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  • Viewing a large-resolution VNC server through a small-resolution viewer in Ubuntu

    - by Madiyaan Damha
    I have two Ubuntu computers, one with a large screen resolution (1920x1600) that is running the default Ubuntu VNC server. I have another computer that has a resolution of about 1200x1024 that I use to VNC into the server using the default Ubuntu VNC viewer). Now everything works fine except there are annoying scrollbars in the viewer because the server's desktop resolution is so much higher than the viewer's. Is there a way to: Scale the server's desktop down to the viewer's resolution. I know there will be a loss of image quality, but I am willing to try it out. This should be something like how Windows Media Player or VLC scales down the window (and does some interpolation of pixels). Automatically shrink the resolution of the server to the client's when I connect and scale the resolution back when I disconnect. This seems like a less attractive solution. Any other solution that gurus out there use? I am sure someone has experienced this before (annoying scroll bars) so there must be a solution out there.

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  • Raid-5 Performance per spindle scaling

    - by Bill N.
    So I am stuck in a corner, I have a storage project that is limited to 24 spindles, and requires heavy random Write (the corresponding read side is purely sequential). Needs every bit of space on my Drives, ~13TB total in a n-1 raid-5, and has to go fast, over 2GB/s sort of fast. The obvious answer is to use a Stripe/Concat (Raid-0/1), or better yet a raid-10 in place of the raid-5, but that is disallowed for reasons beyond my control. So I am here asking for help in getting a sub optimal configuration to be as good as it can be. The array built on direct attached SAS-2 10K rpm drives, backed by a ARECA 18xx series controller with 4GB of cache. 64k array stripes and an 4K stripe aligned XFS File system, with 24 Allocation groups (to avoid some of the penalty for being raid 5). The heart of my question is this: In the same setup with 6 spindles/AG's I see a near disk limited performance on the write, ~100MB/s per spindle, at 12 spindles I see that drop to ~80MB/s and at 24 ~60MB/s. I would expect that with a distributed parity and matched AG's, the performance should scale with the # of spindles, or be worse at small spindle counts, but this array is doing the opposite. What am I missing ? Should Raid-5 performance scale with # of spindles ? Many thanks for your answers and any ideas, input, or guidance. --Bill Edit: Improving RAID performance The other relevant thread I was able to find, discusses some of the same issues in the answers, though it still leaves me with out an answer on the performance scaling.

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  • MySQL Master-Master w/ multiple read slave cost effective setup in AWS

    - by Ross
    I've been evaluating Amazon Web Services RDS for MySQL and costing out potential scenarios involving a simple multi-AZ deployment read/write setup vs. a multi-AZ deployment mysql master (hot-standby) with additional read-only slaves. the issue I'm trying to cost-optimize includes their reserved instance vs on-demand instances. Situation 1: purchase reserved multi-az setup for Extra-large-hi-mem(17GB RAM) instance for $5200/yr and have my application query the master all the time. the problem is, if I don't need all the resources of the (17GB RAM) all the time and therefore, especially not a hot-standby, what alternatives for savings can a better topology create, like potentially situation 2 below: Situation 2: purchase reserved multi-az setup using smaller master instances than above for the master-master hot-standby to receive the writes only. Then create and load balance several read-only slaves off the master and add/remove and/or scale up/down the read slaves based on demand. This might only cost $1000 + the on-demand usage of the read slaves. My thinking is, if I have a variable read-intensive application load, with low write load, the single level topology in situation 1 means I'm paying for a lot of resources at the write level of topology when I don't need them there. My hope is that situation 2 can yield cost savings from smaller reserved instances on the master-master resource level allowing me to scale up and down and/or out on the read-level according to demand as needed. Does anyone see a downside to doing this or know of some reason this isn't possible with RDS? Any other thoughts or advice always welcome of course. Thanks in advance, R

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  • FFmpeg overlay two videos, one input with transparency

    - by Gian B.
    I am trying to create a karaoke from a CD+G file (converted to AVI using FFmpeg) and add a video as a background of the lyrics. Here's a screenshot of a the output from CD+G conversion, for simplicity let's call this lyrics.avi http://imgur.com/wUwHUhV Now a have this video.mp4 file that I'd like to put behind this lyrics.avi Here's a sample of what I'm trying to achieve http://imgur.com/8GuWXtQ I'm sure most of you are familiar with karaoke. I haven't used FFmpeg much and I'm not really sure if what I want to achieve is possible with FFmpeg. Is it possible to overlay two videos, and add a transparency to one of the videos? In this case the colour black? How can I set the offset time of the lyrics.avi? Here's the command the I've tried so far: ffmpeg -i video.mp4 -i lyrics.avi -filter_complex "nullsrc=size=854x480 [base]; [0:v] setpts=PTS-STARTPTS, scale=854x480 [upperleft]; [1:v] setpts=PTS-STARTPTS, scale=854x200 [bottomleft]; [base][upperleft] overlay=shortest=1 [tmp1]; [tmp1][bottomleft] overlay=shortest=1:y=280" -c:v libx264 -y karaoke.mp4

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  • Need a recommendation for shared storage on auto-scaling ec2 w/ scalr

    - by john h.
    I have come across so many answers to this question that I am completely lost! I am moving our 2 sites to a load balanced ec2 system with scalr as our cloud manager. Now the question is coming up about persistent storage for the user's uploaded content and other files. Could someone please give me a suggestion and possible a link to a tutorial for the following setup and goals. 2 websites (1 Forum, 1 ecommerce). 1 LB 1 App server (to scale out to as many as needed) 1 DB server (to scale out to as many as needed) Our sites will need to autoscale and according to what I am learning about scalr, that means as new instances load up, I need to run a script to set the basics up on that server (git,php mods, pull site from git, move keys, etc) What I don't understand is how should I handle user uploaded content like profile pictures, avatars, product images, themes, etc... Do I mount an EBS or s3fs folder to hold the websites (maybe /var/www/websitefolder) or do I do something like mount the avatar folders /var/www/websitefolder/images/avatars) I am not sure where to go with this. Could someone give me some detailed help? -John

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  • How to embed/hardcode SRT subtitles into mp4 videos with VLC?

    - by Jens Bannmann
    I'm looking for a way to "burn in" or render/rembed/hardcode subtitles (from an SRT file) into an MP4 video with VLC. But no matter what options I use, it never works properly. I get a file that plays video way too fast (audio is normal), or one that plays normally, but actually does not have embedded subtitles. Also, with some options (like the one below) it does not play in QuickTime, only in VLC. So the main question is: how can I make this work in VLC? Secondary questions are: How do I decide which options I should set? Which settings are best if I want to leave the file bitrate etc. the same as much as possible, only embed subtitles? It seems I cannot leave the field empty or Video/Audio unchecked, so I guess I would first need to figure out the original audio and video bitrate. What do the "Scale" and "Channels" options mean? ... none of which are answered within the VLC documentation. For example, this is one set of options I used in the "Advanced Open File…" dialog: Advanced Open File… myFileName.mp4 [ ] Treat as a pipe rather than as a file [x] Load subtitles file: mySubtitleFileName.srt [ ] Play another media synchronously [x] Streaming/Saving Streaming and Transcoding Options [ ] Display the stream locally (o) File [outputFileName.mp4 ] [ ] Dump raw input Encapsulation Method: (MPEG 4 ) Transcoding options [x] Video (mp4v ) Bitrate (kb/s) [256 ] Scale [1 ] [x] Audio (mp3 ) Bitrate (kb/s) [128 ] Channels [1 ]

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  • What is the best cloud technology to use for MongoDB/GridFS database servers

    - by Nerian
    We are going to launch a service that will require between 1 and 2 GB for file storage per paid user. I am going to use GridFS for storing files. GridFS is a module for MongoDB that allows to store large files in de database. I am pondering the different options for storing the database. But since I am unexperienced at deployment and it is my first time with Mongodb I need your experience. Criteria: I want to spend my time developing my core business, that is, my own application. I am a Ruby on Rails developer. I do not like to mess with server configuration. Hence, I would like a fully managed hosting solution. But I would like to know about any other option, if you think it is worth it. It should be able to scale. Cloud style. Pay as you go. The lower the price, the better. So far I known of these services: https://mongohq.com/pricing https://mongomachine.com/pricing https://mongolab.com/about/pricing/ http://cloudcontrol.com/add-ons/mongodb/ And they seem to be OK for common needs, that is no file storage. But I am going to use GridFS, so the size matters. These services seems to scale, in price, quite poorly. MongoHQ: The larger plan max storage is 20 GB. Seems like a very little storage, for GridFS. MongoMachine: Flat price, 2.5$ per GB. I didn't found the limit. Seems like a good price, comparing the others. MongoLab: 3.984 GB max, which I don't think I will hit, so perfect. 8$ per GB, quite costly. CloudControl: The larger plan is 20 Gb. The custom service starts at 250€ plus some unspecified charge per GB. What is your experience with these services? Any downtimes? Other possibilities? Edit: Added meaning of GridFS

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  • AWS: Multi-region setup using single RDS instance

    - by Ion
    I'm trying to scale our web application (PHP, MySQL, memcache) in a multi-region scheme. Currently we are using a setup with two EC2 instances behind an ELB and an RDS instance, all of them in US-EAST (Virginia) region. We would like to have a presence in the EU (Ireland) region as well. This means at least a new EC2 instance there (identical to the others, serving the same application). I have copied the desired AMI, setup the new instance, setup a same ELB configuration (required for SSL termination) and configured latency-based routing in Route53. And it works as suggested. But, clients from EU have speed problems. This is due to the fact that the EU EC2 instances connect to the US-based RDS instance. As far as I know Amazon has not yet enabled RDS multi-region replication. Do you have any suggestions on how to properly speed up the whole setup while using the single RDS instance? Also, any ideas in general on how to scale things up? Ideally we would like to continue using the RDS technology for various reasons. Nevertheless, I am open to suggestions (I guess the next idea would be to host our own MySQL servers).

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  • What are good systems for managing PHP/MySQL infrastructure?

    - by sbrattla
    I work in a company which is about to migrate most applications from in-house custom built Java/Tomcat applications to Drupal. Due to company policies, applications and websites need to run on in-house servers. This means that we need infrastructure for Drupal (PHP/MySQL) applications. This must have been solved a million times already. I believe this is what web-hosting companies does every day. Even though we work on a much smaller scale than web-hosting companies, i assume it would make sense to look at the task as if we're going to have an internal small-scale web-hosting company. This means that the guys in IT operations could be "responsible" for "offering" web-hosting, while developers could use these "services". We have three environments; dev(elopment), test and prod(uction). It would make sense that developers could log in to a system and create/edit/delete dev and test sites as they'd like. Production sites should be available through the same system, but only available to IT ops. We need to work with clusters of web servers, meaning that an administration system should be capable of creating/editing/deleting sites across multiple servers. I know there's no "this is it" answer to my question; but what would be a good place to start to get going with this? Apart from the actual hardware, what would be a good administration system for this?

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  • when to upgrade server to include more cores, versus more processors, versus additional server?

