Project Igi Compressed Download

[Caterina Haggins]

Tosave disk space, you can ZIP the backup project after saving - typically that can reduce the project file size to about half.

You will need to extract the project from the ZIP file to work on it.

During compression, EPLAN checks which project data is used in the project and whether the project data is consistent. All of the project data that is not used and is part of the scope of the compression is deleted. The settings for the scope of compression are stored in schemes. This allows you to save the settings you use most often for easy reuse.

For some background, I am compiling a large list of all the active projects in a certain time period. All these projects compete for the same few resource groups, which I'll call Apples, Bananas, and Cats. These resources are used sporadically throughout all projects, but sometimes only for a single day, with long gaps with other irrelevant resources between them. I have the resources color coded, such that when a resource is entered for a certain task, the task bar changes to a corresponding color.

However, if considering a project where say, I will need Apples 8 times throughout a 3 month period, the gantt chart gets a little long, and with 20 projects under similar circumstances, this is where the problem arises.

I'm trying to figure out a way to compress the projects into a single color coded bar, similar to the one I've crudely drawn in Microsoft Paint. Then, I'd like to print MANY of these style of bars for MANY projects. Any advice?

Within a given project, you can use the native "timeline bar" functionality. This may work for you if you have those resources assigned to distinct tasks. Also, you can apply formatting to the bars so colors shouldn't be hard.

However, to compile a view with multiple projects and if you have them in Project Online, you're looking more at using the odata feeds for assignments. I'd guess PowerBI would probably be your best friend, but you might be able to do some visualization directly in excel.

I looked at the Wikpedia page for the Unreal Engine and saw a list of all the games made using the Unreal engine 4, so I decided to buy a couple of them (Soul Calibur VI and Snake Pass) for ideas/inspiration because I plan on making my own game.

I imported roughly 1.3 GB worth of textures into a blank project (thirty-three 8192x8192 textures), and made a material for 11 of them (because the other 22 were the specular and normal maps of the material) and the final build size (even with compressed cooked content) was about 890 MB. And the project folder size was 10 GB! Is it normal for Unreal Projects to be this huge?

When you create a build out of UE4 it already compresses the files, so definitely the files are compressed in Soul Calibur, the question would be if they do more compression or if they even implement their own version of compression instead.

For GPU memory the texture dimensions control the amount of memory it uses even if the source file is compressed and small, so 4x 1024 textures is equal to one 2048 texture

As far as atlases go, UE4 does some of that itself

All AAA games and UE4 with defaults settings use compressed textures.(DXT) This make texture already 4 to 8 times smaller in size. Cooked package is also compressed which further reduce size of textures around to 50%. Atlases does not save any space and actually can waste memory because of inefficient packing and required padding to avoid bleeding. They also cause texture streaming to be more inefficient. Only use atlases if you can save a lot of draw calls by using them.

For minimal memory size and maximal quality we pack our textures like this. Texture1 has albedo in rgb and roughness in a , we use default compression for this.(BC3) Texture2 has just normal info and we use default normal map compression which just stores r and g channel and compress them individually.(BC5) This is most important texture and usually biggest in size. Then we have optional metalness texture using Alpha compression.(BC4) This is usually really small like 25% of the albedo/roughness texture. Then we have optional emissive texture which uses default compression without alpha channel.(BC1) This is usually half of the size of the albedo texture. To hide compression artefacts and low res look we usually have really small(64-128) detail normal map with really small weight just to add some additional detail in close ups.

The eight-hour duration Willow Rock Energy Storage Center has been slated to come online in Kern County in 2028 and recently secured an offtake agreement from a co-operative utility for 40% of its energy (200MW/1,600MWh), reported by Energy-Storage.news.

It recommended that CEC staff pause active work on the long duration energy storage (LDES) project until Hydrostor submitted a new project proposal backed up by new reports confirming site viability, and then a week later representatives for the Hydrostor told the CEC:

The firm is also developing another project in California, the 400MW/3,200MWh Pechos Energy Storage Center in San Luis Obispo County, as well as the 200MW/1,500MWh Silver City Energy Storage Center in Broken Hill, New South Wales, Australia.

We are exploring a number of locations in Kern County and exploring multiple opportunities and locations in parallel. Hydrostor believes Kern County offers a bright future for energy storage investment.

The benefits of additional sites that Hydrostor already controls further to the east of the current Willow Rock location includes opportunities to potentially reducing the environmental impacts as well as ideal geologic conditions, which will result in improved certainty and potential savings to overall schedule and cost of the project.

Which situation is the best (occupies the least space)? Why? Does it depend on compression algorithm? I know that compressing one compressed file cannot help much, but let's say 20 of them? For me situation 1 doesn't look like a good idea.

You might however look at something like RAR which allows redundancy and split archives. This is a bit like RAID5. You create multiple archive files each of which has built in redundancy so that you can loose a file and still recreate the original data.

Also, your 3rd approach can indeed lead to another reduction in size. I remember some discussion (see here) about compressing files multiple times using different algorithms. The author was compressing highly redundant text files and could go from 100GB to a few MB after experimenting enough. Note that his case was a bit special, but the general idea is that iterated compression can actually be worthwhile in some cases.

Between Situations 1 and 2 the latter definitely has more chance of resulting in a smaller archive, especially when you use larger dictionary sizes (the dictionary in simple words is the memory area used to find and compress repeated patterns in data). Plain old ZIP can only use a tiny 32KB dictionary, which given the hardware these days is way too small.

If this option is enabled, WinRAR analyzes the file contents before starting archiving. If several identical files larger than 64 KB are found, the first file in the set is saved as usual file and all following files are saved as references to this first file. It allows to reduce the archive size, but applies some restrictions to resulting archive. You must not delete or rename the first identical file in archive after the archive was created, because it will make extraction of following files using it as a reference impossible. If you modify the first file, following files will also have the modified contents after extracting. Extraction command must involve the first file to create following files successfully.

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