Ranked, Arena, Casual, Friendly and so on. RECORD FOR: make sure you’ve checked all the game modes relevant to you, e.g.Once there, you need to check you’ve selected the right settings in the following subsections: The next step is to head on over to Options and select the Stats section. If you don’t have an Active deck, the games you play won’t be associated with any of your decks. To sort that out, simply use the instructions in the previous step. To start, you should make sure that you’re working with an Active deck. Fortunately, there are a few fixes out there-so don’t worry! Imagine a scenario, though, in which you’ve been playing several games-only to find that Hearthstone Deck Tracker’s not recording stats. There’s nothing more satisfying than finishing a Hearthstone marathon and being able to unwind and look over your stats. One of Deck Tracker’s best features is its ability to track stats-giving you information on all of your games, the cards you’ve played and even the cards your enemy has played. In this situation, what do you do? Fortunately, this issue has an incredibly simple solution-make sure you’re not filtering out the missing decks! Stats issues and fixes Hearthstone Deck Tracker stats not working Worse still, your brand new deck vanishes when you apply a tag to your decks. Unfortunately, sometimes the latest deck you’ve created simply doesn’t show up. If you click anywhere else on one of your decks, it will become SELECTED and will be highlighted with your current theme colour.īy now, you’ve figured out the ins and outs of Active and Selected decks-but what happens when you come across an issue with Hearthstone Deck Tracker not showing deck when you’ve created a brand new deck? If you’re a regular Hearthstone player, creating new decks on a regular basis is pretty standard. It will then display your deck as being ACTIVE. If you’d like to make one of your decks Active, all you’ve got to do is click USE-located to the right of the deck name. What’s the difference between an Active and Selected deck? Your Active deck is the one that you want to play with in a game, whereas your Selected game lets you view your deck without interfering with the deck that you’re already playing with. You need to have an Active Deck in order for the overlay to show you all of your remaining cards. So, why isn’t it showing the full deck? The answer lies in Active and Selected decks. You’ve been playing a few heated games of Hearthstone and everything seems to be going just fine… except, you’ve just realised that while you’ve been playing and looking at your Deck Tracker, the overlay is only showing the cards that you’ve played. HTC Vive Pro Review, Specs & Setup Guide Deck issues and fixes Hearthstone Deck Tracker not showing full deck If your specific problem happens to be Hearthstone Deck Tracker not working Windows 10, for example, then you should update drivers in Windows 10. The simplest solution, then, is to check for updates. If it hasn’t been updated recently, it could well be the reason why Hearthstone Deck Tracker has stopped working for you. As we’ve mentioned before in display driver not responding, your graphics driver is vital for games. Let’s start with how the issue might be solved on your end. Before we start looking at your Deck Tracker settings and all of the specifics, we’ll first look at the more general issues and fixes. We certainly feel your pain-there’s nothing worse than not being able to play on your favourite game. To take your mind off of things, you decide to play on your favourite strategy game-only to end up staring at your computer in horror when Hearthstone Deck Tracker does not work and crashes at start-up. So, you’ve had a long day at work or you’re just looking for a way to pass the time. It doesn’t matter if you’re dealing with Hearthstone Deck Tracker not showing cards or the Hearthstone Deck Tracker arena not working-we’ve got you completely covered! General issues and fixes Hearthstone Deck Tracker has stopped working Our latest guide takes you through all the issues related to Hearthstone Deck Tracker not working and all of the quick fixes.
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The solution ? Instead of letting anyone buy a canvas from any CC image, let uploaders opt-in with a define revenue share and sign up process. But there are some basic rules of conduct that cannot be disrupted by pure brute force just because building the tools to abide by them are too time consuming. Sure they, like others, would like to change the landscape and not be yet another stock photo agency. If Flickr is going to monetize its content via various scheme – which it is apparently starting to do aggressively – it will need to be more thorough in investigating current practices in the photography space and even hire season executives to avoid known pitfalls that are just a waste of time and resources. We know already who will win this conflict. Flickr is owned by Yahoo, a publicly traded company. The Flickr community is very vocal and has a long experience of taking to social media to very loudly voice their concerns. Users can turn against a sharing platform if they feel the terms are not fair as Flickr users did already in 2003 But if Flickr gets revenue they should share, especially if the invited members from the “Flickr Marketplace” get 51% of the sale. Users get to upload, share and store images for free already so what are they complaining about ? But perception is that each individual image remains the property of its owner – whatever rights they have forgone – and that as long as they remain free, pretty much anything is fair game. Yes Flickr built the platform and yes Flickr created the marketplace at a high cost. Huge mistake and the backlash is just starting. Most certainly because building a revenue sharing back-end was more than Yahoo wanted to invest, Flickr stuck to the legal aspect of CC ( Members who license under Creative Commons commercial licenses (CC-BY and CC-BY-SA) are embracing these free licenses and encouraging use of their photos, whether