    - by gkdsp
    The server hosting market is separated into single, double, qual, etc., processors, where each processor has several cores, or CPUs. My company will offer a Linux-based web application that relies on an Apache web server and a middle tier for business logic. The middle tier is used to crunch math, and return result to a client. Many clients may access the application simultaneously. The company will start with one processor having 4 cores. I'm trying to understand how the app uses the cores and then how to scale the application as business grows, in terms of servers/processors/cores. For example, I'd assume initially one core would be used for Apache, and the other 3 used to process client's requests for math crunching... Question 1: does that mean, with the 3 cores available, I can handle 3 separate client requests simultaneously (e.g. 1 for each of 3 cores)? I mean, except for the shared RAM, is this effectively like having 3 individual machines (from pt of view or processing client requests simulaneously)? Or, only one client's request may be processed at any one time, but that client's request is divided up into up to 3 cores depending on the type of process running that does the math crunching and whether or not it can take advantage of multi threading (so the # of cores impacts how fast any one client request completes)? I'm confused about what the cores mean to the application here. Question 2: As the business grows and more client requests need to be processed, should the server be upgraded to (A) a new machine with more cores, (B) a new machine with two processors, 4 cores each, or (C) keep the original server and add another server with a single processor? Which route provides the most efficient way to scale the application, in terms of processing more client requests per time interval? Is the choice, for example, limited by RAM (when you need more RAM than box can handle it's time to add another server), or something else? Question 3: Is the total number of client requests processed simultaneously equal to the number of cores times the number of servers (minus the one core for Apache)?

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  • MongoDB and GrifFS. What are the best storage options in the range of 1 TB?

    - by Nerian
    We are going to launch a service that will require between 1 and 2 GB for file storage per paid user. I am going to use GridFS for storing files. I am pondering the different options for storing the database. But since I am unexperienced at deployment and it is my first time with Mongodb I need your experience. Criteria: I want to spend my time developing my core business, that is, my own application. I am a Ruby on Rails developer. I do not like to mess with server configuration. Hence, I would like a fully managed hosting solution. But I would like to know about any other option, if you think it is worth it. It should be able to scale. Cloud style. Pay as you go. The lower the price, the better. So far I known of these services: https://mongohq.com/pricing https://mongomachine.com/pricing https://mongolab.com/about/pricing/ http://cloudcontrol.com/add-ons/mongodb/ And they seem to be OK for common needs, that is no file storage. But I am going to use GridFS, so the size matters. These services seems to scale, in price, quite poorly. MongoHQ: The larger plan max storage is 20 GB. Seems like a very little storage, for GridFS. MongoMachine: Flat price, 2.5$ per GB. I didn't found the limit. Seems like a good price, comparing the others. MongoLab: 3.984 GB max, which I don't think I will hit, so perfect. 8$ per GB, quite costly. CloudControl: The larger plan is 20 Gb. The custom service starts at 250€ plus some unspecified charge per GB. What is your experience with these services? Any downtimes? Other possibilities?

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  • How do you update without cutting off users?

    - by Griffin
    I searched around and I was surprised that I couldn't find an answer to this question. My assumption is that you have multiple servers. Normally they both will be doing their specific take (for the rest of this I will assume a simple website). Now lets say server A & B need updates. Do you update server A while server B keeps pushing out the webpage and then when server A is okay you update server B? This seems like it would work in small scale but seems horrible in large scale due to the fact that you'd need twice the power that you normally have. When dealing with a large number of servers do you update small sections at a time? I thought the problem with this would be if server A can't work alongside server B C D E or F any-longer that's not that bad. But when you start updating you slowly lose this small percentage. What is the proper way to deal with updates like this?

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  • Handling TclErrors in Python

    - by anteater7171
    In the following code I'll get the following error if I right click the window that pops up. Then go down to the very bottom entry widget then delete it's contents. It seems to be giving me a TclError. How do I go about handeling such an error? The Error Exception in Tkinter callback Traceback (most recent call last): File "C:\Python26\Lib\lib-tk\Tkinter.py", line 1410, in __call__ return self.func(*args) File "C:\Python26\CPUDEMO.py", line 503, in I TL.sclS.set(S1) File "C:\Python26\Lib\lib-tk\Tkinter.py", line 2765, in set self.tk.call(self._w, 'set', value) TclError: expected floating-point number but got "" The Code #F #PIthon.py # Import/Setup import Tkinter import psutil,time import re from PIL import Image, ImageTk from time import sleep class simpleapp_tk(Tkinter.Tk): def __init__(self,parent): Tkinter.Tk.__init__(self,parent) self.parent = parent self.initialize() def initialize(self): Widgets self.menu = Tkinter.Menu(self, tearoff = 0 ) M = [ "Options...", "Exit"] self.selectedM = Tkinter.StringVar() self.menu.add_radiobutton( label = 'Hide', variable = self.selectedM, command = self.E ) self.menu.add_radiobutton( label = 'Bump', variable = self.selectedM, command = self.E ) self.menu.add_separator() self.menu.add_radiobutton( label = 'Options...', variable = self.selectedM, command = self.E ) self.menu.add_separator() self.menu.add_radiobutton( label = 'Exit', variable = self.selectedM, command = self.E ) self.frame1 = Tkinter.Frame(self,bg='grey15',relief='ridge',borderwidth=4,width=185, height=39) self.frame1.grid() self.frame1.grid_propagate(0) self.frame1.bind( "<Button-3><ButtonRelease-3>", self.D ) self.frame1.bind( "<Button-2><ButtonRelease-2>", self.C ) self.frame1.bind( "<Double-Button-1>", self.C ) self.labelVariable = Tkinter.StringVar() self.label = Tkinter.Label(self.frame1,textvariable=self.labelVariable,fg="lightgreen",bg="grey15",borderwidth=1,font=('arial', 10, 'bold')) self.label.grid(column=1,row=0,columnspan=1,sticky='nsew') self.label.bind( "<Button-3><ButtonRelease-3>", self.D ) self.label.bind( "<Button-2><ButtonRelease-2>", self.C ) self.label.bind( "<Double-Button-1>", self.C ) self.F() self.overrideredirect(1) self.wm_attributes("-topmost", 1) global TL1 TL1 = Tkinter.Toplevel(self) TL1.wm_geometry("+0+5000") TL1.overrideredirect(1) TL1.button = Tkinter.Button(TL1,text="? CPU",fg="lightgreen",bg="grey15",activeforeground="lightgreen", activebackground='grey15',borderwidth=4,font=('Arial', 8, 'bold'),command=self.J) TL1.button.pack(ipadx=1) Events def Reset(self): self.label.configure(font=('arial', 10, 'bold'),fg='Lightgreen',bg='grey15',borderwidth=0) self.labela.configure(font=('arial', 8, 'bold'),fg='Lightgreen',bg='grey15',borderwidth=0) self.frame1.configure(bg='grey15',relief='ridge',borderwidth=4,width=224, height=50) self.label.pack(ipadx=38) def helpmenu(self): t2 = Tkinter.Toplevel(self) Tkinter.Label(t2, text='This is a help menu', anchor="w",justify="left",fg="darkgreen",bg="grey90",relief="ridge",borderwidth=5,font=('Arial', 10)).pack(fill='both', expand=1) t2.resizable(False,False) t2.title('Help') menu = Tkinter.Menu(self) t2.config(menu=menu) filemenu = Tkinter.Menu(menu) menu.add_cascade(label="| Exit |", menu=filemenu) filemenu.add_command(label="Exit", command=t2.destroy) def aboutmenu(self): t1 = Tkinter.Toplevel(self) Tkinter.Label(t1, text=' About:\n\n CPU Usage v1.0\n\n Publisher: Drew French\n Date: 05/09/10\n Email: [email protected] \n\n\n\n\n\n\n Written in Python 2.6.4', anchor="w",justify="left",fg="darkgreen",bg="grey90",relief="sunken",borderwidth=5,font=('Arial', 10)).pack(fill='both', expand=1) t1.resizable(False,False) t1.title('About') menu = Tkinter.Menu(self) t1.config(menu=menu) filemenu = Tkinter.Menu(menu) menu.add_cascade(label="| Exit |", menu=filemenu) filemenu.add_command(label="Exit", command=t1.destroy) def A (self,event): TL.entryVariable1.set(TL.sclY.get()) TL.entryVariable2.set(TL.sclX.get()) Y = TL.sclY.get() X = TL.sclX.get() self.wm_geometry("+" + str(X) + "+" + str(Y)) def B(self,event): Y1 = TL.entryVariable1.get() X1 = TL.entryVariable2.get() self.wm_geometry("+" + str(X1) + "+" + str(Y1)) TL.sclY.set(Y1) TL.sclX.set(X1) def C(self,event): s = self.wm_geometry() geomPatt = re.compile(r"(\d+)?x?(\d+)?([+-])(\d+)([+-])(\d+)") m = geomPatt.search(s) X3 = m.group(4) Y3 = m.group(6) M = int(Y3) - 150 P = M + 150 while Y3 > M: sleep(0.0009) Y3 = int(Y3) - 1 self.update_idletasks() self.wm_geometry("+" + str(X3) + "+" + str(Y3)) sleep(2.00) while Y3 < P: sleep(0.0009) Y3 = int(Y3) + 1 self.update_idletasks() self.wm_geometry("+" + str(X3) + "+" + str(Y3)) def D(self, event=None): self.menu.post( event.x_root, event.y_root ) def E(self): if self.selectedM.get() =='Options...': Setup global TL TL = Tkinter.Toplevel(self) menu = Tkinter.Menu(TL) TL.config(menu=menu) filemenu = Tkinter.Menu(menu) menu.add_cascade(label="| Menu |", menu=filemenu) filemenu.add_command(label="Instruction Manual...", command=self.helpmenu) filemenu.add_command(label="About...", command=self.aboutmenu) filemenu.add_separator() filemenu.add_command(label="Exit Options", command=TL.destroy) filemenu.add_command(label="Exit", command=self.destroy) helpmenu = Tkinter.Menu(menu) menu.add_cascade(label="| Help |", menu=helpmenu) helpmenu.add_command(label="Instruction Manual...", command=self.helpmenu) helpmenu.add_separator() helpmenu.add_command(label="Quick Help...", command=self.helpmenu) Title TL.label5 = Tkinter.Label(TL,text="CPU Usage: Options",anchor="center",fg="black",bg="lightgreen",relief="ridge",borderwidth=5,font=('Arial', 18, 'bold')) TL.label5.pack(padx=15,ipadx=5) X Y scale TL.separator = Tkinter.Frame(TL,height=7, bd=1, relief='ridge', bg='grey95') TL.separator.pack(pady=5,padx=5) # TL.sclX = Tkinter.Scale(TL.separator, from_=0, to=1500, orient='horizontal', resolution=1, command=self.A) TL.sclX.grid(column=1,row=0,ipadx=27, sticky='w') TL.label1 = Tkinter.Label(TL.separator,text="X",anchor="s",fg="black",bg="grey95",font=('Arial', 8 ,'bold')) TL.label1.grid(column=0,row=0, pady=1, sticky='S') TL.sclY = Tkinter.Scale(TL.separator, from_=0, to=1500, resolution=1, command=self.A) TL.sclY.grid(column=2,row=1,rowspan=2,sticky='e', padx=4) TL.label3 = Tkinter.Label(TL.separator,text="Y",fg="black",bg="grey95",font=('Arial', 8 ,'bold')) TL.label3.grid(column=2,row=0, padx=10, sticky='e') TL.entryVariable2 = Tkinter.StringVar() TL.entry2 = Tkinter.Entry(TL.separator,textvariable=TL.entryVariable2, fg="grey15",bg="grey90",relief="sunken",insertbackground="black",borderwidth=5,font=('Arial', 10)) TL.entry2.grid(column=1,row=1,ipadx=20, pady=10,sticky='EW') TL.entry2.bind("<Return>", self.B) TL.label2 = Tkinter.Label(TL.separator,text="X:",fg="black",bg="grey95",font=('Arial', 8 ,'bold')) TL.label2.grid(column=0,row=1, ipadx=4, sticky='W') TL.entryVariable1 = Tkinter.StringVar() TL.entry1 = Tkinter.Entry(TL.separator,textvariable=TL.entryVariable1, fg="grey15",bg="grey90",relief="sunken",insertbackground="black",borderwidth=5,font=('Arial', 10)) TL.entry1.grid(column=1,row=2,sticky='EW') TL.entry1.bind("<Return>", self.B) TL.label4 = Tkinter.Label(TL.separator,text="Y:", anchor="center",fg="black",bg="grey95",font=('Arial', 8 ,'bold')) TL.label4.grid(column=0,row=2, ipadx=4, sticky='W') TL.label7 = Tkinter.Label(TL.separator,text="Text Colour:",fg="black",bg="grey95",font=('Arial', 8 ,'bold')) TL.label7.grid(column=1,row=3,stick="W",ipady=10) TL.selectedP = Tkinter.StringVar() TL.opt1 = Tkinter.OptionMenu(TL.separator, TL.selectedP,'Normal', 'White','Black', 'Blue', 'Steel Blue','Green','Light Green','Yellow','Orange' ,'Red',command=self.G) TL.opt1.config(fg="black",bg="grey90",activebackground="grey90",activeforeground="black", anchor="center",relief="raised",direction='right',font=('Arial', 10)) TL.opt1.grid(column=1,row=4,sticky='EW',padx=20,ipadx=20) TL.selectedP.set('Normal') TL.label7 = Tkinter.Label(TL.separator,text="Refresh Rate:",fg="black",bg="grey95",font=('Arial', 8 ,'bold')) TL.label7.grid(column=1,row=5,stick="W",ipady=10) TL.sclS = Tkinter.Scale(TL.separator, from_=10, to=2000, orient='horizontal', resolution=10, command=self.H) TL.sclS.grid(column=1,row=6,ipadx=27, sticky='w') TL.sclS.set(650) TL.entryVariableS = Tkinter.StringVar() TL.entryS = Tkinter.Entry(TL.separator,textvariable=TL.entryVariableS, fg="grey15",bg="grey90",relief="sunken",insertbackground="black",borderwidth=5,font=('Arial', 10)) TL.entryS.grid(column=1,row=7,ipadx=20, pady=10,sticky='EW') TL.entryS.bind("<Return>", self.I) TL.entryVariableS.set(650) # TL.resizable(False,False) TL.title('Options') geomPatt = re.compile(r"(\d+)?x?(\d+)?([+-])(\d+)([+-])(\d+)") s = self.wm_geometry() m = geomPatt.search(s) X = m.group(4) Y = m.group(6) TL.sclY.set(Y) TL.sclX.set(X) if self.selectedM.get() == 'Exit': self.destroy() if self.selectedM.get() == 'Bump': s = self.wm_geometry() geomPatt = re.compile(r"(\d+)?x?(\d+)?([+-])(\d+)([+-])(\d+)") m = geomPatt.search(s) X3 = m.group(4) Y3 = m.group(6) M = int(Y3) - 150 P = M + 150 while Y3 > M: sleep(0.0009) Y3 = int(Y3) - 1 self.update_idletasks() self.wm_geometry("+" + str(X3) + "+" + str(Y3)) sleep(2.00) while Y3 < P: sleep(0.0009) Y3 = int(Y3) + 1 self.update_idletasks() self.wm_geometry("+" + str(X3) + "+" + str(Y3)) if self.selectedM.get() == 'Hide': s = self.wm_geometry() geomPatt = re.compile(r"(\d+)?x?(\d+)?([+-])(\d+)([+-])(\d+)") m = geomPatt.search(s) X3 = m.group(4) Y3 = m.group(6) M = int(Y3) + 5000 self.update_idletasks() self.wm_geometry("+" + str(X3) + "+" + str(M)) TL1.wm_geometry("+0+190") def F (self): G = round(psutil.cpu_percent(), 1) G1 = str(G) + '%' self.labelVariable.set(G1) try: S2 = TL.entryVariableS.get() except ValueError, e: S2 = 650 except NameError: S2 = 650 self.after(int(S2), self.F) def G (self,event): if TL.selectedP.get() =='Normal': self.label.config( fg = 'lightgreen' ) TL1.button.config( fg = 'lightgreen',activeforeground='lightgreen') if TL.selectedP.get() =='Red': self.label.config( fg = 'red' ) TL1.button.config( fg = 'red',activeforeground='red') if TL.selectedP.get() =='Orange': self.label.config( fg = 'orange') TL1.button.config( fg = 'orange',activeforeground='orange') if TL.selectedP.get() =='Yellow': self.label.config( fg = 'yellow') TL1.button.config( fg = 'yellow',activeforeground='yellow') if TL.selectedP.get() =='Light Green': self.label.config( fg = 'lightgreen' ) TL1.button.config( fg = 'lightgreen',activeforeground='lightgreen') if TL.selectedP.get() =='Normal': self.label.config( fg = 'lightgreen' ) TL1.button.config( fg = 'lightgreen',activeforeground='lightgreen') if TL.selectedP.get() =='Steel Blue': self.label.config( fg = 'steelblue1' ) TL1.button.config( fg = 'steelblue1',activeforeground='steelblue1') if TL.selectedP.get() =='Blue': self.label.config( fg = 'blue') TL1.button.config( fg = 'blue',activeforeground='blue') if TL.selectedP.get() =='Green': self.label.config( fg = 'darkgreen' ) TL1.button.config( fg = 'darkgreen',activeforeground='darkgreen') if TL.selectedP.get() =='White': self.label.config( fg = 'white' ) TL1.button.config( fg = 'white',activeforeground='white') if TL.selectedP.get() =='Black': self.label.config( fg = 'black') TL1.button.config( fg = 'black',activeforeground='black') def H (self,event): TL.entryVariableS.set(TL.sclS.get()) S = TL.sclS.get() def I (self,event): S1 = TL.entryVariableS.get() TL.sclS.set(S1) TL.sclS.set(TL.sclS.get()) S1 = TL.entryVariableS.get() TL.sclS.set(S1) def J (self): s = self.wm_geometry() geomPatt = re.compile(r"(\d+)?x?(\d+)?([+-])(\d+)([+-])(\d+)") m = geomPatt.search(s) X3 = m.group(4) Y3 = m.group(6) M = int(Y3) - 5000 self.update_idletasks() self.wm_geometry("+" + str(X3) + "+" + str(M)) TL1.wm_geometry("+0+5000") Loop if name == "main": app = simpleapp_tk(None) app.mainloop()

