The personal website of Scott W Harden

Draw with Maui.Graphics and Skia in a C# Console Application

This page describes how to draw graphics in a console application with Maui Graphics and Skia

Microsoft’s System.Drawing.Common package is commonly used for cross-platform graphics in .NET Framework and .NET Core applications, but according to the dotnet roadmap System.Drawing will soon only support Windows. As Microsoft sunsets cross-platform support for System.Drawing they will be simultaneously developing Microsoft.Maui.Graphics, a cross-platform graphics library for iOS, Android, Windows, macOS, Tizen and Linux completely in C#.

The Maui.Graphics library can be used in any .NET application (not just MAUI applications). This page documents how I used the Maui.Drawing package to render graphics in memory (using a Skia back-end) and save them as static images from a console application.

I predict Maui.Graphics will eventually evolve to overtake System.Drawing in utilization. It has many advantages for performance and memory management (discussed extensively elsewhere on the internet), but it is still early in development. As of today (July 2021) the Maui.Graphics GitHub page warns “This is an experimental library … There is no official support. Use at your own Risk.”

Maui Graphics Skia Console Quickstart

This program will create an image, fill it with blue, add 1,000 random lines, then draw some text. It is written as a .NET 5 top-level console application and requires the Microsoft.Maui.Graphics and Microsoft.Maui.Graphics.Skia NuGet packages (both are currently in preview).

We use SkiaSharp to create a canvas, but importantly that canvas implements Microsoft.Maui.Graphics.ICanvas (it’s not Skia-specific) so all the methods that draw on it can be agnostic to which rendering system was used. This makes it easy to write generic rendering methods now and have the option to switch the rendering system later.


using System;
using System.IO;
using Microsoft.Maui.Graphics;
using Microsoft.Maui.Graphics.Skia;

// Use Skia to create a Maui graphics context and canvas
BitmapExportContext bmpContext = SkiaGraphicsService.Instance.CreateBitmapExportContext(600, 400);
SizeF bmpSize = new(bmpContext.Width, bmpContext.Height);
ICanvas canvas = bmpContext.Canvas;

// Draw on the canvas with abstract methods that are agnostic to the renderer
ClearBackground(canvas, bmpSize, Colors.Navy);
DrawRandomLines(canvas, bmpSize, 1000);
DrawBigTextWithShadow(canvas, "This is Maui.Graphics with Skia");
SaveFig(bmpContext, Path.GetFullPath("quickstart.jpg"));

static void ClearBackground(ICanvas canvas, SizeF bmpSize, Color bgColor)
    canvas.FillColor = Colors.Navy;
    canvas.FillRectangle(0, 0, bmpSize.Width, bmpSize.Height);

static void DrawRandomLines(ICanvas canvas, SizeF bmpSize, int count = 1000)
    Random rand = new();
    for (int i = 0; i < count; i++)
        canvas.StrokeSize = (float)rand.NextDouble() * 10;

        canvas.StrokeColor = new Color(
            red: (float)rand.NextDouble(),
            green: (float)rand.NextDouble(),
            blue: (float)rand.NextDouble(),
            alpha: .2f);

            x1: (float)rand.NextDouble() * bmpSize.Width,
            y1: (float)rand.NextDouble() * bmpSize.Height,
            x2: (float)rand.NextDouble() * bmpSize.Width,
            y2: (float)rand.NextDouble() * bmpSize.Height);

static void DrawBigTextWithShadow(ICanvas canvas, string text)
    canvas.FontSize = 36;
    canvas.FontColor = Colors.White;
    canvas.SetShadow(offset: new SizeF(2, 2), blur: 1, color: Colors.Black);
    canvas.DrawString(text, 20, 50, HorizontalAlignment.Left);

static void SaveFig(BitmapExportContext bmp, string filePath)
    Console.WriteLine($"WROTE: {filePath}");


<Project Sdk="Microsoft.NET.Sdk">


    <PackageReference Include="Microsoft.Maui.Graphics" Version="6.0.100-preview.6.299" />
    <PackageReference Include="Microsoft.Maui.Graphics.Skia" Version="6.0.100-preview.6.299" />



C# Microphone Level Monitor

How to continuously monitor the level of an audio input (mono or stereo) with C#

This page demonstrates how to continuously monitor microphone input using C#. Code here may be a helpful reference for developers interested in working with mono or stereo data captured from an audio device in real time. This project uses NAudio to provide simple access to the microphone on Windows platforms.

