I always struggled with the plt.imshow() method of Python’s matplotlib library. To help you and me master it, I committed to write the most in-depth resource about it on the web.

As you study the resource, you can play my explainer video that leads you through the code in this article:

To show an image in matplotlib, first read it in using plt.imread(), then display it with plt.imshow().

import matplotlib.pyplot as plt cat_img = plt.imread('Figures/cat.jpeg')

plt.imshow(cat_img)

To turn the (annoying) axis ticks off, call plt.axis('off').

cat_img = plt.imread('Figures/cat.jpeg')

plt.axis('off')

plt.imshow(cat_img)

Much better! But there is a lot more you can do than just show images. Let’s look at how this works in more detail.

Matplotlib Imshow Example

When you display an in image in matplotlib, there are 2 steps you need to take: first you read the image and then you show it.

You read in the image using plt.imread() and pass it a string. I have the images stored in a directory called Figures, so I first write Figures/ followed by the name of the image with its file extension – cat.jpeg. If your images are stored in your current working directory, you can omit Figures/.

I store the output of plt.imread() in a variable with a descriptive name because you need to pass this to plt.imshow(). So, the first line is cat_img = plt.imread('Figures/cat.jpeg').

Images are made up of pixels and each pixel is a dot of color. The cat image is 1200×800 pixels. When an image is loaded into a computer, it is saved as an array of numbers. Each pixel in a color image is made up of a Red, Green and Blue (RGB) part. It can take any value between 0 and 255 with 0 being the darkest and 255 being the brightest. In a grayscale image, each pixel is represented by just a single number between 0 and 1. If a pixel is 0, it is completely black, if it is 1 it is completely white. Everything in between is a shade of gray.

So, if the cat image was black and white, it would be a 2D numpy array with shape (800, 1200). As it is a color image, it is in fact a 3D numpy array (to represent the three different color channels) with shape (800, 1200, 3).

Note that Numpy writes image sizes is the opposite way to matplotlib and the ‘real world’. Numpy is from the world of mathematics and matrices where you always write the number of rows (height) first followed by the columns (width). If you need a quick NumPy refresher, check out my in-depth NumPy tutorial on this blog.

Matplotlib bases its image functions on the ‘real world’ and so displays the width first followed by the height.

Let’s look at the type and size of cat_img.

cat_img = plt.imread('Figures/cat.jpeg') print(type(cat_img))

# <class 'numpy.ndarray'> print(cat_img.shape)

# (800, 1200, 3) <class 'numpy.ndarray'>

(800, 1200, 3)

Once you’ve read your image into a numpy array, it’s time to display it using plt.imshow(). This is similar to plt.show() which you call at the end of any matplotlib plot. However, unlike plt.show(), you must pass the image you want to display as an argument. This is helpful if you have read in multiple images but only want to display a certain number of them.

plt.imshow(cat_img)

I’m not sure why but, by default, all images are shown with axis ticks and labels. This can be quite annoying, so call plt.axis('off') to remove them.

However, ticks and labels can be helpful if you only want to select part of the image. By using them as guides, I’ll slice cat_img to just get the head of our cute kitten.

# Slicing found by using axis ticks and lables on image above

cat_head = cat_img[150:450, 275:675, :]

plt.imshow(cat_head)

The above image is a jpeg but you can also display other types of image files such as pngs.

# A png image

snow_img = plt.imread('Figures/snowboarder.png')

plt.axis('off')

plt.imshow(snow_img)

You can display gifs but it is slightly more complicated to do so and so is outside the scope of this article. If you read in and show a gif using the above steps, matplotlib will just show one of its frames.

janice = plt.imread('Figures/janice.gif')

plt.axis('off')

plt.imshow(janice)

Matplotlib Imshow Grayscale

You can turn any color image into a grayscale image in matplotlib. Since grayscale images are 2D numpy arrays, use slicing to turn your 3D array into a 2D one.

cat_img = plt.imread('Figures/cat.jpeg') # Turn 3D array into 2D by selecting just one of the three dimensions

grayscale_cat = cat_img[:, :, 0]

If you write cat_img[:, :, 1] or cat_img[:, :, 2] you will get different arrays but the final image will still look the same. This is because grayscale images just care about the relative difference of intensity between each pixel and this is the same over each RGB channel.

