蹭下ChatGPT的热度哈哈

今天注册了个ChatGPT,感觉用处超级大，能写sci论文的那种大哈哈。当然，使用chatGPT写论文百分之九十会被查出来。但是我们可以用它来辅助写文章呀，翻译些句子呀，读不懂别人写的某句话可以让它转述一下呀。哈哈偷偷告诉你，要是有个大作业没时间写的话，可以用它偷偷懒哈哈（绝对不提倡）。

我测试了几个它的作用：

1.写程序

我试了一下，程序可以运行，运行出来得图片如下图，但比较简单，需要自己调整得更好看一些。

我追问了句，它调整了下，感觉真好用呀！！

2.改程序

我故意将一些关键字啥的写错，单词写错，看它能不能调整，我的输入内容如下

import matplotlib.pyplot as pltnp.randm.seed(0)
x = np.linspace(0, 10, 1000)
y = 0.8 * x + np.random.normal(0, 1, 1000)fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(x, y, color='green', alpha=0.7, eolor='black', linewidth=1)
ax.plot(x, 0.8 * x, color='red', linewidth=2, label='fit line')
ax.plot(x, x, color='black', linestyle='--', lidth=1, label='y=x line')ax.set_ylim(0, 10)
ax.grid(color='lightgray', linestyle='--', linewidth=0.5)
ax.spines['bottom'].set_color('black')
ax.spines['top'].set_color('blk')
ax.spis['left'].set_color('blk')
ax.spines['right'].selor('black')
ax.gend(fontsize=12)
ax.set_xlabel("X", fontsize=14)
ax.set_ylabel("Y", fontsize=14)
ax.set_title("Scatter Plot with Fit Line and y=x Line", fontsize=18)
plt.tighut()
plt.show()调整这段代码，使其能运行

它的回复：

import numpy as np
import matplotlib.pyplot as pltnp.random.seed(0)
x = np.linspace(0, 10, 1000)
y = 0.8 * x + np.random.normal(0, 1, 1000)fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(x, y, color='green', alpha=0.7, edgecolor='black', linewidth=1)
ax.plot(x, 0.8 * x, color='red', linewidth=2, label='fit line')
ax.plot(x, x, color='black', linestyle='--', linewidth=1, label='y=x line')ax.set_ylim(0, 10)
ax.grid(color='lightgray', linestyle='--', linewidth=0.5)
ax.spines['bottom'].set_color('black')
ax.spines['top'].set_color('black')
ax.spines['left'].set_color('black')
ax.spines['right'].set_color('black')
ax.legend(fontsize=12)
ax.set_xlabel("X", fontsize=14)
ax.set_ylabel("Y", fontsize=14)
ax.set_title("Scatter Plot with Fit Line and y=x Line", fontsize=18)
plt.tight_layout()
plt.show()

能运行！！

3.文字功夫

我用了一些文字来测试它，选用的是下面这篇文章的片段。

这是文章摘要：

Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002–2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, or climate change. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango delta. Others are consistent with climate model predictions. This observation-based assessment of how the world’s water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.

首先，我用它来翻译一下：