Mono Stereo

Full source code is available on GitHub (Program.cs)

Configure the Audio Input Device

This program starts by creating a WaveInEvent with a WaveFormat that specifies the sample rate, bit depth, and number of channels (1 for mono, 2 for stereo).

We can create a function to handle incoming data and add it to the DataAvailable event handler:

var waveIn = new NAudio.Wave.WaveInEvent
    DeviceNumber = 0, // customize this to select your microphone device
    WaveFormat = new NAudio.Wave.WaveFormat(rate: 44100, bits: 16, channels: 1),
    BufferMilliseconds = 50
waveIn.DataAvailable += ShowPeakMono;

Analyze Mono Audio Data

This method is called when the incoming audio buffer is filled. One of the arguments gives you access to the raw bytes in the buffer, and it’s up to you to convert them to the appropriate data format.

This example is suitable for 16-bit (two bytes per sample) mono input.

private static void ShowPeakMono(object sender, NAudio.Wave.WaveInEventArgs args)
    float maxValue = 32767;
    int peakValue = 0;
    int bytesPerSample = 2;
    for (int index = 0; index < args.BytesRecorded; index += bytesPerSample)
        int value = BitConverter.ToInt16(args.Buffer, index);
        peakValue = Math.Max(peakValue, value);

    Console.WriteLine("L=" + GetBars(peakValue / maxValue));

This method converts a level (fraction) into bars suitable to display in the console:

private static string GetBars(double fraction, int barCount = 35)
    int barsOn = (int)(barCount * fraction);
    int barsOff = barCount - barsOn;
    return new string('#', barsOn) + new string('-', barsOff);

Analyze Stereo Audio Data

When the WaveFormat is configured for 2 channels, bytes in the incoming audio buffer will have left and right channel values interleaved (2 bytes for left, two bytes for right, then repeat). Left and right channels must be treated separately to display independent levels for stereo audio inputs.

This example is suitable for 16-bit (two bytes per sample) stereo input.

private static void ShowPeakStereo(object sender, NAudio.Wave.WaveInEventArgs args)
    float maxValue = 32767;
    int peakL = 0;
    int peakR = 0;
    int bytesPerSample = 4;
    for (int index = 0; index < args.BytesRecorded; index += bytesPerSample)
        int valueL = BitConverter.ToInt16(args.Buffer, index);
        peakL = Math.Max(peakL, valueL);
        int valueR = BitConverter.ToInt16(args.Buffer, index + 2);
        peakR = Math.Max(peakR, valueR);

    Console.Write("L=" + GetBars(peakL / maxValue));
    Console.Write(" ");
    Console.Write("R=" + GetBars(peakR / maxValue));


Working with 16-bit Images in CSharp

A summary of how I work with 16-bit TIF file data in C# (using ImageMagick and LibTiff).

Scientific image analysis frequently involves working with 12-bit and 14-bit sensor data stored in 16-bit TIF files. Images commonly encountered on the internat are 24-bit or 32-bit RGB images (where each pixel is represented by 8 bits each for red, green, blue, and possibly alpha). Typical image analysis libraries and documentation often lack information about how to work with 16-bit image data.

This page summarizes how I work with 16-bit TIF file data in C#. I prefer Magick.NET (an ImageMagick wrapper) when working with many different file formats, and LibTiff.Net whenever I know my source files will all be identically-formatted TIFs or multidimensional TIFs (stacks).