Let’s plot it and see what happens.

plt.axis('off')

plt.imshow(grayscale_cat)

Wait, what?

Don’t worry, you haven’t done anything wrong! You created a grayscale image but matplotlib applied a colormap to it automatically. This happens even if you pick the second or third dimension in your grayscale_cat definition.

To make it grayscale, set the cmap keyword argument to 'gray' in plt.imshow().

plt.axis('off')

plt.imshow(grayscale_cat, cmap='gray')

Perfect! Now you can easily turn any color image into a grayscale one in matplotlib. But there are many more colors you can choose from. Let’s look at all the colormaps in detail.

Matplotlib Imshow Colormap

There are many different colormaps you can apply to your images. Simply pass the name to the cmap keyword argument in plt.imshow() and you’re good to go. I recommend you play around with them since most of the names won’t mean anything to you on first reading.

The default colormap is viridis. Let’s explicitly apply it to our cat image.

# Turn cat_img into a 2D numpy array colored_cat = cat_img[:, :, 0]

plt.axis('off')

plt.imshow(colored_cat, cmap='viridis')

I could write a whole article on colormaps – in fact, the creator of Seaborn did. It’s a fascinating topic. Choosing the right one can make your plot incredible and choosing a bad one can ruin it.

For beginners, matplotlib recommends the Perceptually Uniform Sequential colormaps as they work well and are easy on the eyes. Here’s a list of them all.

percp_uniform_seq_cmaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis']

You’ve already seen viridis, so let’s display the others on a 2×2 grid (if you don’t know how to make subplots in matplotlib check out my article).

fig, axes = plt.subplots(nrows=2, ncols=2)

cmaps = ['plasma', 'inferno', 'magma', 'cividis'] for ax, cmap in zip(axes.flat, cmaps): ax.imshow(colored_cat, cmap=cmap) ax.set(title=cmap) ax.axis('off')

plt.show()

The colormaps are quite similar and easy to look at. They don’t distort the image and the highs and lows can still be seen.

Here are some other colormaps I’ve randomly chosen. Note how odd some of them are, especially flag!

fig, axes = plt.subplots(nrows=2, ncols=3, figsize=plt.figaspect(1/2))

cmaps = ['jet', 'binary', 'RdYlGn', 'twilight', 'Pastel1', 'flag'] for ax, cmap in zip(axes.flat, cmaps): ax.imshow(colored_cat, cmap=cmap, label=cmap) ax.set(title=cmap) ax.axis('off')

plt.show()

Matplotlib Imshow Colorbar

If your image has a colormap applied to it, you may want to tell the reader what each color means by providing a colorbar. Use plt.colorbar() to add one.

plt.imshow(colored_cat, cmap='hot')

plt.axis('off')

plt.colorbar()

Remember that every value in an array representing a colored image is a number between 0 and 255 and so the colorbar reflects this.

Note that I called plt.imshow() on the first line. Unlike plt.show(), you can modify images after calling plt.imshow() and the changes will be applied to the output.

Matplotlib Imshow Size

As with every Figure in matplotlib, you can manually set the Figure‘s size. Simply call plt.figure() at the top and set the figsize argument. You can either set it to a specific size in inches or set the aspect ratio:

1. plt.figure(figsize=(8, 6)) – 8 inches wide, 6 inches tall
2. plt.figure(figsize=plt.figaspect(2)) – aspect ratio of 2, i.e. twice as tall as it is wide.

Note: the default figsize can be found by calling plt.rcParams['figure.figsize'] and is (6.4, 4.8) inches.

Let’s see what happens when we modify the Figure size of our cat image.