commercially or otherwise) and proceeded to sell photos without sharing the proceeds with their creators. This issue just hit Flickr this week after they extended their canvas offering to all CC-BY and CC-BY-SA “creative common”images on their site without sharing revenue with the owners of the images. Users feel they own the platform they use to share photos and that can quickly become a conflict. Mostly because you are dealing with massive volume and not people. However, when your role, day in and day out, is to find ways to monetize your content, you lose perspective and regard for the individuals behind your exchangeable assets. Both consider they should get all the credits for its success. The issue is that while start-up operators feel they have done all the work, committed users feel that they have made the platform popular. You built the platform, you created a demand, you operate the marketplace then why should you share anything with those that are uploading images for free ? Why, after all, should you reward anyone besides yourself if you can turn something that used to be free into valuable assets. In a VC-funded economy, where capital comes at a premium and revenue, however small it might be, is the most important currency after traction, sharing any income is highly frowned upon. Tech companies, those created by programmers, developers, or serial entrepreneurs do not like this idea. After all, it is the way multi hundreds of million dollars companies like Getty or Shutterstock operate as well as a multi billion industry called stock photography. There is an unwritten rule in the photo:tech space that says if you are going to monetize the content on an individual basis, you have to compensate the creators. Keep in mind that there are any number of technologies and approaches you can use to build data quality monitors, and the choices you make will depend on your tech stack. To crystalize how anomaly detection works, let’s walk through a real-world tutorial in building an anomaly detector for a very anomalous data set. Incomplete or stale data that goes unnoticed until several weeks later, affecting key marketing metricsĪ code change that causes an API to stop collecting data feeding an important new productĭata drift over time, which can be challenging to catch, particularly if your tests look only at the data being written at the time of your ETL jobs, which don’t normally take into account data that is already in a given table Unknown unknowns might include:Ī distribution anomaly in a critical field that causes your Tableau dashboard to malfunctionĪ JSON schema change made by another team that turns 6 columns into 600Īn unintended change to ETL (or reverse ETL, if you fancy) leading to tests not running and bad data being missed Unknown unknowns refer to data downtime that even the most comprehensive testing can’t account for, issues that arise across your entire data pipeline, not just the sections covered by specific tests. In Figure 4-1, we highlight popular examples of both. These issues may not happen, but with a healthy dose of testing, you can often account for them before they cause issues downstream. Known unknowns are issues that you can easily predict, i.e., null values, specific freshness issues, or schema changes triggered by a system that updates regularly. There are two types of data quality issues in this world: those you can predict (known unknowns) and those you can’t (unknown unknowns). Knowing Your Known Unknowns and Unknown Unknowns In the process, we’ll introduce important concepts and terms necessary to bulk up your understanding of important anomaly detection techniques. In this chapter, we’ll walk through how to build your own data quality monitors for a data warehouse environment to monitor and alert to the pillars of data observability: freshness, volume, distribution, and schema. Data monitoring and anomaly detection function in much the same way. Most contemporary vehicles alert you when oil, brake fluid, gas, tire pressure, and other vital entities are lower than they should be and encourage you to take action. While automobiles are vastly different from data pipelines, cars and other mechanical systems have their own monitoring and anomaly detection capabilities, too. Now, as data systems become increasingly complex and companies empower employees across functions to use data, it’s imperative that teams take both proactive and reactive approaches to solving for data quality. Up until recently, anomaly detection was considered a nice-to-have-not a need-to-have-for many data teams. For a technical deep dive, we recommend Preetam Jinka and Baron Schwartz’s report Anomaly Detection for Monitoring (O’Reilly). Assuming your website is normally up and running, of course.Ī number of techniques, algorithms, and frameworks exist and are used (and developed) by industry giants like Meta, Google, Uber, and others. When it comes to understanding when data breaks, your best course of action is to lean on monitoring, specifically anomaly detection techniques that identify when your expected thresholds for volume, freshness, distribution, and other values don’t meet expectations.Īnomaly detection refers to the identification of events or observations that deviate from the norm-for instance, fraudulent credit card behavior or a technical glitch, like a website crash. Similarly, in data, all of the testing and data quality checks under the sun can’t fully protect you from data downtime, which can manifest at all stages of the pipeline and surface for a variety of reasons that are often unaffiliated with the data itself. No matter how many tests or checks your dealership could have done to validate the health of your car, there’s no accounting for unknown unknowns (i.e, nails or debris on the highway) that might affect your vehicle. After a brief investigation, you’ve identified the alleged culprit of the loud sound-a flat tire. You pull onto the shoulder, turn on your hazard lights, and jump out of the car. Everything is fine and dandy until you hear a loud pop. “There’s nothing like that new car smell!” you think as you pull onto the highway. Based on the routine prepurchase check, all systems are working according to the manual, the oil and brake fluid tanks are filled nearly to the brim, and the parts are good as new-because, well, they are.