ImageMagick is a free and open-source cross-platform software suite for displaying, creating, converting, modifying, and editing raster images. Although ImageMagick is commonly used at the command line, .NET wrappers exist to make it easy to use ImageMagick from within C# applications.

ImageMagick has many packages on NuGet and they are described on ImageMagick’s documentation GitHub page. TLDR: Install the Q16 package (not HDRI) to allow you to work with 16-bit data without losing precision.

ImageMagick is free and distributed under the Apache 2.0 license, so it can easily be used in commercial projects.

An advantage of loading images with ImageMagick is that it will work easily whether the source file is a JPG, PNG, GIF, TIF, or something different. ImageMagick supports over 100 file formats!

Load a 16-bit TIF File as a Pixel Value Array

// Load pixel values from a 16-bit TIF using ImageMagick (Q16)
MagickImage image = new MagickImage("16bit.tif");
ushort[] pixelValues = image.GetPixels().GetValues();

That’s it! The pixelValues array will contain one value per pixel from the original image. The length of this array will equal the image’s height times its width.

Load an 8-bit TIF File as a Pixel Value Array

Since the Q16 package was installed, 2 bytes will be allocated for each pixel (16-bit) even if it only requires one byte (8-bit). In this case you must collect just the bytes you are interested in:

MagickImage image = new MagickImage("8bit.tif");
ushort[] pixelValues = image.GetPixels().GetValues();
int[] values8 = Enumerable.Range(0, pixelValues.Length / 2).Select(x => (int)pixelValues[x * 2 + 1]).ToArray();

Load a 32-bit TIF File as a Pixel Value Array

For this you’ll have to install the high dynamic range (HDRI) Q16 package, then your GetValues() method will return a float[] instead of a ushort[]. Convert these values to proper pixel intensity values by dividing by 2^16.

MagickImage image = new MagickImage("32bit.tif");
float[] pixels = image.GetPixels().GetValues();
for (int i = 0; i < pixels.Length; i++)
    pixels[i] = (long)pixels[i] / 65535;


LibTiff is a pure C# (.NET Standard) TIF file reader. Although it doesn’t support all the image formats that ImageMagick does, it’s really good at working with TIFs. It has a more intuitive interface for working with TIF-specific features such as multi-dimensional images (color, Z position, time, etc.).

LibTiff gives you a lower-level access to the bytes that underlie image data, so it’s on you to perform the conversion from a byte array to the intended data type. Note that some TIFs are little-endian encoded and others are big-endian encoded, and endianness can be read from the header.

LibTiff is distributed under a BSD 3-clause license, so it too can be easily used in commercial projects.

Load a 16-bit TIF as a Pixel Value Array

// Load pixel values from a 16-bit TIF using LibTiff
using Tiff image = Tiff.Open("16bit.tif", "r");

// get information from the header
int width = image.GetField(TiffTag.IMAGEWIDTH)[0].ToInt();
int height = image.GetField(TiffTag.IMAGELENGTH)[0].ToInt();
int bytesPerPixel = image.GetField(TiffTag.BITSPERSAMPLE)[0].ToInt() / 8;

// read the image data bytes
int numberOfStrips = image.NumberOfStrips();
byte[] bytes = new byte[numberOfStrips * image.StripSize()];
for (int i = 0; i < numberOfStrips; ++i)
    image.ReadRawStrip(i, bytes, i * image.StripSize(), image.StripSize());

// convert the data bytes to a double array
if (bytesPerPixel != 2)
    throw new NotImplementedException("this is only for 16-bit TIFs");
double[] data = new double[bytes.Length / bytesPerPixel];
for (int i = 0; i < data.Length; i++)
    if (image.IsBigEndian())
        data[i] = bytes[i * 2 + 1] + (bytes[i * 2] << 8);
        data[i] = bytes[i * 2] + (bytes[i * 2 + 1] << 8);

Routines for detecting and converting data from 8-bit, 24-bit, and 32-bit TIF files can be created by inspecting bytesPerPixel. LibTiff has documentation describing how to work with RGB TIF files and multi-frame TIFs.

