• Cogs Now Available For Mac Download

. Fundamental. Valuation Ratio. Profitability. Price.

Dividends. Income Statement. Balance Sheet. Cashflow statement.

= ( - ) / = (62900 - 38816) / 62900 = 38.29%. All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency. A company that has a moat can usually maintain or even expand their Gross Margin.

A company can increase its Gross Margin in two ways. It can increase the prices of the goods it sells and keeps its Cost of Goods Sold unchanged. Or it can keep the sales price unchanged and squeeze its suppliers to reduce the Cost of Goods Sold. Warren Buffett believes businesses with the power to raise prices have moats. Cost of Goods Sold is also directly linked to another concept called Inventory Turnover: Apple Inc's for the three months ended in Sep. 2018 is calculated as.

= / = 38816 / 4946 = 7.85. All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency. Inventory Turnover measures how fast the company turns over its inventory within a year.

A higher inventory turnover means the company has light inventory. Therefore the company spends less money on storage, write downs, and obsolete inventory. If the inventory is too light, it may affect sales because the company may not have enough to meet demand. Usually retailers pile up their inventories at holiday seasons to meet the stronger demand. Therefore, the inventory of a particular quarter of a year should not be used to calculate inventory turnover. An average inventory is a better indication. Related Terms.

Cogs Now Available For Mac Download

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Introduction is a simple code generation tool written in Python. We use it or its results every day in the production of Kubi.

Is a collaboration system embodied in a handful of different products. We have a schema that describes the representation of customers' collaboration data: discussion topics, documents, calendar events, and so on. This data has to be handled in many ways: stored in a number of different data stores, shipped over the wire in an XML representation, manipulated in memory using traditional C objects, presented for debugging, and reasoned about to assess data validity, to name a few. We needed a way to describe this schema once and then reliably produce executable code from it. The Hard Way with C Our first implementation of this schema involved a fractured collection of representations. The XML protocol module had tables describing the serialization and deserialization of XML streams. The storage modules had other tables describing the mapping from disk to memory structures.

The validation module had its own tables containing rules about which properties had to be present on which items. The in-memory objects had getters and setters for each property.

It worked, after a fashion, but was becoming unmanageable. Adding a new property to the schema required editing ten tables in different formats in as many source files, as well as adding getters and setters for the new property.

There was no single authority in the code for the schema as a whole. Different aspects of the schema were represented in different ways in different files. We tried to simplify the mess using C macros. This worked to a degree, but was still difficult to manage.

The schema representation was hampered by the simplistic nature of C macros, and the possibilities for expansion were extremely limited. The schema tables that could not be created with these primitive macros were still composed and edited by hand.

Daftar lengkap office home and business 2016 for mac. Office 2016 feels like a completely new and very Mac-friendly application suite that might just replace Pages, Numbers, Keynote and the rest of the Apple office suite on our Mac systems here in the office.

Changing a property in the schema still meant touching a dozen files. This was tedious and error prone. Missing one place might introduce a bug that would go unnoticed for days. Searching for a Better Way It was becoming clear that we needed a better way to manage the property schema. Not only were the existing modifications difficult, but new areas of development were going to require new uses of the schema, and new kinds of modification that would be even more onerous.

We'd been using C macros to try to turn a declarative description of the schema into executable code. The better way to do it is with code generation: a program that writes programs.

We could use a tool to read the schema and generate the C code, then compile that generated code into the product. We needed a way to read the schema description file and output pieces of code that could be integrated into our C sources to be compiled with the rest of the product. Rather than write a program specific to our problem, I chose instead to write a general-purpose, although simple, code generator tool. It would solve the problem of managing small chunks of generator code sprinkled throughout a large collection of files. We could then use this general purpose tool to solve our specific generation problem. The tool I wrote is called Cog.

Its requirements were:. We needed to be able to perform interesting computation on the schema to create the code we needed. Cog would have to provide a powerful language to write the code generators in.

An existing language would make it easier for developers to begin using Cog. I wanted developers to be able to change the schema, and then run the tool without having to understand the complexities of the code generation.

Cog would have to make it simple to combine the generated chunks of code with the rest of the C source, and it should be simple to run Cog to generate the final code. The tool shouldn't care about the language of the host file. We originally wanted to generate C files, but we were branching out into other languages. The generation process should be a pure text process, without regard to the eventual interpretation of that text.

Because the schema would change infrequently, the generation of code should be an edit-time activity, rather than a build-time activity. This avoided having to run the code generator as part of the build, and meant that the generated code would be available to our IDE and debugger.

Code Generation with Python The language I chose for the code generators was, of course, Python. Its simplicity and power are perfect for the job of reading data files and producing code.

To simplify the integration with the C code, the Python generators are inserted directly into the C file as comments. Cog reads a text file (C in our case), looking for specially-marked sections of text, that it will use as generators. It executes those sections as Python code, capturing the output. The output is then spliced into the file following the generator code. Because the generator code and its output are both kept in the file, there is no distinction between the input file and output file.

Cog reads and writes the same file, and can be run over and over again without losing information. Cog processes text files, converting specially marked sections of the file into new content without disturbing the rest of the file or the sections that it executes to produce the generated content. In addition to executing Python generators, Cog itself is written in Python. Python's dynamic nature made it simple to execute the Python code Cog found, and its flexibility made it possible to execute it in a properly-constructed environment to get the desired semantics. Much of Cog's code is concerned with getting indentation correct: I wanted the author to be able to organize his generator code to look good in the host file, and produce generated code that looked good as well, without worrying about fiddly whitespace issues.

Python's OS-level integration let me execute shell commands where needed. We use Perforce for source control, which keeps files read-only until they need to be edited. When running Cog, it may need to change files that the developer has not edited yet. It can execute a shell command to check out files that are read-only. Lastly, we used XML for our new property schema description, and Python's wide variety of XML processing libraries made parsing the XML a snap. How It Worked Out In a word, great. We now have a powerful tool that lets us maintain a single XML file that describes our data schema.

Developers changing the schema have a simple tool to run that generates code from the schema, producing output code in four different languages across 50 files. Where we once used a repetitive and aggravating process that was inadequate to our needs, we now have an automated process that lets developers express themselves and have Cog do the hard work. Python's flexibility and power were put to work in two ways: to develop Cog itself, and sprinkled throughout our C source code to give our developers a powerful tool to turn static data into running code.

Although our product is built in C, we've used Python to increase our productivity and expressive power, ease maintenance work, and automate error-prone tasks. Our shipping software is built every day with Python hard at work behind the scenes. More information, and Cog itself, is available at.