A Step-by-Step Guide to Installing Node.js and Configuring the Development Environment
Node.js is a JavaScript runtime environment based on Chrome's V8 engine, supporting backend development and extending JavaScript to server, desktop, and other domains, making it suitable for full-stack beginners. Installation varies by system: for Windows, download the LTS version installer and check "Add to PATH"; for Mac, use Homebrew; for Linux (Ubuntu), run `apt update` followed by `apt install nodejs npm`. VS Code is recommended for environment configuration—install the Node.js extension, create an `index.js` file, input `console.log('Hello, Node.js!')`, and execute `node index.js` in the terminal to run. npm is a package manager; initialize a project with `npm init -y`, install dependencies like `lodash` via `npm install lodash`, and use `require` in code. After setup, you can develop servers, APIs, etc., with regular practice recommended.
Read MoreGetting Started with Node.js: The First Step in JavaScript Backend Development
Node.js is a JavaScript runtime environment built on the V8 engine, enabling JavaScript to run on the server - side without a browser and facilitating full - stack development. Its core advantages lie in: no need to switch languages for full - stack development, non - blocking I/O for efficient handling of concurrent requests, light weight for rapid project development, and npm providing a rich ecosystem of packages. Installation is simple; after downloading the LTS version from the official website, you can verify the success by running `node -v` and `npm -v`. For the first program, create a `server.js` file, use the `http` module to write an HTTP server, and listen on a port to return "Hello World". Core capabilities include file operations with the `fs` module and npm package management (such as installing `figlet` to achieve artistic text). It is easy to get started with Node.js. It is recommended to start with practice, and later you can explore the Express framework or full - stack projects.
Read Morepandas Sorting Operations: An Introduction and Practical Guide to the sort_values Function
This article introduces the sorting method of the `sort_values` function in pandas, which is applicable to sorting DataFrame/Series data. Core parameters: `by` specifies the column(s) to sort by (required), `ascending` controls ascending/descending order (default is ascending True), and `inplace` determines whether to modify the original data (default is False, returning a new dataset). Basic usage: Single-column sorting, e.g., ascending order by "Chinese" (default) or descending order by "Math"; multi-column sorting can pass a list of column names and corresponding ascending/descending directions (e.g., first by "Chinese" ascending, then by "Math" descending). Setting `inplace=True` directly modifies the original data; it is recommended to prioritize preserving the original data (default False). Practical examples: After adding a "Total Score" column, sort by total score in descending order to clearly display the ranking of comprehensive scores. Notes: For multi-column sorting, ensure the lengths of the `by` and `ascending` lists are consistent; prioritize data safety to avoid accidental overwriting of original data. By mastering core parameters and common scenarios through examples, sorting serves as a foundational step in data processing, becoming more critical when combined with subsequent analyses (e.g., TopN).
Read Morepandas超实用技巧：数据清洗入门，新手也能轻松搞定
数据清洗是数据分析的关键，pandas是高效处理工具。文章教新手用pandas完成核心清洗：先安装导入数据（`pd.read_csv()`或创建示例DataFrame），用`head()`、`info()`初步检查。 处理缺失值：用`isnull()`识别，`dropna()`删除或`fillna()`（均值/中位数）填充；重复值用`duplicated()`识别，`drop_duplicates()`删除；异常值通过`describe()`统计或逻辑筛选（如收入≤20000）；数据类型转换用`astype()`或`to_datetime()`。 新手流程：导入→检查→处理缺失→重复→异常→类型转换。强调多动手练习，灵活应用工具解决实际数据问题。
Read MorePandas Data Merging: Basic Operations of merge and concat, Suitable for Beginners
This article introduces two data merging tools in pandas: `merge` and `concat`, suitable for beginners to quickly master. **concat**: No associated keys, direct concatenation, either row-wise (axis=0) or column-wise (axis=1). Row concatenation (axis=0) is suitable for tables with the same structure (e.g., multi-month data), and it is important to use `ignore_index=True` to reset the index and avoid duplicates. Column concatenation (axis=1) requires the number of rows to be consistent, used for merging by row identifiers (e.g., student information + grade table). **merge**: Merging based on common keys (e.g., name, ID), similar to SQL JOIN, supporting four methods: `inner` (default, retains common keys), `left` (retains left table), `right` (retains right table), and `outer` (retains all). When key names differ, use `left_on`/`right_on` to specify. The default merging method is `inner`. **Key Difference**: concat concatenates without keys, while merge matches by keys. Beginners should note: for column-wise concat, the number of rows must be consistent; merge uses the `how` parameter to control the merge scope, and avoid index duplication and key name mismatch issues.