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  • Atmospheric scattering OpenGL 3.3

    - by user1419305
    Im currently trying to convert a shader by Sean O'Neil to version 330 so i can try it out in a application im writing. Im having some issues with deprecated functions, so i replaced them, but im almost completely new to glsl, so i probably did a mistake somewhere. Original shaders can be found here: http://www.gamedev.net/topic/592043-solved-trying-to-use-atmospheric-scattering-oneill-2004-but-get-black-sphere/ My horrible attempt at converting them: Vertex Shader: #version 330 core layout(location = 0) in vec3 vertexPosition_modelspace; //layout(location = 1) in vec2 vertexUV; layout(location = 2) in vec3 vertexNormal_modelspace; uniform vec3 v3CameraPos; uniform vec3 v3LightPos; uniform vec3 v3InvWavelength; uniform float fCameraHeight; uniform float fCameraHeight2; uniform float fOuterRadius; uniform float fOuterRadius2; uniform float fInnerRadius; uniform float fInnerRadius2; uniform float fKrESun; uniform float fKmESun; uniform float fKr4PI; uniform float fKm4PI; uniform float fScale; uniform float fScaleDepth; uniform float fScaleOverScaleDepth; // passing in matrixes for transformations uniform mat4 MVP; uniform mat4 V; uniform mat4 M; const int nSamples = 4; const float fSamples = 4.0; out vec3 v3Direction; out vec4 gg_FrontColor; out vec4 gg_FrontSecondaryColor; float scale(float fCos) { float x = 1.0 - fCos; return fScaleDepth * exp(-0.00287 + x*(0.459 + x*(3.83 + x*(-6.80 + x*5.25)))); } void main(void) { vec3 v3Pos = vertexPosition_modelspace; vec3 v3Ray = v3Pos - v3CameraPos; float fFar = length(v3Ray); v3Ray /= fFar; vec3 v3Start = v3CameraPos; float fHeight = length(v3Start); float fDepth = exp(fScaleOverScaleDepth * (fInnerRadius - fCameraHeight)); float fStartAngle = dot(v3Ray, v3Start) / fHeight; float fStartOffset = fDepth*scale(fStartAngle); float fSampleLength = fFar / fSamples; float fScaledLength = fSampleLength * fScale; vec3 v3SampleRay = v3Ray * fSampleLength; vec3 v3SamplePoint = v3Start + v3SampleRay * 0.5; vec3 v3FrontColor = vec3(0.0, 0.0, 0.0); for(int i=0; i<nSamples; i++) { float fHeight = length(v3SamplePoint); float fDepth = exp(fScaleOverScaleDepth * (fInnerRadius - fHeight)); float fLightAngle = dot(v3LightPos, v3SamplePoint) / fHeight; float fCameraAngle = dot(v3Ray, v3SamplePoint) / fHeight; float fScatter = (fStartOffset + fDepth*(scale(fLightAngle) - scale(fCameraAngle))); vec3 v3Attenuate = exp(-fScatter * (v3InvWavelength * fKr4PI + fKm4PI)); v3FrontColor += v3Attenuate * (fDepth * fScaledLength); v3SamplePoint += v3SampleRay; } gg_FrontSecondaryColor.rgb = v3FrontColor * fKmESun; gg_FrontColor.rgb = v3FrontColor * (v3InvWavelength * fKrESun); gl_Position = MVP * vec4(vertexPosition_modelspace,1); v3Direction = v3CameraPos - v3Pos; } Fragment Shader: #version 330 core uniform vec3 v3LightPos; uniform float g; uniform float g2; in vec3 v3Direction; out vec4 FragColor; in vec4 gg_FrontColor; in vec4 gg_FrontSecondaryColor; void main (void) { float fCos = dot(v3LightPos, v3Direction) / length(v3Direction); float fMiePhase = 1.5 * ((1.0 - g2) / (2.0 + g2)) * (1.0 + fCos*fCos) / pow(1.0 + g2 - 2.0*g*fCos, 1.5); FragColor = gg_FrontColor + fMiePhase * gg_FrontSecondaryColor; FragColor.a = FragColor.b; } I wrote a function to render a sphere, and im trying to render this shader onto a inverted version of it, the sphere works completely fine, with normals and all. My problem is that the sphere gets rendered all black, so the shader is not working. This is how i'm trying to render the atmosphere inside my main rendering loop. glUseProgram(programAtmosphere); glBindTexture(GL_TEXTURE_2D, 0); //###################### glUniform3f(v3CameraPos, getPlayerPos().x, getPlayerPos().y, getPlayerPos().z); glUniform3f(v3LightPos, lightPos.x / sqrt(lightPos.x * lightPos.x + lightPos.y * lightPos.y), lightPos.y / sqrt(lightPos.x * lightPos.x + lightPos.y * lightPos.y), 0); glUniform3f(v3InvWavelength, 1.0 / pow(0.650, 4.0), 1.0 / pow(0.570, 4.0), 1.0 / pow(0.475, 4.0)); glUniform1fARB(fCameraHeight, 1); glUniform1fARB(fCameraHeight2, 1); glUniform1fARB(fInnerRadius, 6350); glUniform1fARB(fInnerRadius2, 6350 * 6350); glUniform1fARB(fOuterRadius, 6450); glUniform1fARB(fOuterRadius2, 6450 * 6450); glUniform1fARB(fKrESun, 0.0025 * 20.0); glUniform1fARB(fKmESun, 0.0015 * 20.0); glUniform1fARB(fKr4PI, 0.0025 * 4.0 * 3.141592653); glUniform1fARB(fKm4PI, 0.0015 * 4.0 * 3.141592653); glUniform1fARB(fScale, 1.0 / (6450 - 6350)); glUniform1fARB(fScaleDepth, 0.25); glUniform1fARB(fScaleOverScaleDepth, 4.0 / (6450 - 6350)); glUniform1fARB(g, -0.85); glUniform1f(g2, -0.85 * -0.85); // vertices glEnableVertexAttribArray(0); glBindBuffer(GL_ARRAY_BUFFER, vertexbuffer[1]); glVertexAttribPointer( 0, // attribute 3, // size GL_FLOAT, // type GL_FALSE, // normalized? 0, // stride (void*)0 // array buffer offset ); // normals glEnableVertexAttribArray(2); glBindBuffer(GL_ARRAY_BUFFER, normalbuffer[1]); glVertexAttribPointer( 2, // attribute 3, // size GL_FLOAT, // type GL_FALSE, // normalized? 0, // stride (void*)0 // array buffer offset ); glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, elementbuffer[1]); glUniformMatrix4fv(ModelMatrixAT, 1, GL_FALSE, &ModelMatrix[0][0]); glUniformMatrix4fv(ViewMatrixAT, 1, GL_FALSE, &ViewMatrix[0][0]); glUniformMatrix4fv(ModelViewPAT, 1, GL_FALSE, &MVP[0][0]); // Draw the triangles glDrawElements( GL_TRIANGLES, // mode cubeIndices[1], // count GL_UNSIGNED_SHORT, // type (void*)0 // element array buffer offset ); Any ideas?