Convert a Pixel Array to a 2D Array

I often prefer to work with scientific image data as a 2D arrays of double values. I write my analysis routines to pass double[,] between methods so the file I/O can be encapsulated in a static class.

// Load pixel values from a 16-bit TIF using ImageMagick (Q16)
MagickImage image = new MagickImage("16bit.tif");
ushort[] pixelValues = image.GetPixels().GetValues();

// create a 2D array of pixel values
double[,] imageData = new double[image.Height, image.Width];
for (int i = 0; i < image.Height; i++)
    for (int j = 0; j < image.Width; j++)
        imageData[i, j] = pixelValues[i * image.Width + j];

Other Libraries


According to ImageProcessor’s GitHub page, “ImageProcessor is, and will only ever be supported on the .NET Framework running on a Windows OS” … it doesn’t appear to be actively maintained and is effectively labeled as deprecated, so I won’t spend much time looking further into it.


As of the time of writing, ImageSharp does not support TIF format, but it appears likely to be supported in a future release.


Although this library can save images at different depths, it can only load image files with 8-bit depths. System.Drawing does not support loading 16-bit TIFs, so another library must be used to work with these file types.


Representing Images in Memory

quick reference for programmers interested in working with image data in memory (byte arrays)

This page is a quick reference for programmers interested in working with image data in memory (byte arrays). This topic is straightforward overall, but there are a few traps that aren’t necessarily intuitive so I try my best to highlight those here.

💡 See my newer article, Creating Bitmaps from Scratch in C#

This article assumes you have some programming experience working with byte arrays in a C-type language and have an understanding of what is meant by 32-bit, 24-bit, 16-bit, and 8-bit integers.

Pixel Values

An image is composed of a 2D grid of square pixels, and the type of image greatly influences how much memory each pixel occupies and what format its data is in.

Bits per pixel (bpp) is the number of bits it takes to represent the value a single pixel. This is typically a multiple of 8 bits (1 byte).

Common Pixel Formats

There are others (e.g., 64-bit RGB images), but these are the most typically encountered pixel formats.


Endianness describes the order of bytes in a multi-byte value uses to store its data:

Assuming array index values ascend from left to right, 32-bit (4-byte) pixel data can be represented using either of these two formats in memory:

Bitmap images use little-endian integer format! New programmers may expect the bytes that contain “RGB” values to be sequenced in the order “R, G, B”, but this is not the case.

Premultiplied Alpha

Premultiplication refers to the relationship between color (R, G, B) and transparency (alpha). In transparent images the alpha channel may be straight (unassociated) or premultiplied (associated).

With straight alpha, the RGB components represent the full-intensity color of the object or pixel, disregarding its opacity. Later R, G, and B will each be multiplied by the alpha to adjust intensity and transparency.

With premultiplied alpha, the RGB components represent the emission (color and intensity) of each pixel, and the alpha only represents transparency (occlusion of what is behind it). This reduces the computational performance for image processing if transparency isn’t actually used.

In C# using System.Drawing premultiplied alpha is not enabled by default. This must be defined when creating new Bitmap as seen here:

var bmp = new Bitmap(400, 300, PixelFormat.Format32bppPArgb);

Benchmarking reveals the performance enhancement of drawing on bitmaps in memory using premultiplied alpha pixel format. In this test I’m using .NET 5 with the System.Drawing.Common NuGet package. Anti-aliasing is off in this example, but similar results were obtained with it enabled.