Read MoreBeginner's Guide to pandas Index: Mastering Data Sorting and Renaming Effortlessly
### Detailed Explanation of pandas Index An index is a key element in pandas for identifying data positions and content, similar to row numbers/column headers in Excel. It serves as the "ID card" of data, with core functions including quick data location, supporting sorting and merging operations. **Data Sorting**: - **Series Sorting**: To sort by index, use `sort_index()` (ascending by default; set `ascending=False` for descending order). To sort by values, use `sort_values()` (ascending by default; same parameter for descending order). - **DataFrame Sorting**: Sort by column values using `sort_values(by=column_name)`, and sort by row index using `sort_index()`. **Renaming Indexes**: - Modify row/column labels with `rename()`, e.g., `df.rename(index={old_name: new_name})` or `df.rename(columns={old_name: new_name})`. - Direct assignment: `df.index = [new_index]` or `df.columns = [new_column_names]`, with length consistency required. **Notes**: - Distinguish between row index (`df.index`) and column index (`df.columns`). - When modifying indexes,
Read MorePandas Data Statistics: 5 Common Functions to Quickly Master Basic Analysis
Pandas is a powerful tool for processing tabular data in Python. This article introduces 5 basic statistical functions to help beginners quickly master data analysis skills. - **sum()**: Calculates the total sum, automatically ignoring missing values (NaN). Using `axis=1` allows summation by rows, which is useful for total statistics (e.g., total scores). - **mean()**: Computes the average, reflecting central tendency, but is sensitive to extreme values. Suitable for scenarios without extreme values. - **median()**: Calculates the median, which is robust to extreme values and better reflects the "true level of most data." - **max()/min()**: Returns the maximum/minimum values, respectively, for statistical extremes (e.g., highest/lowest scores). - **describe()**: Provides a one-stop statistical summary, outputting count, mean, standard deviation, quantiles, etc., to comprehensively understand data distribution and variability. These functions address basic questions like "total amount, average, middle level, and extreme values," serving as the "basic skills" of data analysis. Subsequent learning can advance to skills like groupby for more advanced statistics.
Read MoreIntroduction to pandas Series: From Understanding to Practical Operations, Even Beginners Can Grasp It
A Series in pandas is a labeled one-dimensional array containing data and indices, serving as a fundamental data processing structure. It can be created in various ways: from a list (with default 0, 1... indices), a dictionary (with keys as indices), a scalar value with a specified length (resulting in repeated values), or with a custom index (e.g., dates, strings). Key attributes include values (the data array), index (the labels), name (the Series name), and shape (the dimensions). Indexing operations support label-based access (loc) and positional access (iloc). Notably, label-based slicing includes the end label, while positional slicing does not. Data operations include statistical methods like sum and mean, as well as filtering via boolean conditions. In practical applications, Series are used for time series or labeled data (e.g., passenger flow analysis), enabling quick positioning, statistics, and filtering through index manipulation. Mastering index operations is crucial for effective data processing.
Read MoreMust - read for Beginners! Basic Operations in pandas: Creating, Viewing, and Modifying Data
This article introduces basic pandas operations, covering data creation, viewing, and modification. **Data Creation**: The core structures are Series (1D with index) and DataFrame (2D table). A Series can be created from a list (with default 0,1… indices) or custom indices (e.g., ['a','b']). A DataFrame can be created from a dictionary (keys = column names, values = column data) or a 2D list (with columns specified explicitly). **Data Viewing**: `head(n)`/`tail(n)` previews the first/last n rows (default 5 rows). `info()` shows data types and non-null values; `describe()` summarizes numerical columns (count, mean, etc.). `columns`/`index` display column names and row indices, respectively. **Data Modification**: Cell values are modified using `loc[label, column]` (label-based) or `iloc[position, column position]` (position-based). New columns are added via direct assignment (e.g., `df['Class'] = 'Class 1'`) or calculations based on existing columns. Columns are dropped with `drop(column name, axis=1, inplace=True)`. Indices can be modified by direct assignment to `index`/`columns` or renamed using `rename()`. The core is "locating data," requiring clear distinction between `loc` (label-based) and `iloc` (position-based) indexing.
