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@amithegde·9 public vizzes
A heatmap displays the Crazy Calendar spread strategy's weekly income potential across a grid of trade entry dates and option strike prices. The visualization uses D3.js scales and axes to map the calendar, with cell colors representing expected profit or loss from the strategy's documented historical performance data. The code renders the matrix as colored rectangles and includes interactive hover tooltips that reveal exact values for each combination.
This custom stacked row chart displays server uptime and health across time intervals, with each row representing a server and colored segments indicating health levels from good to bad. The chart uses D3.js version 3.5.5 to render SVG elements, mapping server data with d3.scale.linear for the time axis and d3.scale.ordinal for server rows. Health levels are encoded with four warning colors, and values exceeding three are clamped to the darkest red. The visualization includes labeled axes, dashed guide lines, and a legend explaining the color-to-health mapping, with data provided as inline JavaScript objects.
This example demonstrates how to capture Event Tracing for Windows (ETW) events in real time using a trace session. It creates a `TraceEventSession` and an `ETWTraceEventSource`, then attaches a `DynamicTraceEventParser` whose `All` handler prints every received event to the console. The code enables a specific provider by GUID before processing the session. It relies on the Microsoft.Diagnostics.Tracing.TraceEvent NuGet package to parse events dynamically, showing the basic plumbing for consuming ETW data without a pre-defined event schema.
This example shows three charts—column, line, and bar—rendered from the same randomly generated dataset of 25 items with double-precision values. The helper class LpChart wraps the System.Windows.Forms.DataVisualization.Charting Chart control, binds X and Y series data via DataBindXY, and draws the chart to a Bitmap using DrawToBitmap before dumping it to the LinqPad output panel. Charts are instantiated with different widths (1250, 1250, and 500 pixels) to demonstrate layout flexibility.
This example demonstrates a rate-limiting pattern for executing a method a specified number of times per second, referred to as transactions per second (tps). It shows how to batch and parallelize task execution using `Task.Factory.StartNew`, `Parallel.ForEach`, and `Thread.Sleep` to maintain the target throughput over a given duration. The code defines a `Tasker` class that splits the tps into four batches, each run concurrently with a maximum degree of parallelism of three, and prints the batch counts and task execution messages to the console.
This example shows Azure Table Storage query results as a flattened, tabular dump of dynamic entities, displaying each row’s PartitionKey, RowKey, ETag, Timestamp, and all custom properties. The code uses the Azure Storage SDK’s `CloudTable` and `TableQuery<DynamicTableEntity>` to fetch data, then a custom `ToDynamicList` extension converts each entity into an `ExpandoObject`. It handles all `EdmType` property values—Binary, Boolean, DateTime, Double, Guid, Int32, Int64, and String—and fills missing properties with empty strings. The final output is rendered via LINQPad’s `Dump` method.
This example demonstrates grouping timestamped data records into one-hour time intervals based on a predefined range of time bins. It shows how to aggregate 100 data items with timestamps into dictionary keys representing the end of each hourly interval, using LINQ’s `GroupBy` and `ToDictionary` methods. The code defines `TimeInterval` objects with start and end boundaries, then applies a custom `GetKey` function that matches each item’s timestamp to the appropriate interval. A `DateTimeOffsetHelper` class provides rounding utilities to align interval boundaries to exact hours. The result displays the grouped data structure in the output.
This example demonstrates a live query parser for filter expressions, showing each parsed column name and its associated value in real time as the user types. The interface accepts queries in the format `colName: value`, with optional double quotes around values, and displays the extracted pairs below the input field. The implementation relies on jQuery for DOM manipulation and event handling, while custom regular expressions in JavaScript identify column names and isolate their corresponding values. The parser handles multiple columns by using sequential regex matches, stripping quotes from values and ignoring whitespace-only entries.
This example demonstrates a custom jQuery UI Tooltip interaction on a single input field. The tooltip appears on hover and remains visible when the user moves the mouse onto the tooltip itself, with a 100-millisecond delay before closing on mouse leave. The code uses jQuery’s `.tooltip()` API, custom `open` and `mouseleave` events, and global `mouseenter`/`mouseleave` handlers on `.ui-tooltip` to manage close timers. The tooltip’s title attribute provides the static text content, and all other tooltips are closed when a new one opens.