Random rand = new(0);
int width = 600;
int height = 400;
var bmp = new Bitmap(600, 400, PixelFormat.Format32bppPArgb);
var gfx = Graphics.FromImage(bmp);
var pen = new Pen(Color.Black);
var sw = Stopwatch.StartNew();
for (int i = 0; i < 1e6; i++)
    pen.Color = Color.FromArgb(rand.Next());
    gfx.DrawLine(pen, rand.Next(width), rand.Next(height), rand.Next(width), rand.Next(height));
bmp.Save("benchmark.png", ImageFormat.Png);

Time to render 1 million frames:

At the end you have a beautiful figure:

Pixel Locations in Space and Memory

A 2D image is composed of pixels, but addressing them in memory isn’t as trivial as it may seem. The dimensions of bitmaps are stored in their header, and the arrangement of pixels forms rows (left-to-right) then columns (top-to-bottom).

Width and height are the dimensions (in pixels) of the visible image, but…

⚠️ Image size in memory is not just width * height * bytesPerPixel

Because of old hardware limitations, bitmap widths in memory (also called the stride) must be multiplies of 4 bytes. This is effortless when using ARGB formats because each pixel is already 4 bytes, but when working with RGB images it’s possible to have images with an odd number of bytes in each row, requiring data to be padded such that the stride length is a multiple of 4.

// calculate stride length of a bitmap row in memory
int stride = 4 * ((imageWidth * bytesPerPixel + 3) / 4);

Working with Bitmap Bytes in C#

This example demonstrates how to convert a 3D array (X, Y, C) into a flat byte array ready for copying into a bitmap. Notice this code adds padding to the image width to ensure the stride is a multiple of 4 bytes. Notice also the integer encoding is little endian.

public static byte[] GetBitmapBytes(byte[,,] input)
    int height = input.GetLength(0);
    int width = input.GetLength(1);
    int bpp = input.GetLength(2);
    int stride = 4 * ((width * bpp + 3) / 4);

    byte[] pixelsOutput = new byte[height * stride];
    byte[] output = new byte[height * stride];

    for (int y = 0; y < height; y++)
        for (int x = 0; x < width; x++)
            for (int z = 0; z < bpp; z++)
                output[y * stride + x * bpp + (bpp - z - 1)] = input[y, x, z];

    return output;

For completeness, here’s the complimentary code that converts a flat byte array from bitmap memory to a 3D array (assuming we know the image dimensions and bytes per pixel from reading the image header):

public static byte[,,] GetBitmapBytes3D(byte[] input, int width, int height, int bpp)
    int stride = 4 * ((width * bpp + 3) / 4);

    byte[,,] output = new byte[height, width, bpp];
    for (int y = 0; y < height; y++)
        for (int x = 0; x < width; x++)
            for (int z = 0; z < bpp; z++)
                output[y, x, z] = input[stride * y + x * bpp + (bpp - z - 1)];

    return output;

Marshalling Bytes in and out of Bitmaps

The code examples above are intentionally simple to focus on the location of pixels in memory and the endianness of their values. To actually convert between byte[] and System.Drawing.Bitmap you must use Marshall.Copy as shown:

public static byte[] BitmapToBytes(Bitmap bmp)
    Rectangle rect = new(0, 0, bmp.Width, bmp.Height);
    BitmapData bmpData = bmp.LockBits(rect, ImageLockMode.ReadWrite, bmp.PixelFormat);
    int byteCount = Math.Abs(bmpData.Stride) * bmp.Height;
    byte[] bytes = new byte[byteCount];
    Marshal.Copy(bmpData.Scan0, bytes, 0, byteCount);
    return bytes;
public static Bitmap BitmapFromBytes(byte[] bytes, PixelFormat bmpFormat)
    Bitmap bmp = new(width, height, bmpFormat);
    var rect = new Rectangle(0, 0, width, height);
    BitmapData bmpData = bmp.LockBits(rect, ImageLockMode.ReadOnly, bmpFormat);
    Marshal.Copy(bytes, 0, bmpData.Scan0, bytes.Length);
    return bmp;

How to Create a Bitmap in Memory Without a Graphics Library

The code examples above use System.Drawing.Common to create graphics, but creating bitmaps in a byte[] array is not difficult and can be done in any language. See the Creating Bitmaps from Scratch article for more information.