Read MorePandas Tutorial for Beginners: Missing Value Handling from Entry to Practice
This article introduces methods for handling missing values in data analysis. Missing values refer to non-valid values in a dataset, represented as `NaN` in pandas. Before processing, it is necessary to first check: `isnull()` to mark missing values, `isnull().sum()` to count the number of missing values in each column, and `info()` to view the overall distribution of missing values. Processing strategies are divided into deletion and imputation: Deletion uses `dropna()`, which deletes records containing missing values by row (default) or by column; Imputation uses `fillna()`, including fixed values (e.g., 0), statistical measures (mean/median for numerical values, mode for categorical values), and forward/backward filling (`ffill/bfill`, suitable for time series). Taking e-commerce order data as an example, the case first checks for missing values, then uses the mean to impute the "amount" column and the mode to impute the "payment method" column. The core steps of processing are: check for missing values → select a strategy (delete for extremely few values, impute for many values or key data) → verify the result. It is necessary to flexibly choose methods based on the characteristics of the data.
Read MoreIntroduction to pandas DataFrame: 3-Step Quick Start for Data Selection and Filtering
This article introduces 3 core steps for data selection and filtering in pandas DataFrames, suitable for beginners to quickly master. Step 1: Column Selection. For a single column, use `df['column_name']` to return a Series; for multiple columns, use `df[['column_name1', 'column_name2']]` to return a DataFrame. Step 2: Row Selection. Two methods are provided: `iloc` (by position, integer indexing) and `loc` (by label, custom index). Examples: `df.iloc[row_range]` or `df.loc[row_label]`. Step 3: Conditional Filtering. For single conditions, use `df[condition]`. For multiple conditions, connect them with `&` (AND) / `|` (OR), and each condition must be enclosed in parentheses. Key Reminder: When filtering with multiple conditions, always use `&`/`|` instead of `and`/`or`, and enclose each condition in parentheses. Through these three steps, basic data extraction can be completed, laying the foundation for subsequent analysis.
Read MoreLearning pandas from Scratch: A Step-by-Step Guide to Reading CSV Files
This article introduces the introductory steps to learning pandas for data processing, with the core being reading CSV files and performing basic data operations. First, pandas is likened to the "steward" of data processing, and reading CSV is the first step in data analysis. The steps include: installing pandas (using `pip install`, or skipping if pre-installed with Anaconda/Jupyter); importing pandas as `import pandas as pd`; reading the CSV file with `pd.read_csv()` to generate a DataFrame; viewing data using `head()`/`tail()` for preview, `info()` to check data types and missing values, and `describe()` for numerical statistics; handling special formats such as Chinese garbled characters (via `encoding`), delimiters (via `sep`), and no header rows (via `names`). The article concludes by summarizing the basic skills acquired, noting that this is just the beginning of data processing, and subsequent advanced operations like filtering and cleaning can be learned next.
Read MoreNumpy Array Reshaping: A Beginner's Guide to reshape and flatten
This article introduces two practical methods for array reshaping in Numpy: `reshape` and `flatten`, which are used to meet different data processing needs. The core premise is that the total number of array elements before and after reshaping must be consistent. The `reshape` method can change the array shape (e.g., 1D to 2D). Its syntax is `arr.reshape(new_shape)`, which supports specifying the shape with a tuple. Using `-1` allows automatic calculation of the missing dimension (e.g., if the number of rows is 3, the number of columns is automatically calculated). It returns a new array without modifying the original array. The `flatten` method flattens a multi-dimensional array into a 1D array and returns a new array (a copy), avoiding modification of the original array. Unlike `ravel` (which returns a view), `flatten` is recommended for priority use. A common error is "mismatched element count", where it is necessary to ensure that the product of the `reshape` parameters equals the size of the original array (`original_array.size`). In summary, `reshape` flexibly adjusts the shape, and `flatten` safely flattens to 1D. Mastering both methods enables efficient array reshaping and lays the foundation for data processing (e.g., in machine learning).