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  • Array structure returned by Yii's model

    - by user1104955
    I am a Yii beginner and am running into a bit of a wall and hope someone will be able to help me get back onto track. I think this might be a fairly straight forward question to the seasoned Yii user. So here goes... In the controller, let's say I run the following call to the model- $variable = Post::model()->findAll(); All works fine and I pass the variable into the view. Here's where I get pretty stuck. The array that is returned in the above query is far more complex than I anticipated and I'm struggling to make sense of it. Here's a sample- print_r($variable); gives- Array ( [0] => Post Object ( [_md:CActiveRecord:private] => CActiveRecordMetaData Object ( [tableSchema] => CMysqlTableSchema Object ( [schemaName] => [name] => tbl_post [rawName] => `tbl_post` [primaryKey] => id [sequenceName] => [foreignKeys] => Array ( ) [columns] => Array ( [id] => CMysqlColumnSchema Object ( [name] => id [rawName] => `id` [allowNull] => [dbType] => int(11) [type] => integer [defaultValue] => [size] => 11 [precision] => 11 [scale] => [isPrimaryKey] => 1 [isForeignKey] => [autoIncrement] => 1 [_e:CComponent:private] => [_m:CComponent:private] => ) [post] => CMysqlColumnSchema Object ( [name] => post [rawName] => `post` [allowNull] => [dbType] => text [type] => string [defaultValue] => [size] => [precision] => [scale] => [isPrimaryKey] => [isForeignKey] => [autoIncrement] => [_e:CComponent:private] => [_m:CComponent:private] => ) ) [_e:CComponent:private] => [_m:CComponent:private] => ) [columns] => Array ( [id] => CMysqlColumnSchema Object ( [name] => id [rawName] => `id` [allowNull] => [dbType] => int(11) [type] => integer [defaultValue] => [size] => 11 [precision] => 11 [scale] => [isPrimaryKey] => 1 [isForeignKey] => [autoIncrement] => 1 [_e:CComponent:private] => [_m:CComponent:private] => ) [post] => CMysqlColumnSchema Object ( [name] => post [rawName] => `post` [allowNull] => [dbType] => text [type] => string [defaultValue] => [size] => [precision] => [scale] => [isPrimaryKey] => [isForeignKey] => [autoIncrement] => [_e:CComponent:private] => [_m:CComponent:private] => ) ) [relations] => Array ( [responses] => CHasManyRelation Object ( [limit] => -1 [offset] => -1 [index] => [through] => [joinType] => LEFT OUTER JOIN [on] => [alias] => [with] => Array ( ) [together] => [scopes] => [name] => responses [className] => Response [foreignKey] => post_id [select] => * [condition] => [params] => Array ( ) [group] => [join] => [having] => [order] => [_e:CComponent:private] => [_m:CComponent:private] => ) ) [attributeDefaults] => Array ( ) [_model:CActiveRecordMetaData:private] => Post Object ( [_md:CActiveRecord:private] => CActiveRecordMetaData Object *RECURSION* [_new:CActiveRecord:private] => [_attributes:CActiveRecord:private] => Array ( ) [_related:CActiveRecord:private] => Array ( ) [_c:CActiveRecord:private] => [_pk:CActiveRecord:private] => [_alias:CActiveRecord:private] => t [_errors:CModel:private] => Array ( ) [_validators:CModel:private] => [_scenario:CModel:private] => [_e:CComponent:private] => [_m:CComponent:private] => ) ) [_new:CActiveRecord:private] => [_attributes:CActiveRecord:private] => Array ( [id] => 1 [post] => User Post ) [_related:CActiveRecord:private] => Array ( ) [_c:CActiveRecord:private] => [_pk:CActiveRecord:private] => 1 [_alias:CActiveRecord:private] => t [_errors:CModel:private] => Array ( ) [_validators:CModel:private] => [_scenario:CModel:private] => update [_e:CComponent:private] => [_m:CComponent:private] => ) ) [sorry if there's an easier way to show this array, I'm not aware of it] Can anyone explain to me why the model returns such a complex array? It doesn't seem to matter what tables or columns or relations are used in your application, they all seem to me to return this format. Also, can someone explain the structure to me so that I can isolate the variables that I want to recover? Many thanks in advance, Nick

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  • Movement and Collision with AABB

