=1, label='F', edgecolor='blue', linestyle='--')
```
- Plot unfermented data:
```python
plt.scatter(xfna, ynfna, s=6, color='orange')
confidence_ellipse(xfna, ynfna, ax, n_std=1, label='NF', edgecolor='orange', linestyle='--')
```
4. **Format Plot:**
- Set the title and labels:
```python
ax.set_title('Title', fontsize=16, fontweight='bold')
ax.set_xlabel('x1', fontweight='bold', fontsize=16)
ax.set_ylabel('y1', fontweight='bold', fontsize=16)
```
- Legend and label size:
```python
Transcribed Image Text:### Code Overview
The image displays a Python script utilizing libraries such as `pandas` and `matplotlib` for data manipulation and visualization.
#### Code Explanation:
1. **Data Selection:**
- Data is extracted from a DataFrame `df_ferl`, with specific slices for 'TPC-MEOH', 'TEAC-MEOH', 'FRAP-MEOH', and similarly for another DataFrame `df_nf` with 'TPC-H2O', 'TEAC-H2O', 'FRAP-H2O'.
2. **Data Mapping:**
- Specific columns are selected from the DataFrames to separate the TPC, TEAC, and FRAP values for 'ME-OH' and 'H2O' treatments.
3. **Plotting:**
- A figure with a size of `(24, 24)` is created, and six subplots are defined in a 2x3 grid.
- The function `draw_confidence_ellipse` is used to plot confidence ellipses for various data selections, juxtaposing 'MEOH' and 'H2O' treatments for TPC, TEAC, and FRAP.
4. **Scatter Plots:**
- Blue and orange scatter plots are created with labels 'FR' and 'NF', indicating different datasets.
5. **Error Handling:**
- An `AttributeError` is raised, indicating that a `tuple` object lacks the attribute `transData`. This error occurs on line 58 of the code in the `confidence_ellipse` function, specifically when attempting to call `ax.transData`.
### Error Explanation
- **AttributeError:**
- This error suggests that the code is trying to use an attribute or method that is not available for the specified object. The problem occurs in the `confidence_ellipse` function when trying to reference `transData` from `ax`, which should be replaced by `ax.transData`.
### Diagram Description
There is no diagram visible in the code snippet itself, but the goal is to visualize statistical data distributions using ellipses on `matplotlib` plots, thus identifying relationships or variances between different treatments (MEOH, H2O) in TPC, TEAC, and FRAP indices.
### Conclusion:
The script aims to visualize and compare biochemical data sets by plotting confidence ellipses on scatter plots. Addressing the `AttributeError` by ensuring correct attribute methods should
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by stepSolved in 2 steps
Knowledge Booster
Similar questions
- Explain the Cohen Sutherland lines algorithmarrow_forwardUSING PYTHON NOT MATLAB We can use the sum of the first n terms of the Taylor series of e x with a = 0 to estimate the value of the exponential function. Using a logarithmic y axis, plot the fractional error in the estimated e x (compared to numpy.exp(x)) as a function of n (n ranging between 1 and 80) for (a) x = 15 and (b) x = −15. [You can plot the two cases either as different curves on the same plot or in different plots, whichever is clearer.] Discuss the contributions of truncation and roundoff to the fractional error for the different values of x and n. Also discuss how you could reduce truncation and roundoff errors in order to estimate e x more accurately using the Taylor series.arrow_forwardPlease keep the working neat.arrow_forward
- Solve the following nonlinear equations by Newton iteration and fixed point with initial conjecture x, = yo = 1.2. And get the answer using Python code with an accuracy of 3 significant digits. Sy = -x² +x + 0.75 ly + 5xy = 22 %3Darrow_forwardSolve in python. Expected ouput is 9arrow_forwardUse Matlab to find the roots of the following polynomials: (a) (b) P₁ = 2s5 - 3s4 + 4s³ - s² + 3s P₂ s6 + 5s³ s² - 10 P2 =arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
- Computer Networking: A Top-Down Approach (7th Edi...Computer EngineeringISBN:9780133594140Author:James Kurose, Keith RossPublisher:PEARSON
Computer Organization and Design MIPS Edition, Fi...Computer EngineeringISBN:9780124077263Author:David A. Patterson, John L. HennessyPublisher:Elsevier Science
Network+ Guide to Networks (MindTap Course List)Computer EngineeringISBN:9781337569330Author:Jill West, Tamara Dean, Jean AndrewsPublisher:Cengage Learning 
Concepts of Database ManagementComputer EngineeringISBN:9781337093422Author:Joy L. Starks, Philip J. Pratt, Mary Z. LastPublisher:Cengage Learning
Prelude to ProgrammingComputer EngineeringISBN:9780133750423Author:VENIT, StewartPublisher:Pearson Education
Sc Business Data Communications and Networking, T...Computer EngineeringISBN:9781119368830Author:FITZGERALDPublisher:WILEY
Computer Networking: A Top-Down Approach (7th Edi...
Computer Engineering
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:PEARSON
Computer Organization and Design MIPS Edition, Fi...
Computer Engineering
ISBN:9780124077263
Author:David A. Patterson, John L. Hennessy
Publisher:Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:9781337569330
Author:Jill West, Tamara Dean, Jean Andrews
Publisher:Cengage Learning
Concepts of Database Management
Computer Engineering
ISBN:9781337093422
Author:Joy L. Starks, Philip J. Pratt, Mary Z. Last
Publisher:Cengage Learning
Prelude to Programming
Computer Engineering
ISBN:9780133750423
Author:VENIT, Stewart
Publisher:Pearson Education
Sc Business Data Communications and Networking, T...
Computer Engineering
ISBN:9781119368830
Author:FITZGERALD
Publisher:WILEY