Read MoreNumpy Statistical Analysis: Quick Start with mean, sum, and max Functions
This article introduces the usage methods of three commonly used statistical functions in NumPy: `mean` (average), `sum` (summation), and `max` (maximum). As a core tool for Python data analysis, NumPy provides efficient multidimensional arrays and statistical functions. All three functions support the `axis` parameter to control the calculation direction: `axis=0` calculates column-wise (vertically), `axis=1` calculates row-wise (horizontally), and if not specified, the overall value is computed. - **mean**: Computes the arithmetic mean of array elements. For a one-dimensional array, it returns the overall average; for a two-dimensional array, it can compute column-wise or row-wise averages. - **sum**: Computes the sum of array elements. Similar to `mean`, it specifies row or column summation via the `axis` parameter. - **max**: Finds the maximum value in the array, also supporting maximum value calculation across rows or columns. The article demonstrates basic usage with one-dimensional and two-dimensional array examples, and applies them to a practical case of student scores (3 students × 3 courses): calculating the average score per course, total score per student, and highest score. This verifies the practicality of the functions. It concludes that mastering these three functions and the `axis` parameter is fundamental for data analysis, laying the groundwork for subsequent complex analyses.
Read MoreNumpy File I/O: Practical Application of save and load for Data Persistence
This article introduces Numpy data persistence methods for storing/reading array data. A single array is saved as a `.npy` binary file using `np.save()`, and loaded with `np.load()`. The file automatically appends the extension, so ensure the path is correct. Multiple arrays are saved as a `.npz` compressed file using `np.savez()`, and loading returns a dictionary object accessible via key names. For text format, use `np.savetxt()`/`loadtxt()` to save as CSV or other text files, which are human-readable. However, binary formats (`.npy`/`.npz`) are more efficient and preserve data types. In summary: use `save()`/`load()` for single arrays, `savez()` for multiple arrays, and `savetxt()`/`loadtxt()` for text format, choosing based on specific needs.
Read MoreNumpy Data Types: A Comprehensive Analysis of dtype and astype
The homogeneity of NumPy arrays enables efficient data processing, and the data type (dtype) is crucial as it determines element storage, memory usage, and operation rules. A reasonable choice of dtype can optimize performance and avoid waste. A dtype is an object describing the array's type, viewable via `arr.dtype`, and can be explicitly specified during creation (e.g., `np.int32`). Common types include int (8/16/32/64-bit), uint (unsigned integers), float (32/64-bit), bool, and object. The `astype` method is used for type conversion, returning a new array without modifying the original. Examples include converting integers to floats (`arr.astype(np.float64)`), floats to integers (truncating decimals, e.g., `2.9` to `2`), and boolean-integer conversions (`True`→`1`, non-zero→`True`). It should be noted that converting to a smaller type may cause overflow (e.g., `int64` to `int32`), and floating-point to integer conversion does not round. Mastering dtype and `astype` allows flexible data handling, avoiding memory waste and calculation errors, thus laying a foundation for subsequent analysis.
Read MoreNumpy Matrix Basics: Introduction to Multiplication, Transposition, and Inverse Matrix
This article introduces basic Numpy matrix operations, suitable for beginners to get started quickly. The core of Numpy is `ndarray`, created using `np.array`. Basic attributes include `shape` (number of rows and columns), `ndim` (dimension), and `dtype` (data type). Three core operations: 1. **Multiplication**: Distinguish between element-wise multiplication (`*`, requiring identical shapes) and matrix dot product (`np.dot`/`@`, where the number of columns of the first matrix equals the number of rows of the second matrix, resulting in a shape of `m×p`). 2. **Transposition**: Achieved using `.T` to swap rows and columns, suitable for adjusting shapes to fit operations. 3. **Inverse Matrix**: Exists only for square matrices with non-zero determinants, calculated using `np.linalg.inv`. Verification is done with `np.allclose` to check if it is the identity matrix. After mastering the basics, more complex operations can be advanced. Numpy requires more practice to improve proficiency.