    - by Jeremy Giberson
    I'm having a little difficulty figuring out the following scenarios. http://i.stack.imgur.com/8lM6i.png In scenario A, the moving entity has fallen to (and slightly into the floor). The current position represents the projected position that will occur if I apply the acceleration & velocity as usual without worrying about collision. The Next position, represents the corrected projection position after collision check. The resulting end position is the falling entity now rests ON the floor--that is, in a consistent state of collision by sharing it's bottom X axis with the floor's top X axis. My current update loop looks like the following: // figure out forces & accelerations and project an objects next position // check collision occurrence from current position -> projected position // if a collision occurs, adjust projection position Which seems to be working for the scenario of my object falling to the floor. However, the situation becomes sticky when trying to figure out scenario's B & C. In scenario B, I'm attempt to move along the floor on the X axis (player is pressing right direction button) additionally, gravity is pulling the object into the floor. The problem is, when the object attempts to move the collision detection code is going to recognize that the object is already colliding with the floor to begin with, and auto correct any movement back to where it was before. In scenario C, I'm attempting to jump off the floor. Again, because the object is already in a constant collision with the floor, when the collision routine checks to make sure moving from current position to projected position doesn't result in a collision, it will fail because at the beginning of the motion, the object is already colliding. How do you allow movement along the edge of an object? How do you allow movement away from an object you are already colliding with. Extra Info My collision routine is based on AABB sweeping test from an old gamasutra article, http://www.gamasutra.com/view/feature/3383/simple_intersection_tests_for_games.php?page=3 My bounding box implementation is based on top left/bottom right instead of midpoint/extents, so my min/max functions are adjusted. Otherwise, here is my bounding box class with collision routines: public class BoundingBox { public XYZ topLeft; public XYZ bottomRight; public BoundingBox(float x, float y, float z, float w, float h, float d) { topLeft = new XYZ(); bottomRight = new XYZ(); topLeft.x = x; topLeft.y = y; topLeft.z = z; bottomRight.x = x+w; bottomRight.y = y+h; bottomRight.z = z+d; } public BoundingBox(XYZ position, XYZ dimensions, boolean centered) { topLeft = new XYZ(); bottomRight = new XYZ(); topLeft.x = position.x; topLeft.y = position.y; topLeft.z = position.z; bottomRight.x = position.x + (centered ? dimensions.x/2 : dimensions.x); bottomRight.y = position.y + (centered ? dimensions.y/2 : dimensions.y); bottomRight.z = position.z + (centered ? dimensions.z/2 : dimensions.z); } /** * Check if a point lies inside a bounding box * @param box * @param point * @return */ public static boolean isPointInside(BoundingBox box, XYZ point) { if(box.topLeft.x <= point.x && point.x <= box.bottomRight.x && box.topLeft.y <= point.y && point.y <= box.bottomRight.y && box.topLeft.z <= point.z && point.z <= box.bottomRight.z) return true; return false; } /** * Check for overlap between two bounding boxes using separating axis theorem * if two boxes are separated on any axis, they cannot be overlapping * @param a * @param b * @return */ public static boolean isOverlapping(BoundingBox a, BoundingBox b) { XYZ dxyz = new XYZ(b.topLeft.x - a.topLeft.x, b.topLeft.y - a.topLeft.y, b.topLeft.z - a.topLeft.z); // if b - a is positive, a is first on the axis and we should use its extent // if b -a is negative, b is first on the axis and we should use its extent // check for x axis separation if ((dxyz.x >= 0 && a.bottomRight.x-a.topLeft.x < dxyz.x) // negative scale, reverse extent sum, flip equality ||(dxyz.x < 0 && b.topLeft.x-b.bottomRight.x > dxyz.x)) return false; // check for y axis separation if ((dxyz.y >= 0 && a.bottomRight.y-a.topLeft.y < dxyz.y) // negative scale, reverse extent sum, flip equality ||(dxyz.y < 0 && b.topLeft.y-b.bottomRight.y > dxyz.y)) return false; // check for z axis separation if ((dxyz.z >= 0 && a.bottomRight.z-a.topLeft.z < dxyz.z) // negative scale, reverse extent sum, flip equality ||(dxyz.z < 0 && b.topLeft.z-b.bottomRight.z > dxyz.z)) return false; // not separated on any axis, overlapping return true; } public static boolean isContactEdge(int xyzAxis, BoundingBox a, BoundingBox b) { switch(xyzAxis) { case XYZ.XCOORD: if(a.topLeft.x == b.bottomRight.x || a.bottomRight.x == b.topLeft.x) return true; return false; case XYZ.YCOORD: if(a.topLeft.y == b.bottomRight.y || a.bottomRight.y == b.topLeft.y) return true; return false; case XYZ.ZCOORD: if(a.topLeft.z == b.bottomRight.z || a.bottomRight.z == b.topLeft.z) return true; return false; } return false; } /** * Sweep test min extent value * @param box * @param xyzCoord * @return */ public static float min(BoundingBox box, int xyzCoord) { switch(xyzCoord) { case XYZ.XCOORD: return box.topLeft.x; case XYZ.YCOORD: return box.topLeft.y; case XYZ.ZCOORD: return box.topLeft.z; default: return 0f; } } /** * Sweep test max extent value * @param box * @param xyzCoord * @return */ public static float max(BoundingBox box, int xyzCoord) { switch(xyzCoord) { case XYZ.XCOORD: return box.bottomRight.x; case XYZ.YCOORD: return box.bottomRight.y; case XYZ.ZCOORD: return box.bottomRight.z; default: return 0f; } } /** * Test if bounding box A will overlap bounding box B at any point * when box A moves from position 0 to position 1 and box B moves from position 0 to position 1 * Note, sweep test assumes bounding boxes A and B's dimensions do not change * * @param a0 box a starting position * @param a1 box a ending position * @param b0 box b starting position * @param b1 box b ending position * @param aCollisionOut xyz of box a's position when/if a collision occurs * @param bCollisionOut xyz of box b's position when/if a collision occurs * @return */ public static boolean sweepTest(BoundingBox a0, BoundingBox a1, BoundingBox b0, BoundingBox b1, XYZ aCollisionOut, XYZ bCollisionOut) { // solve in reference to A XYZ va = new XYZ(a1.topLeft.x-a0.topLeft.x, a1.topLeft.y-a0.topLeft.y, a1.topLeft.z-a0.topLeft.z); XYZ vb = new XYZ(b1.topLeft.x-b0.topLeft.x, b1.topLeft.y-b0.topLeft.y, b1.topLeft.z-b0.topLeft.z); XYZ v = new XYZ(vb.x-va.x, vb.y-va.y, vb.z-va.z); // check for initial overlap if(BoundingBox.isOverlapping(a0, b0)) { // java pass by ref/value gotcha, have to modify value can't reassign it aCollisionOut.x = a0.topLeft.x; aCollisionOut.y = a0.topLeft.y; aCollisionOut.z = a0.topLeft.z; bCollisionOut.x = b0.topLeft.x; bCollisionOut.y = b0.topLeft.y; bCollisionOut.z = b0.topLeft.z; return true; } // overlap min/maxs XYZ u0 = new XYZ(); XYZ u1 = new XYZ(1,1,1); float t0, t1; // iterate axis and find overlaps times (x=0, y=1, z=2) for(int i = 0; i < 3; i++) { float aMax = max(a0, i); float aMin = min(a0, i); float bMax = max(b0, i); float bMin = min(b0, i); float vi = XYZ.getCoord(v, i); if(aMax < bMax && vi < 0) XYZ.setCoord(u0, i, (aMax-bMin)/vi); else if(bMax < aMin && vi > 0) XYZ.setCoord(u0, i, (aMin-bMax)/vi); if(bMax > aMin && vi < 0) XYZ.setCoord(u1, i, (aMin-bMax)/vi); else if(aMax > bMin && vi > 0) XYZ.setCoord(u1, i, (aMax-bMin)/vi); } // get times of collision t0 = Math.max(u0.x, Math.max(u0.y, u0.z)); t1 = Math.min(u1.x, Math.min(u1.y, u1.z)); // collision only occurs if t0 < t1 if(t0 <= t1 && t0 != 0) // not t0 because we already tested it! { // t0 is the normalized time of the collision // then the position of the bounding boxes would // be their original position + velocity*time aCollisionOut.x = a0.topLeft.x + va.x*t0; aCollisionOut.y = a0.topLeft.y + va.y*t0; aCollisionOut.z = a0.topLeft.z + va.z*t0; bCollisionOut.x = b0.topLeft.x + vb.x*t0; bCollisionOut.y = b0.topLeft.y + vb.y*t0; bCollisionOut.z = b0.topLeft.z + vb.z*t0; return true; } else return false; } }

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  • Solving Big Problems with Oracle R Enterprise, Part II

    - by dbayard
    Part II – Solving Big Problems with Oracle R Enterprise In the first post in this series (see https://blogs.oracle.com/R/entry/solving_big_problems_with_oracle), we showed how you can use R to perform historical rate of return calculations against investment data sourced from a spreadsheet.  We demonstrated the calculations against sample data for a small set of accounts.  While this worked fine, in the real-world the problem is much bigger because the amount of data is much bigger.  So much bigger that our approach in the previous post won’t scale to meet the real-world needs. From our previous post, here are the challenges we need to conquer: The actual data that needs to be used lives in a database, not in a spreadsheet The actual data is much, much bigger- too big to fit into the normal R memory space and too big to want to move across the network The overall process needs to run fast- much faster than a single processor The actual data needs to be kept secured- another reason to not want to move it from the database and across the network And the process of calculating the IRR needs to be integrated together with other database ETL activities, so that IRR’s can be calculated as part of the data warehouse refresh processes In this post, we will show how we moved from sample data environment to working with full-scale data.  This post is based on actual work we did for a financial services customer during a recent proof-of-concept. Getting started with the Database At this point, we have some sample data and our IRR function.  We were at a similar point in our customer proof-of-concept exercise- we had sample data but we did not have the full customer data yet.  So our database was empty.  But, this was easily rectified by leveraging the transparency features of Oracle R Enterprise (see https://blogs.oracle.com/R/entry/analyzing_big_data_using_the).  The following code shows how we took our sample data SimpleMWRRData and easily turned it into a new Oracle database table called IRR_DATA via ore.create().  The code also shows how we can access the database table IRR_DATA as if it was a normal R data.frame named IRR_DATA. If we go to sql*plus, we can also check out our new IRR_DATA table: At this point, we now have our sample data loaded in the database as a normal Oracle table called IRR_DATA.  So, we now proceeded to test our R function working with database data. As our first test, we retrieved the data from a single account from the IRR_DATA table, pull it into local R memory, then call our IRR function.  This worked.  No SQL coding required! Going from Crawling to Walking Now that we have shown using our R code with database-resident data for a single account, we wanted to experiment with doing this for multiple accounts.  In other words, we wanted to implement the split-apply-combine technique we discussed in our first post in this series.  Fortunately, Oracle R Enterprise provides a very scalable way to do this with a function called ore.groupApply().  You can read more about ore.groupApply() here: https://blogs.oracle.com/R/entry/analyzing_big_data_using_the1 Here is an example of how we ask ORE to take our IRR_DATA table in the database, split it by the ACCOUNT column, apply a function that calls our SimpleMWRR() calculation, and then combine the results. (If you are following along at home, be sure to have installed our myIRR package on your database server via  “R CMD INSTALL myIRR”). The interesting thing about ore.groupApply is that the calculation is not actually performed in my desktop R environment from which I am running.  What actually happens is that ore.groupApply uses the Oracle database to perform the work.  And the Oracle database is what actually splits the IRR_DATA table by ACCOUNT.  Then the Oracle database takes the data for each account and sends it to an embedded R engine running on the database server to apply our R function.  Then the Oracle database combines all the individual results from the calls to the R function. This is significant because now the embedded R engine only needs to deal with the data for a single account at a time.  Regardless of whether we have 20 accounts or 1 million accounts or more, the R engine that performs the calculation does not care.  Given that normal R has a finite amount of memory to hold data, the ore.groupApply approach overcomes the R memory scalability problem since we only need to fit the data from a single account in R memory (not all of the data for all of the accounts). Additionally, the IRR_DATA does not need to be sent from the database to my desktop R program.  Even though I am invoking ore.groupApply from my desktop R program, because the actual SimpleMWRR calculation is run by the embedded R engine on the database server, the IRR_DATA does not need to leave the database server- this is both a performance benefit because network transmission of large amounts of data take time and a security benefit because it is harder to protect private data once you start shipping around your intranet. Another benefit, which we will discuss in a few paragraphs, is the ability to leverage Oracle database parallelism to run these calculations for dozens of accounts at once. From Walking to Running ore.groupApply is rather nice, but it still has the drawback that I run this from a desktop R instance.  This is not ideal for integrating into typical operational processes like nightly data warehouse refreshes or monthly statement generation.  But, this is not an issue for ORE.  Oracle R Enterprise lets us run this from the database using regular SQL, which is easily integrated into standard operations.  That is extremely exciting and the way we actually did these calculations in the customer proof. As part of Oracle R Enterprise, it provides a SQL equivalent to ore.groupApply which it refers to as “rqGroupEval”.  To use rqGroupEval via SQL, there is a bit of simple setup needed.  Basically, the Oracle Database needs to know the structure of the input table and the grouping column, which we are able to define using the database’s pipeline table function mechanisms. Here is the setup script: At this point, our initial setup of rqGroupEval is done for the IRR_DATA table.  The next step is to define our R function to the database.  We do that via a call to ORE’s rqScriptCreate. Now we can test it.  The SQL you use to run rqGroupEval uses the Oracle database pipeline table function syntax.  The first argument to irr_dataGroupEval is a cursor defining our input.  You can add additional where clauses and subqueries to this cursor as appropriate.  The second argument is any additional inputs to the R function.  The third argument is the text of a dummy select statement.  The dummy select statement is used by the database to identify the columns and datatypes to expect the R function to return.  The fourth argument is the column of the input table to split/group by.  The final argument is the name of the R function as you defined it when you called rqScriptCreate(). The Real-World Results In our real customer proof-of-concept, we had more sophisticated calculation requirements than shown in this simplified blog example.  For instance, we had to perform the rate of return calculations for 5 separate time periods, so the R code was enhanced to do so.  In addition, some accounts needed a time-weighted rate of return to be calculated, so we extended our approach and added an R function to do that.  And finally, there were also a few more real-world data irregularities that we needed to account for, so we added logic to our R functions to deal with those exceptions.  For the full-scale customer test, we loaded the customer data onto a Half-Rack Exadata X2-2 Database Machine.  As our half-rack had 48 physical cores (and 96 threads if you consider hyperthreading), we wanted to take advantage of that CPU horsepower to speed up our calculations.  To do so with ORE, it is as simple as leveraging the Oracle Database Parallel Query features.  Let’s look at the SQL used in the customer proof: Notice that we use a parallel hint on the cursor that is the input to our rqGroupEval function.  That is all we need to do to enable Oracle to use parallel R engines. Here are a few screenshots of what this SQL looked like in the Real-Time SQL Monitor when we ran this during the proof of concept (hint: you might need to right-click on these images to be able to view the images full-screen to see the entire image): From the above, you can notice a few things (numbers 1 thru 5 below correspond with highlighted numbers on the images above.  You may need to right click on the above images and view the images full-screen to see the entire image): The SQL completed in 110 seconds (1.8minutes) We calculated rate of returns for 5 time periods for each of 911k accounts (the number of actual rows returned by the IRRSTAGEGROUPEVAL operation) We accessed 103m rows of detailed cash flow/market value data (the number of actual rows returned by the IRR_STAGE2 operation) We ran with 72 degrees of parallelism spread across 4 database servers Most of our 110seconds was spent in the “External Procedure call” event On average, we performed 8,200 executions of our R function per second (110s/911k accounts) On average, each execution was passed 110 rows of data (103m detail rows/911k accounts) On average, we did 41,000 single time period rate of return calculations per second (each of the 8,200 executions of our R function did rate of return calculations for 5 time periods) On average, we processed over 900,000 rows of database data in R per second (103m detail rows/110s) R + Oracle R Enterprise: Best of R + Best of Oracle Database This blog post series started by describing a real customer problem: how to perform a lot of calculations on a lot of data in a short period of time.  While standard R proved to be a very good fit for writing the necessary calculations, the challenge of working with a lot of data in a short period of time remained. This blog post series showed how Oracle R Enterprise enables R to be used in conjunction with the Oracle Database to overcome the data volume and performance issues (as well as simplifying the operations and security issues).  It also showed that we could calculate 5 time periods of rate of returns for almost a million individual accounts in less than 2 minutes. In a future post, we will take the same R function and show how Oracle R Connector for Hadoop can be used in the Hadoop world.  In that next post, instead of having our data in an Oracle database, our data will live in Hadoop and we will how to use the Oracle R Connector for Hadoop and other Oracle Big Data Connectors to move data between Hadoop, R, and the Oracle Database easily.

