Greenelk representing geodata, in and around python mariadb australian us dollar exchange rate

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The overall use case is the application ecosystem of geodata for the purpose of outdoor sports, with a dash of travel planning and travel photography exchange rate of indian rupee to us dollar. The end user need is to plan, execute and document adventures, where locations, tracks and maps play a central role.

My recent eager geodata blog entries are prompted by exciting stuff I’m doing to visualise sports adventures on maps. The hacking is done in Python, but I am less than certain it’s good code. So I allow myself to ask another question: How should I represent geodata objects, for maximum usability during editing and data interchange? And most of all: How should geodata be elegantly represented in the Python code itself? Scenario and use case: gps location, track and map data

The overall use case is the application ecosystem of geodata for the purpose of outdoor sports, with a dash of travel planning and travel photography.


The end user need is to plan, execute and document adventures, where locations, tracks and maps play a central role.

Given today’s technology, the possibilities how to plan, execute and document adventures are plentiful and, honestly, not even fully understood. Adding tomorrow’s technology, the result is a dynamic set of requirements.

The end result is a scenario that requires prototyping python dictionary. Quick code snippets are hacked and tested. Snippets are improved and extended. Needs are covered better, and more needs are covered. Roles in the target audience

A distinction between developers and end users exists, but is somewhat blurred eur usd exchange rate history. Few outdoor enthusiasts are expected to be able or available to write code, but many are known to have a technical view on editing and interchanging data. Even more are pure end users.

On the continuum between end user and developer, individuals can certainly move over time, but let’s simplify by identifying three categories (and their roles during prototyping):

Techies (content creators). A smaller but still large group forex rates in pakistan. Accept (or even like) character based editors and the command line. Have some IT skills and a technical interest hkd usd peg. Often coders and developers of other applications.

Until needs are well understood, only techies are expected to create content in the form of describing placemarks, uploading and editing tracks, and planning trips. At some point, as needs are stable and tools mature, part of the resources will be devoted to enabling surfers to create new content nzd to usd chart. When and how this happens is outside the scope of this text. Surfer requirements and needs

Offline: Availability of functionality when not connected to the internet (which can be either flaky or expensive when you’re on an outdoor expedition). This means that extra-to-be-installed Python packages are undesirable. Standard rulez.

Usability: Across all the other requirements (Offline, Trackers, Import, Edit, Export), data should be dead easy to edit, control, and keep track of.

Multi-language: Python is the language chosen, for the foreseeable future. But one day, we may want to create an adventure viewer and browser on a platform where Python isn’t supported 100 usd in euro. Today, we should choose clever interchange formats.

Metadata: No redundancy when describing data structures. Have only one place where data is described (such as a JSON), and make all other places use it, with as little code as possible.

Mapping: Have simple mapping mechanisms between data representations chf usd. Make it easy to import and export between code objects and various file formats, such as KML and the various CSV and JSON formats described below.

KML: Keyhole Markup Language is an XML schema for expressing geographic annotation and visualisation within Internet-based Earth browsers, primarily Google Earth and Google Maps.

HTML: HyperText Markup Language is the main markup language for creating web pages and other information that can be displayed in a web browser.

GeoJSON: GeoJSON is an open standard format for encoding collections of simple geographical features along with their non-spatial attributes using JavaScript Object Notation (.json).

Cesium: Cesium Language (CZML) [note: links to github, not Wikipedia] is a JSON schema for describing dynamic scenes in virtual globes and maps

We want to represent coordinates (in placemarks and time-stamped trackpoints), paths (sequences of coordinates), tracks (sequences of trackpoints) and other entities in Python code. But how? As objects? lists? dicts? What are the advantages of each one of them?

Given the coder requirement of non-redundancy (the “ metadata” need above), and the vague assumption that JSON is the shit (= the data representation of choice), which Python concept can best be mapped onto a JSON object? Would this requirement help us choose between Python objects, lists and dicts?

From the way I asked the question, you may decipher that I am not quite 100% up to speed on Python. I do understand that Python objects are complex, and that you can fine tune their exact structure gold vs usd chart. But while I did most of my coding last century, I am still a simple man in need of an elegant answer.

By the way, part of a great answer would also tell me what Python modules I need to import (such as import json, xml.etree.ElementTree as ET), with a strong preference for standard modules, as I want any techie to be able to run the scripts without installation hassle (remember the “ Offline” techie need above). Representing data in flat files

We want to represent the geodata in flat files, in order for the techie end user to use his favourite text editor to annotate adventures, plan tracks, give metadata on placemarks and do other stuff not easily accomplished in an Earth viewer or web app, which is constrained by a rigid graphical user interface (see, I did do most of my coding in the character based past century).

The XML and even JSON based flat files are fine, but wordy. I coded a simple Python snippet for converting KML to CSV, and used it on my sample KML file with a thousand placemarks. What happened? The CSV file ended up with 15 % of the KML file size in bytes, and 5 % the number of lines. How this satisfies the “ Edit” and “ Usability” needs of the techie should be fairly clear: Within one eyeful, you get a much better overview. Much less error prone, much faster to edit, much quicker to sort. Perfect edit usability for techies! So what am I asking you?

While I did enough coding last century to be quite comfortable with defining the greenelk comma-separated value format loosely basing it on RFC 4180, I am only somewhat comfortable, if at all, with the other choices made so far. And I would like to verify them with you.

I am somewhat comfortable with JSON as the core format to define the data structures (for coordinates, placemarks, trackpoints, paths, tracks et al.).

I am not at all comfortable with how to best represent JSON (GeoJSON or otherwise) in Python code convert ip to binary. How should I map the JSON components – on objects, lists, dicts?

One of the company founders, Kaj Arnö has been with the company from the start in October 2010. Kaj is a software industry generalist, having served as VP Professional Services, VP Engineering, CIO and VP Community Relations of MySQL AB prior to the acquisition by Sun. At Sun, Kaj served as MySQL Ambassador to Sun and Sun VP of Database Community. Kaj is board member of Carus Ltd Ab (Åland) and Footbalance System Oy (Finland) and was founder, CEO and 14 year main entrepreneur of Polycon Ab (Finland). His non-IT passion is to experience the world with his own muscle power.

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