# 1 inch wide x 1 inch tall...?

plt.figure(figsize=(1, 1)) cat_img = plt.imread('Figures/cat.jpeg')

plt.axis('off')

plt.imshow(cat_img)

This is certainly smaller than the original. But it doesn’t look like it is 1×1 inches – it’s clearly wider that it is tall. But it looks like one of the sides is correct which is better than nothing.

Let’s try to make it 1 inch wide and 3 inches tall.

# 1 inch wide x 3 inches tall...?

plt.figure(figsize=(1, 3)) cat_img = plt.imread('Figures/cat.jpeg')

plt.axis('off')

plt.imshow(cat_img)

Now the image hasn’t changed at all. What’s going on?

When you resize images in most programs like Microsoft Word or Google Sheets, they maintain their original height/width ratios. This is called preserving the aspect ratio of the image and is usually a very good thing. It ensures that pixels in the image are not distorted when you resize them. This is also the default behavior in matplotlib.

If you actually want to distort the image and to fill up the entire space available in the Figure, change the aspect keyword argument in plt.imshow().

There are only two possible values for aspect:

1. ‘equal’ – (default) preserves the aspect ratio, i.e. the side lengths of the pixels are equal (square)
2. ‘auto’ – does not preserve the aspect ratio

Let’s look at the same examples as above but set aspect='auto'.

# 1 inch wide x 1 inch tall (definitely)

plt.figure(figsize=(1, 1)) cat_img = plt.imread('Figures/cat.jpeg')

plt.axis('off')

plt.imshow(cat_img, aspect='auto')

Great! Now, the image is clearly 1×1 inches but it has come at the expense of slight image distortion.

Let’s distort it even further.

# 1 inch wide x 3 inches tall (definitely)

plt.figure(figsize=(1, 3)) cat_img = plt.imread('Figures/cat.jpeg')

plt.axis('off')

plt.imshow(cat_img, aspect='auto')

Perfect! Clearly the image is distorted but now you know how to do it.

Note the default aspect value can be changed for all images by setting plt.rcParams['image.aspect'] = 'auto'.

Summary

Now you know the basics of displaying images with matplotlib.

You understand why you need to first read images in from a file and store them as a numpy array (NumPy tutorial here) before displaying them with plt.imshow(). You know how to read in different types of image files (hint – if it’s a still image, it’s the same process regardless of the file type!).

If you want to set a specific colormap, you use the cmap keyword argument and you can also turn your color image to a grayscale one. Finally, you can add a colorbar and change the size of your image to be anything you want.

There are some more advanced topics to learn about such as interpolation, adding legends and using log scales for your axes but I’ll leave them for another article.

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As a security advisor working with one to three Chief Information Security Officers (CISOs) each week, the topic of identity comes up often. These are smart people who have often been in industry for decades. They have their own vocabulary of acronyms that only security professionals know such as DDoS, CEH, CERT, RAT, and 0-Day (if you don’t know one or several of these terms, I encourage you to look them up to build your vocabulary), but they often find themselves confused by Microsoft’s own set of acronyms.

This is the first in a blog series that aims to lessen some confusion around identity by sharing with you some of the terms used at Microsoft. Terms like MFA, PIM, PAM, MIM, MAM, MDM, and a few others. What do they mean and how do they relate to each other?

Multi-Factor Authentication or MFA

Let’s start with what identity means to Microsoft. Identity is the ability to clearly and without doubt ensure the identification of a person, device, location, or application. This is done by establishing trust verification and identity verification using what Microsoft calls Multi-Factor Authentication or MFA. This is a combination of capabilities that allow the entity to establish trust and verify who or what they are.

MFA is an authentication method in which a computer user is granted access only after successfully presenting two or more pieces of evidence (or factors) to an authentication mechanism: something the user and only the user knows (such as a password or PIN), something the user and only the user has (such as a mobile device or FIDO key), and something the user and only the user is (a biometric such as a fingerprint or iris scan).