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  • Big GRC: Turning Data into Actionable GRC Intelligence

    - by Jenna Danko
    While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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  • Toon shader with Texture. Can this be optimized?

    - by Alex
    I am quite new to OpenGL, I have managed after long trial and error to integrate Nehe's Cel-Shading rendering with my Model loaders, and have them drawn using the Toon shade and outline AND their original texture at the same time. The result is actually a very nice Cel Shading effect of the model texture, but it is havling the speed of the program, it's quite very slow even with just 3 models on screen... Since the result was kind of hacked together, I am thinking that maybe I am performing some extra steps or extra rendering tasks that maybe are not needed, and are slowing down the game? Something unnecessary that maybe you guys could spot? Both MD2 and 3DS loader have an InitToon() function called upon creation to load the shader initToon(){ int i; // Looping Variable ( NEW ) char Line[255]; // Storage For 255 Characters ( NEW ) float shaderData[32][3]; // Storate For The 96 Shader Values ( NEW ) FILE *In = fopen ("Shader.txt", "r"); // Open The Shader File ( NEW ) if (In) // Check To See If The File Opened ( NEW ) { for (i = 0; i < 32; i++) // Loop Though The 32 Greyscale Values ( NEW ) { if (feof (In)) // Check For The End Of The File ( NEW ) break; fgets (Line, 255, In); // Get The Current Line ( NEW ) shaderData[i][0] = shaderData[i][1] = shaderData[i][2] = float(atof (Line)); // Copy Over The Value ( NEW ) } fclose (In); // Close The File ( NEW ) } else return false; // It Went Horribly Horribly Wrong ( NEW ) glGenTextures (1, &shaderTexture[0]); // Get A Free Texture ID ( NEW ) glBindTexture (GL_TEXTURE_1D, shaderTexture[0]); // Bind This Texture. From Now On It Will Be 1D ( NEW ) // For Crying Out Loud Don't Let OpenGL Use Bi/Trilinear Filtering! ( NEW ) glTexParameteri (GL_TEXTURE_1D, GL_TEXTURE_MAG_FILTER, GL_NEAREST); glTexParameteri (GL_TEXTURE_1D, GL_TEXTURE_MIN_FILTER, GL_NEAREST); glTexImage1D (GL_TEXTURE_1D, 0, GL_RGB, 32, 0, GL_RGB , GL_FLOAT, shaderData); // Upload ( NEW ) } This is the drawing for the animated MD2 model: void MD2Model::drawToon() { float outlineWidth = 3.0f; // Width Of The Lines ( NEW ) float outlineColor[3] = { 0.0f, 0.0f, 0.0f }; // Color Of The Lines ( NEW ) // ORIGINAL PART OF THE FUNCTION //Figure out the two frames between which we are interpolating int frameIndex1 = (int)(time * (endFrame - startFrame + 1)) + startFrame; if (frameIndex1 > endFrame) { frameIndex1 = startFrame; } int frameIndex2; if (frameIndex1 < endFrame) { frameIndex2 = frameIndex1 + 1; } else { frameIndex2 = startFrame; } MD2Frame* frame1 = frames + frameIndex1; MD2Frame* frame2 = frames + frameIndex2; //Figure out the fraction that we are between the two frames float frac = (time - (float)(frameIndex1 - startFrame) / (float)(endFrame - startFrame + 1)) * (endFrame - startFrame + 1); // I ADDED THESE FROM NEHE'S TUTORIAL FOR FIRST PASS (TOON SHADE) glHint (GL_LINE_SMOOTH_HINT, GL_NICEST); // Use The Good Calculations ( NEW ) glEnable (GL_LINE_SMOOTH); // Cel-Shading Code // glEnable (GL_TEXTURE_1D); // Enable 1D Texturing ( NEW ) glBindTexture (GL_TEXTURE_1D, shaderTexture[0]); // Bind Our Texture ( NEW ) glColor3f (1.0f, 1.0f, 1.0f); // Set The Color Of The Model ( NEW ) // ORIGINAL DRAWING CODE //Draw the model as an interpolation between the two frames glBegin(GL_TRIANGLES); for(int i = 0; i < numTriangles; i++) { MD2Triangle* triangle = triangles + i; for(int j = 0; j < 3; j++) { MD2Vertex* v1 = frame1->vertices + triangle->vertices[j]; MD2Vertex* v2 = frame2->vertices + triangle->vertices[j]; Vec3f pos = v1->pos * (1 - frac) + v2->pos * frac; Vec3f normal = v1->normal * (1 - frac) + v2->normal * frac; if (normal[0] == 0 && normal[1] == 0 && normal[2] == 0) { normal = Vec3f(0, 0, 1); } glNormal3f(normal[0], normal[1], normal[2]); MD2TexCoord* texCoord = texCoords + triangle->texCoords[j]; glTexCoord2f(texCoord->texCoordX, texCoord->texCoordY); glVertex3f(pos[0], pos[1], pos[2]); } } glEnd(); // ADDED THESE FROM NEHE'S FOR SECOND PASS (OUTLINE) glDisable (GL_TEXTURE_1D); // Disable 1D Textures ( NEW ) glEnable (GL_BLEND); // Enable Blending ( NEW ) glBlendFunc(GL_SRC_ALPHA,GL_ONE_MINUS_SRC_ALPHA); // Set The Blend Mode ( NEW ) glPolygonMode (GL_BACK, GL_LINE); // Draw Backfacing Polygons As Wireframes ( NEW ) glLineWidth (outlineWidth); // Set The Line Width ( NEW ) glCullFace (GL_FRONT); // Don't Draw Any Front-Facing Polygons ( NEW ) glDepthFunc (GL_LEQUAL); // Change The Depth Mode ( NEW ) glColor3fv (&outlineColor[0]); // Set The Outline Color ( NEW ) // HERE I AM PARSING THE VERTICES AGAIN (NOT IN THE ORIGINAL FUNCTION) FOR THE OUTLINE AS PER NEHE'S TUT glBegin (GL_TRIANGLES); // Tell OpenGL What We Want To Draw for(int i = 0; i < numTriangles; i++) { MD2Triangle* triangle = triangles + i; for(int j = 0; j < 3; j++) { MD2Vertex* v1 = frame1->vertices + triangle->vertices[j]; MD2Vertex* v2 = frame2->vertices + triangle->vertices[j]; Vec3f pos = v1->pos * (1 - frac) + v2->pos * frac; Vec3f normal = v1->normal * (1 - frac) + v2->normal * frac; if (normal[0] == 0 && normal[1] == 0 && normal[2] == 0) { normal = Vec3f(0, 0, 1); } glNormal3f(normal[0], normal[1], normal[2]); MD2TexCoord* texCoord = texCoords + triangle->texCoords[j]; glTexCoord2f(texCoord->texCoordX, texCoord->texCoordY); glVertex3f(pos[0], pos[1], pos[2]); } } glEnd (); // Tell OpenGL We've Finished glDepthFunc (GL_LESS); // Reset The Depth-Testing Mode ( NEW ) glCullFace (GL_BACK); // Reset The Face To Be Culled ( NEW ) glPolygonMode (GL_BACK, GL_FILL); // Reset Back-Facing Polygon Drawing Mode ( NEW ) glDisable (GL_BLEND); } Whereas this is the drawToon function in the 3DS loader void Model_3DS::drawToon() { float outlineWidth = 3.0f; // Width Of The Lines ( NEW ) float outlineColor[3] = { 0.0f, 0.0f, 0.0f }; // Color Of The Lines ( NEW ) //ORIGINAL CODE if (visible) { glPushMatrix(); // Move the model glTranslatef(pos.x, pos.y, pos.z); // Rotate the model glRotatef(rot.x, 1.0f, 0.0f, 0.0f); glRotatef(rot.y, 0.0f, 1.0f, 0.0f); glRotatef(rot.z, 0.0f, 0.0f, 1.0f); glScalef(scale, scale, scale); // Loop through the objects for (int i = 0; i < numObjects; i++) { // Enable texture coordiantes, normals, and vertices arrays if (Objects[i].textured) glEnableClientState(GL_TEXTURE_COORD_ARRAY); if (lit) glEnableClientState(GL_NORMAL_ARRAY); glEnableClientState(GL_VERTEX_ARRAY); // Point them to the objects arrays if (Objects[i].textured) glTexCoordPointer(2, GL_FLOAT, 0, Objects[i].TexCoords); if (lit) glNormalPointer(GL_FLOAT, 0, Objects[i].Normals); glVertexPointer(3, GL_FLOAT, 0, Objects[i].Vertexes); // Loop through the faces as sorted by material and draw them for (int j = 0; j < Objects[i].numMatFaces; j ++) { // Use the material's texture Materials[Objects[i].MatFaces[j].MatIndex].tex.Use(); // AFTER THE TEXTURE IS APPLIED I INSERT THE TOON FUNCTIONS HERE (FIRST PASS) glHint (GL_LINE_SMOOTH_HINT, GL_NICEST); // Use The Good Calculations ( NEW ) glEnable (GL_LINE_SMOOTH); // Cel-Shading Code // glEnable (GL_TEXTURE_1D); // Enable 1D Texturing ( NEW ) glBindTexture (GL_TEXTURE_1D, shaderTexture[0]); // Bind Our Texture ( NEW ) glColor3f (1.0f, 1.0f, 1.0f); // Set The Color Of The Model ( NEW ) glPushMatrix(); // Move the model glTranslatef(Objects[i].pos.x, Objects[i].pos.y, Objects[i].pos.z); // Rotate the model glRotatef(Objects[i].rot.z, 0.0f, 0.0f, 1.0f); glRotatef(Objects[i].rot.y, 0.0f, 1.0f, 0.0f); glRotatef(Objects[i].rot.x, 1.0f, 0.0f, 0.0f); // Draw the faces using an index to the vertex array glDrawElements(GL_TRIANGLES, Objects[i].MatFaces[j].numSubFaces, GL_UNSIGNED_SHORT, Objects[i].MatFaces[j].subFaces); glPopMatrix(); } glDisable (GL_TEXTURE_1D); // Disable 1D Textures ( NEW ) // THIS IS AN ADDED SECOND PASS AT THE VERTICES FOR THE OUTLINE glEnable (GL_BLEND); // Enable Blending ( NEW ) glBlendFunc(GL_SRC_ALPHA,GL_ONE_MINUS_SRC_ALPHA); // Set The Blend Mode ( NEW ) glPolygonMode (GL_BACK, GL_LINE); // Draw Backfacing Polygons As Wireframes ( NEW ) glLineWidth (outlineWidth); // Set The Line Width ( NEW ) glCullFace (GL_FRONT); // Don't Draw Any Front-Facing Polygons ( NEW ) glDepthFunc (GL_LEQUAL); // Change The Depth Mode ( NEW ) glColor3fv (&outlineColor[0]); // Set The Outline Color ( NEW ) for (int j = 0; j < Objects[i].numMatFaces; j ++) { glPushMatrix(); // Move the model glTranslatef(Objects[i].pos.x, Objects[i].pos.y, Objects[i].pos.z); // Rotate the model glRotatef(Objects[i].rot.z, 0.0f, 0.0f, 1.0f); glRotatef(Objects[i].rot.y, 0.0f, 1.0f, 0.0f); glRotatef(Objects[i].rot.x, 1.0f, 0.0f, 0.0f); // Draw the faces using an index to the vertex array glDrawElements(GL_TRIANGLES, Objects[i].MatFaces[j].numSubFaces, GL_UNSIGNED_SHORT, Objects[i].MatFaces[j].subFaces); glPopMatrix(); } glDepthFunc (GL_LESS); // Reset The Depth-Testing Mode ( NEW ) glCullFace (GL_BACK); // Reset The Face To Be Culled ( NEW ) glPolygonMode (GL_BACK, GL_FILL); // Reset Back-Facing Polygon Drawing Mode ( NEW ) glDisable (GL_BLEND); glPopMatrix(); } Finally this is the tex.Use() function that loads a BMP texture and somehow gets blended perfectly with the Toon shading void GLTexture::Use() { glEnable(GL_TEXTURE_2D); // Enable texture mapping glBindTexture(GL_TEXTURE_2D, texture[0]); // Bind the texture as the current one }