Read MoreNumpy Random Number Generation: A Beginner's Guide to rand and randn
NumPy is the core library for scientific computing in Python. The `np.random` submodule provides random number generation functionality, with `rand` and `randn` being commonly used functions. These random numbers are pseudo-random, and fixing the seed allows for reproducible results. `np.random.rand(d0, …, dn)` generates random numbers from a **uniform distribution over [0, 1)**. The parameters specify the array shape (e.g., 1-dimensional, 2-dimensional, etc.), and all elements lie within [0, 1). It is suitable for scenarios requiring equal probability values (e.g., initializing weights). `np.random.randn(d0, …, dn)` generates random numbers from a **standard normal distribution** (mean 0, standard deviation 1). Elements are concentrated between -1 and 1, with a low probability of extreme values. To adjust the mean and standard deviation, the formula `μ + σ * randn` can be used. This is often applied to simulate natural data fluctuations (e.g., noise). Both functions accept shape parameters, with the former producing uniform distribution and the latter normal distribution. The results can be reproduced by fixing the seed using `np.random.seed(seed)`.
Read MoreNumpy for Beginners: Quick Reference for Common Functions arange and zeros
This article introduces two basic numerical array creation functions in Python Numpy: `arange` and `zeros`. `arange` is used to generate ordered arrays, similar to Python's built-in `range` but returns a Numpy array. Its syntax includes `start` (default 0), `stop` (required, exclusive), `step` (default 1), and `dtype`. Examples: The default parameters generate an array from 0 to 4; specifying `start=2, step=2` generates [2, 4, 6, 8] (note that `stop` is not included). When the step is a decimal, attention should be paid to floating-point precision. `zeros` is used to generate arrays filled with zeros, commonly for initialization. Its syntax parameters are `shape` (required, integer or tuple) and `dtype` (default float). Examples: `zeros(5)` generates a 1D array [0.0, 0.0, 0.0, 0.0, 0.0]; `zeros((2, 3))` generates a 2×3 2D array. Specifying `dtype=int` can produce arrays of integer zeros. Note that `shape` must be clearly specified, and a tuple should be passed for multi-dimensional arrays. Both are core tools for Numpy beginners. `arange` constructs ordered data,
Read MoreNumpy Broadcasting: A Core Technique to Simplify Array Operations
The Numpy broadcasting mechanism addresses element-wise operations for arrays of different shapes by automatically expanding smaller arrays to match the shape of larger arrays and aligning dimensions, eliminating the need for manual reshaping, thus saving memory and improving efficiency. Core rules: dimensions are matched from right to left, with each dimension size either being 1 or equal; smaller arrays are broadcasted to the merged shape of the larger array. For example, scalars (e.g., 10) can be broadcast to any array shape; when a 1D array (e.g., [10, 20, 30]) is broadcasted with a 2×3 2D array, the 1D array is repeated into 2 rows. When a 3D array (2×2×2) is broadcasted with a 2×2 2D array, the 2D array is expanded to 2×2×2. If dimensions are incompatible (e.g., 2×2 and 1×3), an error is raised. Practical applications include element-wise operations (e.g., adding a constant to an array) and matrix standardization, avoiding loops and simplifying code. Mastering broadcasting significantly enhances the efficiency and readability of Numpy array operations.
Read MoreComprehensive Guide to Numpy Arrays: shape, Indexing, and Slicing
NumPy arrays are the foundation of Python data analysis, providing efficient multi-dimensional array objects with core operations including array creation, shape manipulation, indexing, and slicing. Creation methods: np.array() is commonly used to generate arrays from lists; zeros/ones create arrays filled with 0s/1s; arange generates sequences similar to Python's range. Shape is the dimension identifier of an array, viewed via .shape. The reshape() method adjusts dimensions (total elements must remain unchanged), with -1 indicating automatic dimension calculation. Indexing: 1D arrays behave like lists (0-based indexing with support for negative indices); 2D arrays use double indexing [i, j]. Slicing: Follows the syntax [start:end:step], with 1D/2D slicing producing subarrays. Slices return views by default (modifications affect the original array), requiring .copy() for independent copies. Mastering shape, indexing, and slicing is essential. Practical exercises are recommended to solidify these fundamental operations.