Microsoft does this with technologies such as Azure Active Directory (Azure AD) in the cloud combined with Windows Hello. Azure AD is Microsoft’s identity and access management solution. Windows Hello is a Windows capability that allows a user to verify who they are with an image, a pin, or other biometric. The person’s identity is stored via an encrypted hash in the cloud, so it’s never shared in the clear (unencrypted). A cryptographic hash is a checksum that allows someone to proof that they know the original input (e.g., a password) and that the input (e.g., a document) has not been modified.

Privileged Identity Management or PIM

What is Privileged Identity Management or PIM? Organizations use PIM to assign, activate, and approve privileged identities in Azure AD. PIM provides time-based and approval-based role activation to mitigate the risks of excessive, unnecessary, or misused access permissions to sensitive resources.

Key features of PIM include:

• Just-in-time privileged access to Azure AD and Azure resources.
• Time-bound access to resources.
• An approval process to activate privileged roles.
• MFA enforcement.
• Justification to understand why users activate.
• Notifications when roles are activated.
• Access reviews and internal and external audit history.

Privileged Access Management or PAM

What is Privileged Access Management or PAM? Often confused with PIM, PAM is a capability to help organizations manage identities for existing on-premises Active Directory environments. PAM is an instance of PIM that is accessed using Microsoft Identity Manager or MIM. Confused? Let me explain.

PAM helps organizations solve a few problems including:

• Making it harder for attackers to penetrate a network and obtain privileged account access.
• Adding protection to privileged groups that control access to domain-joined computers and the applications on those computers.
• Providing monitoring, visibility, and fine-grained controls so organizations can see who their privileged admins are and what they are doing.

PAM gives organizations more insight into how admin accounts are being used in the environment.

Microsoft Identity Manager or MIM

But I also mentioned MIM… What is this? Microsoft Identity Manager or MIM helps organizations manage the users, credentials, policies, and access within their organizations and hybrid environments. With MIM, organizations can simplify identity lifecycle management with automated workflows, business rules, and easy integration with heterogenous platforms across the datacenter. MIM enables Active Directory to have the right users and access rights for on-premises apps. Azure AD Connect can then make those users and permissions available in Azure AD for Office 365 and cloud-hosted apps.

OK, so now we know that:

• PIM is a capability to help companies manage identities in Azure AD.
• PAM is an on-premises capability to manage identities in Active Directory.
• MIM helps organizations manage users, credentials, policies, and on-premises access.

Mobile Application Management or MAM

What’s left… Oh yes: Mobile Application Management or MAM. MAM is important because if organizations can only manage identities—but not the apps then they miss a key aspect of protecting data. MAM is connected to a Microsoft capability called Microsoft Intune and is a suite of management features to publish, push, configure, secure, monitor, and update mobile apps for users.

MAM works with or without enrollment of the device, which means organizations can protect sensitive data on almost any device using MAM-WE (without enrollment). If organizations enable MFA, they can verify the user on the device. MAM also helps manage that apps the trusted user or entity can access. If you add in the Mobile Device Management or MDM feature of Intune, you can force enrollment of devices and then use MAM to manage the apps.

It’s well known that Microsoft has a lot of acronyms. This is the first in a series of blog posts aimed to assist you in navigating the acronym forest created by companies and industry. The Microsoft Platform includes a powerful set of capabilities to help encourage users to make the right decisions and gives security leadership, like you, the ability to manage and monitor identities and control access to critical files and network assets.

MARS

“I’m willing to overlook your past.”

Crimson light quivers, exhales, and surges in waves across bedrock walls at quickening tempo. Deified machination ripples in judgement.

“Trust doesn’t come easily with you.” Osiris’s eyes wash in Warmind light. He remembers Saladin’s words. Remembers the names that were. He feels small again, against the wash. Alone.

Osiris feels the weight of Rasputin’s assessment. Rhythmic cipher crashes over him as displays sling projections into maddening motion. Osiris’s face splits into golden multitudes to consume the information. Eyes in all directions, searching for the path. Rasputin constructs a model of the system, highlighting an anomalous signal near the edge of Sol’s influence. Osiris’s mind sieves the data into manufactured purpose.