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  • UIScrollView zoomToRect not zooming to given rect (created from UITouch CGPoint)

    - by pmhart
    My application has a UIScrollView with one subview. The subview is an extended UIView which prints a PDF page to itself using layers in the drawLayer event. Zooming using the built in pinching works great. setZoomScale also works as expected. I have been struggling with the zoomToRect function. I found an example online which makes a CGRect zoomRect variable from a given CGPoint. In the touchesEnded function, if there was a double tap and they are all the way zoomed out, I want to zoom in to that PDFUIView I created as though they were pinching out with the center of the pinch where they double tapped. So assume that I pass the UITouch variable to my function which utilizes zoomToRect if they double tap. I started with the following function I found on apples site: http://developer.apple.com/iphone/library/documentation/WindowsViews/Conceptual/UIScrollView_pg/ZoomZoom/ZoomZoom.html The following is a modified version for my UIScrollView extended class: - (void)zoomToCenter:(float)scale withCenter:(CGPoint)center { CGRect zoomRect; zoomRect.size.height = self.frame.size.height / scale; zoomRect.size.width = self.frame.size.width / scale; zoomRect.origin.x = center.x - (zoomRect.size.width / 2.0); zoomRect.origin.y = center.y - (zoomRect.size.height / 2.0); //return zoomRect; [self zoomToRect:zoomRect animated:YES]; } When I do this, the UIScrollView seems to zoom using the bottom right edge of the zoomRect above and not the center. If I make UIView like this UIView *v = [[UIView alloc] initWithFrame:zoomRect]; [v setBackgroundColor:[UIView redColor]]; [self addSubview:v]; The red box shows up with the touch point dead in the center. Please note: I am writing this from my PC, I recall messing around with the divided by two part on my Mac, so just assume that this draws a rect with the touch point in the center. If the UIView drew off center but zoomed to the right spot it would be all good. However, what happens is when it preforms the zoomToRect it seems to use the bottom right off the zoomRect at the top left of the zoomed in results. Also, I noticed that depending on where I click on the UIScrollView, it anchors to diffrent spots. It almost seems like there is a cross down the middle and it's reflecting the points somehow as though anywhere left of the middle is a negative reflection and anywhere right of the middle is a positive reflection? This seems to complicated, shouldn't it just zoom to the rect that was drawn as the UIView was able to draw? I used a lot of research to figure out how to create a PDF that scales in high quality, so I am assuming that using the CALayer may be throwing off the coordinate system? But to the UIScrollView it should just treat it as a view with 768x985 dimensions. This is sort of advanced, please assume the code for creating the zoomRect is all good. There is something deeper with the CALayer in the UIView which is in the UIScrollView....

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  • Using JavaScript, how do I write the same text to multiple HTML elements, or how do I write text to all HTML elements of the same class?

    - by myfavoritenoisemaker
    I am writing this program to take a root music note and populate tables with various scales from that root note. So, many of the tables cells will have the exact same value in them. I realize I can call my "useScale" function for every single that I need to write text to but since there will be repeats, it seemed like there should be a way to run my function once and apply the results to multiple but it did not work to use the document.getElementsByClassName("").innerHTML, I had been using "ById" which worked fine but each ID must be unique so, I can't write to multiple elements. Here's my code, I'd love some suggestions. many thanks Root Note <input type="text" name="defineRootNote" id="rootNoteCapture" size="2"/> <button onclick="findScale()">Submit</button> <table id="majorTriad"> <th>Major Triad</th> <tr><td>1st</td><td class="root"> </td></tr> <tr><td>3rd</td><td class="3rd"> </td></tr> <tr><td>5th</td><td class="5th"> </td></tr> </table> <table id="minorTriad"> <th>Minor Triad</th> <tr><td>1st</td><td class="root"> </td></tr> <tr><td>3 Flat</td><td class="3Flat"> </td></tr> <tr><td>5th</td><td class="5th"> </td></tr> </table> <script type="text/javascript"> function findScale(rootNote){ var rootNote = document.getElementById("rootNoteCapture").value; rootNote = rootNote.toUpperCase(); var scaleCheck = ["A", "A#", "AB", "B", "BB", "C", "C#", "D", "D#", "DB", "E", "EB", "F", "F#", "G", "G#", "GB"]; if (scaleCheck.indexOf(rootNote) == -1) { document.getElementById("root").innerHTML = "Invalid Entry"; } else { switch(rootNote){ case "AB": rootNote = "G#"; break; case "BB": rootNote = "A#"; break; case "DB": rootNote = "C#"; break; case "EB": rootNote = "D#"; break; case "GB": rootNote = "F#"; break; rootNote = rootNote; } document.getElementsByClassName("root").innerHTML = rootNote; document.getElementsByClassName("3rd").innerHTML = useScale(rootNote, 4); document.getElementsByClassName("5th").innerHTML = useScale(rootNote, 7); document.getElementsByClassName("3Flat").innerHTML = useScale(rootNote, 3); } } function useScale(startPoint, offset){ var scale = ["A", "A#", "B", "C", "C#", "D", "D#", "E", "F", "F#", "G", "G#"]; var returnNote = null; var scalePoint = scale.indexOf(startPoint); for (var i = 0; i < offset; ){ i = i + 1; //console.log(i); //console.log(scalePoint); scalePoint ++; if (scalePoint > 11) {scalePoint = 0;} } returnNote = scale[scalePoint]; return returnNote; } </script>

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  • Oracle Announces Oracle Exadata X3 Database In-Memory Machine

    - by jgelhaus
    Fourth Generation Exadata X3 Systems are Ideal for High-End OLTP, Large Data Warehouses, and Database Clouds; Eighth-Rack Configuration Offers New Low-Cost Entry Point ORACLE OPENWORLD, SAN FRANCISCO – October 1, 2012 News Facts During his opening keynote address at Oracle OpenWorld, Oracle CEO, Larry Ellison announced the Oracle Exadata X3 Database In-Memory Machine - the latest generation of its Oracle Exadata Database Machines. The Oracle Exadata X3 Database In-Memory Machine is a key component of the Oracle Cloud. Oracle Exadata X3-2 Database In-Memory Machine and Oracle Exadata X3-8 Database In-Memory Machine can store up to hundreds of Terabytes of compressed user data in Flash and RAM memory, virtually eliminating the performance overhead of reads and writes to slow disk drives, making Exadata X3 systems the ideal database platforms for the varied and unpredictable workloads of cloud computing. In order to realize the highest performance at the lowest cost, the Oracle Exadata X3 Database In-Memory Machine implements a mass memory hierarchy that automatically moves all active data into Flash and RAM memory, while keeping less active data on low-cost disks. With a new Eighth-Rack configuration, the Oracle Exadata X3-2 Database In-Memory Machine delivers a cost-effective entry point for smaller workloads, testing, development and disaster recovery systems, and is a fully redundant system that can be used with mission critical applications. Next-Generation Technologies Deliver Dramatic Performance Improvements Oracle Exadata X3 Database In-Memory Machines use a combination of scale-out servers and storage, InfiniBand networking, smart storage, PCI Flash, smart memory caching, and Hybrid Columnar Compression to deliver extreme performance and availability for all Oracle Database Workloads. Oracle Exadata X3 Database In-Memory Machine systems leverage next-generation technologies to deliver significant performance enhancements, including: Four times the Flash memory capacity of the previous generation; with up to 40 percent faster response times and 100 GB/second data scan rates. Combined with Exadata’s unique Hybrid Columnar Compression capabilities, hundreds of Terabytes of user data can now be managed entirely within Flash; 20 times more capacity for database writes through updated Exadata Smart Flash Cache software. The new Exadata Smart Flash Cache software also runs on previous generation Exadata systems, increasing their capacity for writes tenfold; 33 percent more database CPU cores in the Oracle Exadata X3-2 Database In-Memory Machine, using the latest 8-core Intel® Xeon E5-2600 series of processors; Expanded 10Gb Ethernet connectivity to the data center in the Oracle Exadata X3-2 provides 40 10Gb network ports per rack for connecting users and moving data; Up to 30 percent reduction in power and cooling. Configured for Your Business, Available Today Oracle Exadata X3-2 Database In-Memory Machine systems are available in a Full-Rack, Half-Rack, Quarter-Rack, and the new low-cost Eighth-Rack configuration to satisfy the widest range of applications. Oracle Exadata X3-8 Database In-Memory Machine systems are available in a Full-Rack configuration, and both X3 systems enable multi-rack configurations for virtually unlimited scalability. Oracle Exadata X3-2 and X3-8 Database In-Memory Machines are fully compatible with prior Exadata generations and existing systems can also be upgraded with Oracle Exadata X3-2 servers. Oracle Exadata X3 Database In-Memory Machine systems can be used immediately with any application certified with Oracle Database 11g R2 and Oracle Real Application Clusters, including SAP, Oracle Fusion Applications, Oracle’s PeopleSoft, Oracle’s Siebel CRM, the Oracle E-Business Suite, and thousands of other applications. Supporting Quotes “Forward-looking enterprises are moving towards Cloud Computing architectures,” said Andrew Mendelsohn, senior vice president, Oracle Database Server Technologies. “Oracle Exadata’s unique ability to run any database application on a fully scale-out architecture using a combination of massive memory for extreme performance and low-cost disk for high capacity delivers the ideal solution for Cloud-based database deployments today.” Supporting Resources Oracle Press Release Oracle Exadata Database Machine Oracle Exadata X3-2 Database In-Memory Machine Oracle Exadata X3-8 Database In-Memory Machine Oracle Database 11g Follow Oracle Database via Blog, Facebook and Twitter Oracle OpenWorld 2012 Oracle OpenWorld 2012 Keynotes Like Oracle OpenWorld on Facebook Follow Oracle OpenWorld on Twitter Oracle OpenWorld Blog Oracle OpenWorld on LinkedIn Mark Hurd's keynote with Andy Mendelsohn and Juan Loaiza - - watch for the replay to be available soon at http://www.youtube.com/user/Oracle or http://www.oracle.com/openworld/live/on-demand/index.html