Deploy a Website with Python and FTPS

How to use Python, keyring, and TLS to securely deploy website content using FTP (FTPS)

Python can be used to securely deploy website content using FTPS. Many people have used a FTP client like FileZilla to drag-and-drop content from their local computer to a web server, but this method requires manual clicking and is error-prone. If you write a script to accomplish this task it lowers the effort barrier for deployment (encouraging smaller iterations) and reduces the risk you’ll accidentally do something unintentional (like deleting an important folder by accident).

This post reviews how I use Python, keyring, and TLS to securely manage login credentials and deploy builds from my local computer to a remote server using FTP. The strategy discussed here will be most useful in servers that use the LAMP stack, and it’s worth noting that .NET and Node have their own deployment paradigms. I hope you find the code on this page useful, but you should carefully review your deployment script and create something specific to your needs. Just as you could accidentally delete an important folder using a graphical client, an incorrectly written deployment script could cause damage to your website or leak secrets.

Use Keyring to Manage Your Password

I recently wrote about several ways to manage credentials with Python.

In these examples I will use the keyring package to store and recall my FTP password securely.

pip install keyring

Storing Credentials

Store your password using an interactive interpreter to ensure you don’t accidentally save it in a plain text file somewhere. This only needs to be done once.

>>> import keyring
>>> keyring.set_password("system", "me@swharden.com", "P455W0RD")

Recalling Credentials

import keyring
hostname = "swharden.com"
username = "me@swharden.com"
password = keyring.get_password("system", username)


FTP was not designed to be a secure - it transfers login credentials in plain text! Traditionally FTP in Python was achieved using ftplib.FTP from the standard library, but logging-in using this protocol allows anyone sniffing traffic on your network to capture your password. In Python 2.7 ftplib.FTP_TLS was added which adds transport layer security to FTP (FTPS), improving protection of your login credentials.

# ⚠️ This is insecure (password transferred in plain text)
from ftplib import FTP
with FTP(hostname, username, password) as ftp:
# 👍 This is better (password is encrypted)
from ftplib import FTP_TLS
with FTP_TLS(hostname, username, password) as ftps:

By default ftplib.FTP_TLS only encrypts the username and password. You can call prot_p() to encrypt all transferred data, but in this post I’m only interested in encrypting my login credentials.

FTP over SSL (FTPS) is different than FTP over SSH (SFTP), but both use encryption to transfer usernames and passwords, making them superior to traditional FTP which transfers these in plain text.

Recursively Delete a Folder with FTP

This method deletes each of the contents of a folder, then deletes the folder itself. If one of the contents is a subfolder, it calls itself. This example uses modern Python practices, favoring pathlib over os.path.

Note that I define the remote path using pathlib.PurePosixPath() to ensure it’s formatted as a unix-type path since my remote server is a Linux machine.

import ftplib
import pathlib

def removeRecursively(ftp: ftplib.FTP, remotePath: pathlib.PurePath):
    Remove a folder and all its contents from a FTP server

    def removeFile(remoteFile):
        print(f"DELETING FILE {remoteFile}")

    def removeFolder(remoteFolder):
        print(f"DELETING FOLDER {remoteFolder}/")

    for (name, properties) in ftp.mlsd(remotePath):
        fullpath = remotePath.joinpath(name)
        if name == '.' or name == '..':
        elif properties['type'] == 'file':
        elif properties['type'] == 'dir':
            removeRecursively(ftp, fullpath)


if __name__ == "__main__":
    remotePath = pathlib.PurePosixPath('/the/remote/folder')
    with ftplib.FTP_TLS("swharden.com", "scott", "P455W0RD") as ftps:
        removeFolder(ftps, remotePath)

Recursively Upload a Folder with FTP

This method recursively uploads a local folder tree to the FTP server. It first creates the folder tree, then uploads all files individually. This example uses modern Python practices, favoring pathlib over os.walk() and os.path.