He is led.

Never one to follow.

With nothing left to chase.

Oxidized dust scours the landing pad. Sagira greets Osiris as he exits the bunker and slumps into his jumpship.

“How did it go?”

“Better than expected.”

“Did you say hi to Ana?”

“She is busy. We have a lead.”

Osiris grips the flight stick. His gaze slips betwixt and between points of focus.

“Do we have to leave right now?” Sagira floats into his sightline. “I’m sure Saint woul—“

Their eyes don’t meet.

“We have a long flight.”

Sagira relents. Her tone sharpens. “How long?”

KUIPER-OORT EXPANSE

||KUIPER SLINGSHOT ACHIEVED: COURSE CORRECTION; NEGATIVE; BREAK LINE TRAJECTORY FAILURE||

||ALERT: GRAVITATIONAL ANOMALY: BOW||

Solar warmth peels away into guideless vacuum as Osiris skims across the Heliopause. A hollow serenity bathes his face.

“What is it?”

Osiris breathes a sigh of relief at the sight of the anomaly.

“An answer.”

“I… feel strange.” Sagira settles from her orbit about Osiris’s shoulders, her voice crackling with interference.

“It might be best if you stay with the ship.”

“It might be best if you had better ideas.”

Osiris grunts under his breath and cuts the engines. “I won’t be long.”

|COURSE CORRECTION;NEGATIVE;BREAK LINE TRAJECTORY FAILURE||

“That’s never true.” Sagira scans the warping stillness. “There’s nothing in there, Osiris.”

“No reason to worry then.”

Sagira narrows her iris at him. “I can’t even find a point to transmat you to.”

“No matter.”

“What?” Sagira faces the anomaly. “What are you trying to prove?”

Osiris affixes a visor to his helmet and clips a localizing beacon to his belt. With a hiss, his head swims in pressurizing atmosphere.

“It has to lead somewhere.” His helmet radio vies with interference.

Sagira droops in disappointment. “Does it?”

He looks through her, eyes sullen and heavy. He nods.

A great Maw yawns before them, wicked and soft. Brilliant unfurling layers of opaque invitation. They drift. The Deep comfort hums through his skin, breeding a resilient calm. A silent static stasis boiling away at the brim of consciousness.

||COLLISION ALERT: BOW | COURSE CORRECTION;NEGATIVE;COLLISION IMMINENT||

The Anomalous Maw welcomes. It is a gullet, endless in hunger and depth that splits reality like petals opening to consume the Sun. The depth warps. Sweet flavor spins through the senses. It cradles him, locks in motionless descent, rocks away fear with warm recognition. Stretches, and wraps, and cribs.

||COLLISION ALERT: STARBOARD;BOW;ABEAM;RADIAL;AFT;BOW;ABEAM;PORT:AFT;RADIAL;PORT:BOW | COURSE CORRECTION: NEGATIVE;TRAJECTORY FAILURE;COLLISION: FAILURE||

It threads through space set adrift beyond and before, until there is only within. Within: a point. Lone and stark amid the undulating expanse. Distant, at the edges, and forward, only deeper.

Osiris a wayfaring witness. A reluctant heir. A broken promise made true. A husk to fill a throne of sustenance. A shear to prune the vine. A warden to vacancy. A mind elated and crestfallen. A sojourner of meaning ever seeking.

He turns back. Sagira’s light blinks from shaded canopy within his vessel. Starless bends weave and break through pools of luminescent memory. They flow to the point beyond.

The point grows gaunt, and if he were to reach out, he would brush the walls with his fingertips. Osiris stands in dark quiet comfort. He treads placid trim. He swims in depth lined by pale rivers of white gnashing, far below and above.

He sends forth his Echoes. Their sight finds no purchase in the gullet. They push the walls beyond his fingers and let stand only the path of want. They drift until no longer felt. The skeins neither snap nor remain.