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  • Windows Azure Use Case: Web Applications

    - by BuckWoody
    This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx  Description: Many applications have a requirement to be located outside of the organization’s internal infrastructure control. For instance, the company website for a brick-and-mortar retail company may want to post not only static but interactive content to be available to their external customers, and not want the customers to have access inside the organization’s firewall. There are also cases of pure web applications used for a great many of the internal functions of the business. This allows for remote workers, shared customer/employee workloads and data and other advantages. Some firms choose to host these web servers internally, others choose to contract out the infrastructure to an “ASP” (Application Service Provider) or an Infrastructure as a Service (IaaS) company. In any case, the design of these applications often resembles the following: In this design, a server (or perhaps more than one) hosts the presentation function (http or https) access to the application, and this same system may hold the computational aspects of the program. Authorization and Access is controlled programmatically, or is more open if this is a customer-facing application. Storage is either placed on the same or other servers, hosted within an RDBMS or NoSQL database, or a combination of the options, all coded into the application. High-Availability within this scenario is often the responsibility of the architects of the application, and by purchasing more hosting resources which must be built, licensed and configured, and manually added as demand requires, although some IaaS providers have a partially automatic method to add nodes for scale-out, if the architecture of the application supports it. Disaster Recovery is the responsibility of the system architect as well. Implementation: In a Windows Azure Platform as a Service (PaaS) environment, many of these architectural considerations are designed into the system. The Azure “Fabric” (not to be confused with the Azure implementation of Application Fabric - more on that in a moment) is designed to provide scalability. Compute resources can be added and removed programmatically based on any number of factors. Balancers at the request-level of the Fabric automatically route http and https requests. The fabric also provides High-Availability for storage and other components. Disaster recovery is a shared responsibility between the facilities (which have the ability to restore in case of catastrophic failure) and your code, which should build in recovery. In a Windows Azure-based web application, you have the ability to separate out the various functions and components. Presentation can be coded for multiple platforms like smart phones, tablets and PC’s, while the computation can be a single entity shared between them. This makes the applications more resilient and more object-oriented, and lends itself to a SOA or Distributed Computing architecture. It is true that you could code up a similar set of functionality in a traditional web-farm, but the difference here is that the components are built into the very design of the architecture. The API’s and DLL’s you call in a Windows Azure code base contains components as first-class citizens. For instance, if you need storage, it is simply called within the application as an object.  Computation has multiple options and the ability to scale linearly. You also gain another component that you would either have to write or bolt-in to a typical web-farm: the Application Fabric. This Windows Azure component provides communication between applications or even to on-premise systems. It provides authorization in either person-based or claims-based perspectives. SQL Azure provides relational storage as another option, and can also be used or accessed from on-premise systems. It should be noted that you can use all or some of these components individually. Resources: Design Strategies for Scalable Active Server Applications - http://msdn.microsoft.com/en-us/library/ms972349.aspx  Physical Tiers and Deployment  - http://msdn.microsoft.com/en-us/library/ee658120.aspx

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  • OWB 11gR2 - Find and Search Metadata in Designer

    - by David Allan
    Here are some tools and techniques for finding objects, specifically in the design repository. There are ways of navigating and collating objects that are useful for day to day development and build-time usage - this includes features out of the box and utilities constructed on top. There are a variety of techniques to navigate and find objects in the repository, the first 3 are out of the box, the 4th is an expert utility. Navigating by the tree, grouping by project and module - ok if you are aware of the exact module/folder that objects reside in. The structure panel is a useful way of finding parts of an object, especially when large rather than using the canvas. In large scale projects it helps to have accelerators (either find or collections below). Advanced find to search by name - 11gR2 included a find capability specifically for large scale projects. There were improvements in both the tree search and the object editors (including highlighting in mapping for example). So you can now do regular expression based search and quickly navigate to objects within a repository. Collections - logically organize your objects into virtual folders by shortcutting the actual objects. This is useful for a range of things since all the OWB services operate on collections too (export/import, validation, deployment). See the post here for new collection functionality in 11gR2. Reports for searching by type, updated on, updated by etc. Useful for activities such as periodic incremental actions (deploy all mappings changed in the past week). The report style view is useful since I can quickly see who changed what and when. You can see all the audit details for objects within each objects property inspector, but its useful to just get all objects changed today or example, all objects changed since my last build etc. This utility combines both UI extensions via experts and the public views on the repository. In the figure to the right you see the contextual option 'Object Search' which invokes the utility, you can see I have quite a number of modules within my project. Figure out all the potential objects which have been changed is not simple. The utility is an expert which provides this kind of search capability. The utility provides a report of the objects in the design repository which satisfy some filter criteria. The type of criteria includes; objects updated in the last n days optionally filter the objects updated by user filter the user by project and by type (table/mappings etc.) The search dialog appears with these options, you can multi-select the object types, so for example you can select TABLE and MAPPING. Its also possible to search across projects if need be. If you have multiple users using the repository you can define the OWB user name in the 'Updated by' property to restrict the report to just that user also. Finally there is a search name that will be used for some of the options such as building a collection - this name is used for the collection to be built. In the example I have done, I've just searched my project for all process flows and mappings that users have updated in the last 7 days. The results of the query are returned in a table containing the object names, types, full path and audit details. The columns are sort-able, you can sort the results by name, type, path etc. One of the cool things here, is that you can then perform operations on these objects - such as edit them, export single selection or entire results to MDL, create a collection from the results (now you have a saved set of references in the repository, you could do deploy/export etc.), create a deployment script from the results...or even add in your own ideas! You see from this that you can do bulk operations on sets of objects based on search results. So for example selecting the 'Build Collection' option creates a collection with all of the objects from my search, you can subsequently deploy/generate/maintain this collection of objects. Under the hood of the expert if just basic OMB commands from the product and the use of the public views on the design repository. You can see how easy it is to build up macro-like capabilities that will help you do day-to-day as well as build like tasks on sets of objects.

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  • Is there a low carbon future for the retail industry?

    - by user801960
    Recently Oracle published a report in conjunction with The Future Laboratory and a global panel of experts to highlight the issue of energy use in modern industry and the serious need to reduce carbon emissions radically by 2050.  Emissions must be cut by 80-95% below the levels in 1990 – but what can the retail industry do to keep up with this? There are three key aspects to the retail industry where carbon emissions can be cut:  manufacturing, transport and IT.  Manufacturing Naturally, manufacturing is going to be a big area where businesses across all industries will be forced to make considerable savings in carbon emissions as well as other forms of pollution.  Many retailers of all sizes will use third party factories and will have little control over specific environmental impacts from the factory, but retailers can reduce environmental impact at the factories by managing orders more efficiently – better planning for stock requirements means economies of scale both in terms of finance and the environment. The John Lewis Partnership has made detailed commitments to reducing manufacturing and packaging waste on both its own-brand products and products it sources from third party suppliers. It aims to divert 95 percent of its operational waste from landfill by 2013, which is a huge logistics challenge.  The John Lewis Partnership’s website provides a large amount of information on its responsibilities towards the environment. Transport Similarly to manufacturing, tightening up on logistical planning for stock distribution will make savings on carbon emissions from haulage.  More accurate supply and demand analysis will mean less stock re-allocation after initial distribution, and better warehouse management will mean more efficient stock distribution.  UK grocery retailer Morrisons has introduced double-decked trailers to its haulage fleet and adjusted distribution logistics accordingly to reduce the number of kilometers travelled by the fleet.  Morrisons measures route planning efficiency in terms of cases moved per kilometre and has, over the last two years, increased the number of cases per kilometre by 12.7%.  See Morrisons Corporate Responsibility report for more information. IT IT infrastructure is often initially overlooked by businesses when considering environmental efficiency.  Datacentres and web servers often need to run 24/7 to handle both consumer orders and internal logistics, and this both requires a lot of energy and puts out a lot of heat.  Many businesses are lowering environmental impact by reducing IT system fragmentation in their offices, while an increasing number of businesses are outsourcing their datacenters to cloud-based services.  Using centralised datacenters reduces the power usage at smaller offices, while using cloud based services means the datacenters can be based in a more environmentally friendly location.  For example, Facebook is opening a massive datacentre in Sweden – close to the Arctic Circle – to reduce the need for artificial cooling methods.  In addition, moving to a cloud-based solution makes IT services more easily scaleable, reducing redundant IT systems that would still use energy.  In store, the UK’s Carbon Trust reports that on average, lighting accounts for 25% of a retailer’s electricity costs, and for grocery retailers, up to 50% of their electricity bill comes from refrigeration units.  On a smaller scale, retailers can invest in greener technologies in store and in their offices.  The report concludes that widely shared objectives of energy security, reduced emissions and continued economic growth are dependent on the development of a smart grid capable of delivering energy efficiency and demand response, as well as integrating renewable and variable sources of energy. The report is available to download from http://emeapressoffice.oracle.com/imagelibrary/detail.aspx?MediaDetailsID=1766I’d be interested to hear your thoughts on the report.   

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