FIG. 3 is a timing diagram illustrating the operation of the pulse width measurement 16 of the present invention. At top the output pulse from AND gate 24 appears and this is a 125 KHz pulse train with pulse width equal to the delay through the delay chain. The 32/33 Mhz clock 12 appears at center. As shown at bottom, the top output pulse from AND gate 24 is sampled by the 32/33 Mhz clock 12 for a fixed period of, for example, one second. This is done by the pulse width measurement block 16. The pulse width measurement block 16 then counts the sampled pulse train to determine the pulse width, and this is output to a hexadecimal display 28 (driven by a display driver 26). The visual readout from the display 28 is thereby updated continuously to display the propagation delay through the delay chain.翻译
时间: 2024-05-04 21:18:26 浏览: 22
图3是一个时序图,说明了本发明的脉冲宽度测量器16的操作。图中顶部出现了AND门24的输出脉冲,它是一个125kHz的脉冲列,脉冲宽度等于延迟链中的延迟。中间显示了32/33MHz时钟12。如底部所示,通过固定时间(例如1秒钟)内,32/33MHz时钟12对AND门24的顶部输出脉冲进行采样。脉冲宽度测量器16随后对采样到的脉冲列进行计数,以确定脉冲宽度,并将其输出到一个十六进制显示器28(由显示驱动器26驱动)。因此,显示器28的视觉读数被连续更新,以显示延迟链的传播延迟。
相关问题
. Fig. 1 (a) depicts schematic diagrams of Tucker decomposition and the singular value curves in three unfolding modes.什么意思
Fig. 1 (a) 指的是图1中的a部分,其中描述了Tucker分解的示意图以及三种展开模式下的奇异值曲线。Tucker分解是一种高阶张量的分解方法,它将一个高阶张量分解为一个核张量与多个因子矩阵的乘积形式。在三种不同的展开模式下,对应的因子矩阵的大小和奇异值的数量不同,因此,对应的奇异值曲线也不同。这些奇异值曲线反映了不同模式下的数据重要性和分解的效果。因此,通过分析这些奇异值曲线,可以更好地了解Tucker分解的效果和应用场景。
Open the data file using pandas and push data in numpy.array for plotting hydraulic fracturing operation diagram using matplotlib. You should obtain 4 figures something like.
Sure! To open the data file using pandas and plot the hydraulic fracturing operation diagram using matplotlib, you can follow the steps below:
1. Install the required libraries if you haven't already:
```python
pip install pandas matplotlib
```
2. Import the necessary libraries:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
3. Load the data file using pandas:
```python
data = pd.read_excel('path/to/data_file.xlsx')
```
Replace `'path/to/data_file.xlsx'` with the actual path to your data file.
4. Extract the necessary columns from the data:
Assuming your data file contains columns named `'Stage'`, `'Time'`, `'Pressure'`, and `'Rate'`, you can extract them as follows:
```python
stage = data['Stage']
time = data['Time']
pressure = data['Pressure']
rate = data['Rate']
```
5. Plot the hydraulic fracturing operation diagram:
```python
fig, axs = plt.subplots(2, 2, figsize=(12, 8))
axs[0, 0].plot(stage, pressure)
axs[0, 0].set_xlabel('Stage')
axs[0, 0].set_ylabel('Pressure')
axs[0, 1].plot(stage, rate)
axs[0, 1].set_xlabel('Stage')
axs[0, 1].set_ylabel('Rate')
axs[1, 0].plot(time, pressure)
axs[1, 0].set_xlabel('Time')
axs[1, 0].set_ylabel('Pressure')
axs[1, 1].plot(time, rate)
axs[1, 1].set_xlabel('Time')
axs[1, 1].set_ylabel('Rate')
plt.tight_layout()
plt.show()
```
The above code will create a 2x2 grid of subplots, where each subplot represents a different combination of `'Stage'`, `'Time'`, `'Pressure'`, and `'Rate'`. Adjust the size of the figure (`figsize`) as needed.
Make sure to replace `'Stage'`, `'Time'`, `'Pressure'`, and `'Rate'` with the actual column names in your data file.
Hope this helps! Let me know if you have any further questions.
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