As you can see, there are some reasonably comprehensible values, like `-0.50357585`, `-0.222`, `0.075432`, but there's also values like `-1.1984697154842467e-66` and `1.9052595476929043e-65`. Mathematically, these values end up being continous and suitable for generating a noisemap but for a human being doing development work and examining raw data, it's almost impossible to have any intuitive grasp of the numbers I'm seeing. Furthermore, when I pass these values to a visualization tool or serialize them to a storage format, I want them to be meaningful and contextually "sane". The noisemap values describe the absolute height of terrain at a given (X,Y) coordinate pair. If we assume that terrain hight is measured in meters, a world whose total height ranges between -1 meter and 1 meter isn't very sensible. A good visualization tool can accomodate this data, but it's not good enough for my purposes.
As you can see, there are some reasonably comprehensible values, like `-0.50357585`, `-0.222`, `0.075432`, but there's also values like `-1.1984697154842467e-66` and `1.9052595476929043e-65`. Mathematically, these values end up being continuous and suitable for generating a noisemap but for a human being doing development work and examining raw data, it's almost impossible to have any intuitive grasp of the numbers I'm seeing. Furthermore, when I pass these values to a visualization tool or serialize them to a storage format, I want them to be meaningful and contextually "sane". The noisemap values describe the absolute height of terrain at a given (X,Y) coordinate pair. If we assume that terrain height is measured in meters, a world whose total height ranges between -1 meter and 1 meter isn't very sensible. A good visualization tool can accomadate this data, but it's not good enough for my purposes.
To that end, I'm working on implementing a quantization function to scale the [-1,1] float values to arbitrary user defined output spaces. For example, a user might desire a world with very deep oceans, but relatively short mountain features. They should be able to request from the map generator a range of [-7500, 1000], and Quantize() should evenly distribute inputs between those desired outputs.
@ -37,7 +37,7 @@ This query looks at the metric `http_request_duration_microseconds`, buckets it
## Labels
Prometheus lets you apply labels to your metrics. Some are specificed in the scrape configurations; these are usually things like the hostname of the machine, its datacenter or geographic region, etc. Instrumented applications can also specify labels when generating metrics; these are used to indicate things known at runtime like the specific HTTP route ( e.g. `/blog` or `/images/kittens` ) being measured.
Prometheus lets you apply labels to your metrics. Some are specified in the scrape configurations; these are usually things like the hostname of the machine, its datacenter or geographic region, etc. Instrumented applications can also specify labels when generating metrics; these are used to indicate things known at runtime like the specific HTTP route ( e.g. `/blog` or `/images/kittens` ) being measured.
Prometheus queries allow you to specify labels to match against which will let you control how your data is grouped together; you can query against geographic regions, specific hostnames, etc. It also supports regular expressions so you can match against patterns instead of literal strict matches.
Nick Dumas
3 months ago