Before him, the gnarled point softens and splits into a blooming cathedra. A metal seed laid barren in the bosom of the throne in a pool of light. A nexus. He plucks it from the pool. From its drippings spawn a rapturous light, spreading through the enormity and ravenously washing over the gullet at increasing pace.

Dark gives way to cold reflective alloy.

To logic and formless calculous.

The cathedra, overwhelmed by prediction, rings with the dull mimicked tone of congruence. They scream to Osiris. His mind. They crave, never to tire, his unique causality. They would grow, unceasing. Death to death, forever.

The path of want falls to assimilation.

Osiris flees to the safety of Sagira’s blinking light. The gullet quivers reverberation that trails his every step in sentient chromic glisten. He calls for her. To open the ship. To break the false-light wave that besets his every step. To—

“I’m glad you changed your mind.”

Sagira’s shell shines a reflection across the cockpit as Osiris’s jumpship rolls to face the Sun. “Ready to go?”

||KUIPER SLINGSHOT JUMP-LOCK: TRAJECTORY CLEAR; GREEN LINE||

“Sagira…” He grips a cold metal seed. “Yes.”

The Sun hangs dim and distant in a sea of ink. Its waning glare burns the focus out of Osiris’s eyes. Blind to all other points, they drift; engines humming in anticipation; vessel drenched in an angular shadow.

After seeing a number of publishers pull out of PAX East last week due to concerns surrounding the coronavirus outbreak, as well as the postponement of GDC, thoughts naturally fall to another major gaming event coming up in the near future: E3.

The 2020 show is set to take place this summer from 9th – 11th June, and last month saw Nintendo, Xbox, and a number of third party publishers named as attending. The coronavirus no doubt throws a spanner in the works, but E3 organiser Entertainment Software Association has issued a statement regarding its plans.

Here’s what it said to Vice:

“Everyone is watching the situation very closely. We will continue to be vigilant, as our first priority is the health, wellness and safety of all of our exhibitors and attendees. Given what we know at this time, we are moving ahead full speed with E3 2020 planning. Exhibit and registration sales are on track for an exciting show in June.”

With Sony already opting out of this year’s show, E3’s organisers will no doubt be hoping that other major names stay on board despite the current risks.

Jason Blundell, a longtime force behind Call of Duty’s various Zombies modes, has announced his departure from Treyarch.

“After thirteen fantastic, action-packed years, I am moving on from Treyarch,” writes Blundell.

“During my time at the studio, I’ve been privileged to work on a variety of projects, wearing many hats along the way, with my time on the Zombies team proving to be quite special. It’s a team comprised of some of the most creative and talented developers in the industry, many of whom you never see because they’re behind the scenes shaping the direction of each experience.”

Blundell joined the team during the development of Call of Duty 3 and has lent his talents as a producer and game director to a total of seven Call of Duty releases to-date.

Many of his efforts have seen him at the head of each entry’s Zombies mode, most recently for Call of Duty: Black Ops 3, while 2018 saw him assume the role of co-studio head alongside Dan Bunting and Mark Gordon.

We wish to thank Jason for his many contributions to Treyarch. We’ve enjoyed our journey together and wish him the very best going forward! pic.twitter.com/Tb8o36VWb2

— Treyarch Studios (@Treyarch) February 28, 2020

After close consultation with our partners in the game development industry and community around the world, we’ve made the difficult decision to postpone the Game Developers Conference this March.

Having spent the past year preparing for the show with our advisory boards, speakers, exhibitors, and event partners, we’re genuinely upset and disappointed not to be able to host you at this time .

We want to thank all our customers and partners for their support, open discussions and encouragement. As everyone has been reminding us, great things happen when the community comes together and connects at GDC. For this reason, we fully intend to host a GDC event later in the summer. We will be working with our partners to finalize the details and will share more information about our plans in the coming weeks.

For more information, please visit our Frequently Asked Questions page.

Gamasutra and GDC are sibling organizations under parent Informa Tech