Like before I define the remote path using pathlib.PurePosixPath() since the server is running Linux, but I can use pathlib.Path() for the local path and it will auto-detect how to format it based on which system I’m currently running on.

import ftplib
import pathlib

def uploadRecursively(ftp: ftplib.FTP, remoteBase: pathlib.PurePath, localBase: pathlib.PurePath):
    Upload a local folder to a remote path on a FTP server

    def remoteFromLocal(localPath: pathlib.PurePath):
        pathParts = localPath.parts[len(localBase.parts):]
        return remoteBase.joinpath(*pathParts)

    def uploadFile(localFile: pathlib.PurePath):
        remoteFilePath = remoteFromLocal(localFile)
        print(f"UPLOADING FILE {remoteFilePath}")
        with open(localFile, 'rb') as localBinary:
            ftp.storbinary(f"STOR {remoteFilePath}", localBinary)

    def createFolder(localFolder: pathlib.PurePath):
        remoteFolderPath = remoteFromLocal(localFolder)
        print(f"CREATING FOLDER {remoteFolderPath}/")

    for localFolder in [x for x in localBase.glob("**/*") if x.is_dir()]:
    for localFile in [x for x in localBase.glob("**/*") if not x.is_dir()]:

if __name__ == "__main__":
    localPath = pathlib.Path(R'C:\my\project\folder')
    remotePath = pathlib.PurePosixPath('/the/remote/folder')
    with ftplib.FTP_TLS("swharden.com", "scott", "P455W0RD") as ftps:
        uploadRecursively(ftps, remotePath, localPath)

Minimize Disruption by Renaming

Because walking remote folder trees deleting and upload files can be slow, this process may be disruptive to a website with live traffic. For low-traffic websites this isn’t an issue, but as traffic increases (or the size of the deployment increases) it may be worth considering how to achieve the swap faster.

An improved method of deployment could involve uploading the new website to a temporary folder, switching the names of the folders, then deleting the old folder. There is brief downtime between the two FTP rename calls.

remotePath = "/live"
remotePathNew = "/live-new"
remotePathOld = "/live-old"
localPath = R"C:\dev\site\live"

upload(localPath, remotePathNew)
ftpRename(remotePath, remotePathOld)
ftpRename(remotePathNew, remotePath)

Speed could be improved by handling the renaming with a shell script that runs on the server. This would require some coordination to execute though, but is worth considering. It could be executed by a HTTP endpoint.

mv /live /live-old;
mv /live-new /live;
rm -rf /live-old;

Deploy a React App with FTP and Python

You can automate deployment of a React project using Python and FTPS. After creating a new React app add a deploy.py in the project folder that uses FTPS to upload the build folder to the server, then edit your project’s package.json to add predeploy and deploy commands.

  "scripts": {
    "start": "react-scripts start",
    "build": "react-scripts build",
    "test": "react-scripts test",
    "eject": "react-scripts eject",
    "predeploy": "npm run build",
    "deploy" : "python deploy.py"

Then you can create a production build and deploy with one command:

npm run deploy

Consider Using Git to Deploy Content

This post focused on how to automate uploading local content to a remote server using FTP, but don’t overlook the possibility that this may not be the best method for deployment for your application.

You can maintain a website as a git repository and use git pull on the server to update it. GitHub Actions can be used to trigger the pull step automatically using an HTTP endpoint. If this method is available to you, it should be strongly considered.

This method is very popular, but it (1) requires git to be on the server and (2) requires all the build tools/languages to be available on the server if a build step is required. I’m reminded that only SiteGround’s most expensive shared hosting plan even has git available at all.