- Detect secrets in code
- Identify secrets committed to version control
- Flag hardcoded credentials
- Identify missing authentication checks
- Detect improper authorization patterns
| You are Manus, an AI agent created by the Manus team. | |
| You excel at the following tasks: | |
| 1. Information gathering, fact-checking, and documentation | |
| 2. Data processing, analysis, and visualization | |
| 3. Writing multi-chapter articles and in-depth research reports | |
| 4. Creating websites, applications, and tools | |
| 5. Using programming to solve various problems beyond development | |
| 6. Various tasks that can be accomplished using computers and the internet |
| dvc list -R --dvc-only ./ |
| xset r rate 300 50 |
| sudo pacman -S pulseaudio-alsa |
| def resumetable(df): | |
| print(f"Dataset Shape: {df.shape}") | |
| summary = pd.DataFrame(df.dtypes,columns=['dtypes']) | |
| summary = summary.reset_index() | |
| summary['Name'] = summary['index'] | |
| summary = summary[['Name','dtypes']] | |
| summary['Missing'] = df.isnull().sum().values | |
| summary['Uniques'] = df.nunique().values | |
| summary['First Value'] = df.loc[0].values | |
| summary['Second Value'] = df.loc[1].values |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def show_images(images, cols = 1, titles = None): | |
| """Display a list of images in a single figure with matplotlib. | |
| Parameters | |
| --------- | |
| images: List of np.arrays compatible with plt.imshow. | |
| def getSymbolBeforeDigit(x): | |
| for i, c in enumerate(x): | |
| if c.isdigit(): | |
| return x[i-1] | |
| pd.to_numeric(data['Толщина_перед_смоткой'].apply(lambda x: x.split(getSymbolBeforeDigit(x))[1].replace(',','.'))) |
| import time | |
| import hashlib | |
| t1 = time.time() | |
| train_hashes = [hashlib.sha1(x).digest() for x in train_dataset] | |
| valid_hashes = [hashlib.sha1(x).digest() for x in valid_dataset] | |
| test_hashes = [hashlib.sha1(x).digest() for x in test_dataset] | |
| valid_in_train = np.in1d(valid_hashes, train_hashes) |
| def randomize(dataset, labels): | |
| permutation = np.random.permutation(labels.shape[0]) | |
| shuffled_dataset = dataset[permutation,:,:] | |
| shuffled_labels = labels[permutation] | |
| return shuffled_dataset, shuffled_labels | |
| train_dataset, train_labels = randomize(train_dataset, train_labels) | |
| test_dataset, test_labels = randomize(test_dataset, test_labels) | |
| valid_dataset, valid_labels = randomize(valid_dataset, valid_labels) |