Read MoreGetting Started with Numpy from Scratch: From Array Creation to Basic Operations
NumPy is a core library for numerical computing in Python, providing high-performance multidimensional arrays and computational tools, suitable for scenarios such as data science and machine learning. Installation is done via `pip install numpy`, with the import typically abbreviated as `np`. Arrays can be created in various ways: from Python lists, using `np.zeros`/`ones` (arrays of all zeros/ones), `arange` (arithmetic sequences), `linspace` (uniformly distributed values), and `np.random` (random arrays). Array attributes include `shape` (dimensions), `ndim` (number of dimensions), `dtype` (data type), and `size` (total number of elements). Indexing and slicing are flexible: one-dimensional arrays behave like lists, while two-dimensional arrays use row and column indices, with support for boolean filtering (e.g., `arr[arr>3]`). Basic operations are efficient, including element-wise arithmetic (+, *, etc.), matrix multiplication (via `dot` or `@`), and the broadcasting mechanism (e.g., automatic expansion for array-scalar operations). Application examples include statistical analysis (using functions like `sum` and `mean`) and data filtering. Mastering these capabilities enables efficient numerical data processing and lays the foundation for advanced functionalities such as linear algebra.
Read MoreLearn Python OpenCV Easily: Drawing Basic Geometric Shapes
This article introduces methods to draw basic geometric shapes using OpenCV. The steps are as follows: First, install the opencv-python and numpy libraries. After importing these libraries, create a 500x500 black canvas. For drawing shapes: Lines are drawn using cv2.line, e.g., an anti-aliased red line from (50,50) to (450,450); Rectangles are drawn using cv2.rectangle, supporting both outlines (line width 3) and fill (line width -1), such as a green outlined rectangle and a blue filled rectangle; Circles are drawn using cv2.circle, supporting both outlines (line width 5) and fill (line width -1), such as a yellow outlined circle and a red filled circle; Polygons are drawn using cv2.polylines (for outlines) and cv2.fillPoly (for filling), with an example being a cyan triangular outline and a light red quadrilateral fill. Finally, display the image with cv2.imshow and wait for user input to close using cv2.waitKey. Key notes: Colors are in BGR format (e.g., red is (0,0,255)), line width -1 indicates filling, and the coordinate origin is at the top-left corner of the image.
Read MoreIntroduction to Python OpenCV: Denoising Methods in Image Preprocessing
In image preprocessing, denoising is a core step to eliminate noise (such as Gaussian, salt-and-pepper, Poisson noise) during acquisition/transmission and improve the accuracy of subsequent tasks. Python OpenCV provides multiple denoising methods: 1. **Mean Filtering**: A simple average of window pixels, fast but blurs edges. Suitable for Gaussian noise, implemented with `cv2.blur` (3×3 kernel). 2. **Median Filtering**: Replaces the center pixel with the median of window pixels. Effective against salt-and-pepper noise (0/255 specks), preserves edges well. Kernel size must be odd (e.g., 3×3), using `cv2.medianBlur`. 3. **Gaussian Filtering**: Weighted average using a Gaussian distribution kernel, balances denoising and edge preservation. Ideal for Gaussian noise, requires kernel size and standard deviation in `cv2.GaussianBlur`. 4. **Bilateral Filtering**: Combines spatial and color distance, excels at edge-preserving denoising with high computational cost. Suitable for high-precision scenarios (e.g., face images), implemented with `cv2.bilateralFilter`. **Selection Guidelines**: Gaussian noise → Gaussian filtering; salt-and-pepper noise → median filtering; mixed noise → Gaussian followed by median; high-frequency detail noise → bilateral filtering. Beginners are advised to start with Gaussian and median filters, adjusting based on... *(Note: The original text ends abruptly; the translation concludes at the logical cutoff point.)*
Read MorePython OpenCV Practical: Template Matching and Image Localization
This paper introduces an image localization method using Python OpenCV to implement template matching. The core of template matching is sliding a "template image" over a target image and calculating similarity to find the most matching region, which is suitable for simple scenarios (e.g., monitoring object localization). The steps include: preparing target and template images, converting them to grayscale to improve efficiency; using `matchTemplate` (e.g., the `TM_CCOEFF_NORMED` method) to calculate the similarity matrix; setting a threshold (e.g., 0.8) to filter high-similarity regions and using `np.where` to obtain their positions; finally, marking the matching results with rectangles and displaying/saving them. Note: Template matching is only applicable to scenarios where the target has no rotation or scaling; for complex scenarios, feature matching like ORB should be used instead. The matching method and threshold need to be adjusted according to actual conditions—too high a threshold may lead to missed detections, while too low may cause false positives. Through the practical example of "apple localization," this paper helps beginners master the basic process, making it suitable for quickly implementing simple image localization tasks.
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