Īfter grabbing the keys from your dealer, you hit the road. Imagine that you’ve just purchased a new car. Monitoring and Anomaly Detection for Your Data Pipelines Also included are a query builder, tools for creating, editing, and executing stored procedures, triggers, and functions, a tool to compare table data and/or query results, and SQL history.Ĭhanges Added support for using the PostgreSQL pgpass file format when authenticating JDBC connectionsAdded support for using a password file (a file containing nothing but the password) when authenticating JDBC connectionsDatabase Conversion: When converting to MySQL / MariaDB, added the ability to choose the generated SQL insert type Added Test Connection call to DB Tools -> Command Line Call GeneratorAdded Connect call to DB Tools -> Command Line Call GeneratorInformix: Added the ability to set the DBDATE connection property for setting the date formatSQL Server: DDL Generation: create index statements no longer generated for default primary key indexes when generating table DDLFirebird to PostgreSQL Table Conversion: Double and Float columns now get converted to PostgreSQL double precision columnsSalesforce: Comments are stripped from queries automaticallySQL Server: Alter table add column not supporting entering max for column lengthEdit Table Tool: If sort query results preference is set to true, causes problems with the edit table toolPostgreSQL: IN_OUT parameters not supported in the call procedure toolCall Procedure Tool: Error messages are not always displaying to the screenĬhanges Added the ability to set a client timezone via the Preferences -> Query Results Tab. 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For example, you can tell visually from the figure that the gluttonous brother in our fictional mince-pie-eating family has grown an addiction over recent years, whereas my own consumption has remained conspicuously high and consistent over the duration of data. The unstacked bar chart is a great way to draw attention to patterns and changes over time or between different samples (depending on your x-axis). If you select more than one column, Pandas creates, by default, an unstacked bar chart with each column forming one set of columns, and the DataFrame index as the x-axis. Plt.title("Mince Pie Consumption Study Results") With Pandas plot(), labelling of the axis is achieved using the Matplotlib syntax on the “plt” object imported from pyplot. No chart is complete without a labelled x and y axis, and potentially a title and/or caption. In real applications, data does not arrive in your Jupyter notebook in quite such a neat format, and the “plotdata” DataFrame that we have here is typically arrived at after significant use of the Pandas GroupBy, indexing/iloc, and reshaping functionality. For example, the same output is achieved by selecting the “pies” column: # Individual columns chosen from the DataFrame Note that the plot command here is actually plotting every column in the dataframe, there just happens to be only one. In Pandas, the index of the DataFrame is placed on the x-axis of bar charts while the column values become the column heights. To add or change labels to the bars on the x-axis, we add an index to the data object: # Create a sample dataframe with an text index Every Pandas bar chart works this way additional columns become a new sets of bars on the chart. Other chart types (future blogs!) are accessed similarly: df = pd.DataFrame()ĭf.plot.area df.plot.barh df.nsity df.plot.hist df.plot.line df.plot.scatterĭf.plot.bar df.plot.box df.plot.hexbin df.plot.kde df.plot.pie Bar labels in chartsīy default, the index of the DataFrame or Series is placed on the x-axis and the values in the selected column are rendered as bars. bar() exist on the ot object that act as wrappers around the plotting functions – the chart above can be created with (). plot(kind="bar") syntax however there are shortcut functions for the kind parameter to plot(). ()įor the purposes of this post, we’ll stick with the. It’s simple to create bar plots from known values by first creating a Pandas Series or DataFrame and then using the. Pd.Series().plot(kind="bar")Ī Pandas DataFrame could also be created to achieve the same result: # Create a Pandas series from a list of values ("") and plot it: # Import the pandas library with the usual "pd" shortcut To create this chart, place the ages inside a Python list, turn the list into a Pandas Series or DataFrame, and then plot the result using the ot command. (I have no idea why you’d want to do that!) Imagine you have two parents (ate 10 each), one brother (a real mince pie fiend, ate 42), one sister (scoffed 17), and yourself (also with a penchant for the mince pie festive flavours, ate 37). This plot is easily achieved in Pandas by creating a Pandas “Series” and plotting the values, using the kind="bar" argument to the plotting command.įor example, say you wanted to plot the number of mince pies eaten at Christmas by each member of your family on a bar chart. The simplest bar chart that you can make is one where you already know the numbers that you want to display on the chart, with no calculations necessary. %config InlineBackend.figure_format = 'retina' Getting started: Bar charting numbers # Set up with a higher resolution screen (useful on Mac) To import the relevant libraries and set up the visualisation output size, use: # Set the figure size - handy for larger output You can install Jupyter in your Python environment, or get it prepackaged with a WinPython or Anaconda installation (useful on Windows especially). As with most of the tutorials in this site, I’m using a Jupyter Notebook (and trying out Jupyter Lab) to edit Python code and view the resulting output. 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